A brief history of voxel-based grey matter analysis in Alzheimer's disease

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis.

ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.

Traumatic brain injury fast-forwards Alzheimer's pathology: evidence from amyloid positron emission tomorgraphy imaging

PURPOSE

Traumatic brain injury (TBI) has been proposed as a risk factor for Alzheimer's disease (AD), although the mechanisms underlying the putative association are poorly understood. We investigated elderly individuals with a remote history of TBI, aiming to understand how this may have influenced amyloidosis, neurodegeneration, and clinical expression along the AD continuum.

METHODS

Total of 241 individual datasets including amyloid beta (Aβ) positron emission tomography ([¹⁸F]-AV45), structural MRI, and neuropsychological measures, were obtained from the Alzheimer's Disease Neuroimaging Initiative. The data were stratified into groups with (TBI +) or without (TBI -) history of head injury, and by clinical dementia rating (CDR) scores, into subgroups with normal cognition (CDR = 0) and those with symptomatic cognitive decline (CDR ≥ 0.5). We contrasted the TBI + and TBI - subgroups with respect to the onset age and extent of cognitive decline, cortical thickness changes, and Aβ standard uptake value (SUVr).

RESULTS

Compared to the TBI -/CDR ≥ 0.5 subgroup, the TBI + /CDR ≥ 0.5 subgroup showed a 3-4 year earlier age of cognitive impairment onset (ACIO, p = 0.005). Among those participants on the AD continuum (Aβ + , as defined by a cortical SUVr ≥ 1.23), irrespective of current CDR, a TBI + history was associated with greater Aβ deposition and more pronounced cortical thinning. When matched for severity of cognitive status, the TBI + /CDR ≥ 0.5 group showed greater Aβ burden, but earlier ACIO as compared to the TBI -/CDR ≥ 0.5, suggesting a more indolent clinical AD progression in those with TBI history.

CONCLUSION

Remote TBI history may alter the AD onset trajectory, with approximately 4 years earlier ACIO, greater amyloid deposition, and cortical thinning.

Pain processing in older adults with dementia-related cognitive impairment is associated with frontal neurodegeneration

Experimental pain research has shown that pain processing seems to be heightened in dementia. It is unclear which neuropathological changes underlie these alterations. This study examined whether differences in pressure pain sensitivity and endogenous pain inhibition (conditioned pain modulation (CPM)) between individuals with a dementia-related cognitive impairment (N=23) and healthy controls (N=35) are linked to dementia-related neurodegeneration. Pain was assessed via self-report ratings and by analyzing the facial expression of pain using the Facial Action Coding System. We found that cognitively impaired individuals show decreased CPM inhibition as assessed by facial responses compared to healthy controls, which was mediated by decreased gray matter volume in the medial orbitofrontal and anterior cingulate cortex in the patient group. This study confirms previous findings of intensified pain processing in dementia when pain is assessed using non-verbal responses. Our findings suggest that a loss of pain inhibitory functioning caused by structural changes in prefrontal areas might be one of the underlying mechanisms responsible for amplified pain responses in individuals with a dementia-related cognitive impairment.

Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis

Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.

Finding specificity in structural brain alterations through Bayesian reverse inference

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

Hubs of long-distance co-alteration characterize brain pathology

It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.

The atrophy pattern in Alzheimer-related PPA is more widespread than that of the frontotemporal lobar degeneration associated variants

Objective

The three recognized variants of primary progressive aphasia (PPA) are associated with different loci of degeneration—left posterior perisylvian in logopenic variant (lvPPA), left frontal operculum in non-fluent variant (nfvPPA), and left rostroventral-temporal in semantic variant (svPPA). Meanwhile, it has become apparent that patients with lvPPA, in which Alzheimer pathology is the norm, frequently have more extensive language deficits—namely semantic and grammatical problems—than is captured in the strict diagnostic recommendations for this variant. We hypothesized that this may be because the degeneration in AD-related PPA typically extends beyond the left posterior perisylvian region.

Methods

Magnetic resonance images from 25 PPA patients (9AD-related PPA, 10 svPPA, 6 nfvPPA) and a healthy control cohort were used to calculate cortical thickness in three regions of interest (ROIs). The three ROIs being the left-hemispheric loci of maximal reported degeneration for each of the three variants of PPA.

Results

Consistent with past studies, the most severe cortical thinning was in the posterior perisylvian ROI in AD-related PPA; the ventral temporal ROI in svPPA; and the frontal opercular ROI in nfvPPA. Significant cortical thinning in AD-related PPA, however, was evident in all three ROIs. In contrast, thinning in svPPA and nfvPPA was largely restricted to their known peak loci of degeneration.

Conclusions

Although cortical degeneration in AD-related PPA is maximal in the left posterior perisylvian region, it extends more diffusely throughout the left hemisphere language network offering a plausible explanation for why the linguistic profile of lvPPA so often includes additional semantic and grammatic deficits.

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lvPPA is associated with AD pathology.

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AD-PPA present with more extensive deficits than lvPPA.

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Atrophy in AD-PPA encompasses the peak atrophy sites of the other PPA subtypes.

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The extended atrophy in AD-PPA explains the heterogeneity of linguistic deficits.

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.

Exploratory structural assessment in craniocervical dystonia: Global and differential analyses

Introduction

Our goal was to investigate the cortical thickness and subcortical volume in subjects with craniocervical dystonia and its subgroups.

Methods

We studied 49 subjects, 17 with cervical dystonia, 18 with blepharospasm or oromandibular dystonia, and 79 healthy controls. We performed a whole group analysis, followed by a subgroup analysis. We used Freesurfer software to measure cortical thickness, subcortical volume and to perform a primary exploratory analysis in the craniocervical dystonia group, complemented by a region of interest analysis. We also performed a secondary analysis, with data generated from Freesurfer for subgroups, corrected by false discovery rate. We then performed an exploratory generalized linear model with significant areas for the previous steps using clinical features as independent variables.

Results

The primary exploratory analysis demonstrated atrophy in visual processing regions in craniocervical dystonia. The secondary analysis demonstrated atrophy in motor, sensory, and visual regions in blepharospasm or oromandibular dystonia, as well as in limbic regions in cervical dystonia. Cervical dystonia patients also had greater cortical thickness than blepharospasm or oromandibular dystonia patients in frontal pole and medial orbitofrontal regions. Finally, we observed an association between precuneus, age of onset of dystonia and age at the MRI exam, in craniocervical dystonia; between motor and limbic regions and age at the exam, clinical score and time on botulinum toxin in cervical dystonia and sensory regions and age of onset and time on botulinum toxin in blepharospasm or oromandibular dystonia.

Conclusions

We detected involvement of visual processing regions in craniocervical dystonia, and a pattern of involvement in cervical dystonia and blepharospasm or oromandibular dystonia, including motor, sensory and limbic areas. We also showed an association of cortical thickness atrophy and younger onset age, older age at the MRI exam, higher clinical score and an uncertain association with longer time on botulinum toxin.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

The Down syndrome brain in the presence and absence of fibrillar β-amyloidosis

People with Down syndrome (DS) have a neurodevelopmentally distinct brain and invariably developed amyloid neuropathology by age 50. This cross-sectional study aimed to provide a detailed account of DS brain morphology and the changes occuring with amyloid neuropathology. Forty-six adults with DS underwent structural and amyloid imaging—the latter using Pittsburgh compound B (PIB) to stratify the cohort into PIB-positive (n = 19) and PIB-negative (n = 27). Age-matched controls (n = 30) underwent structural imaging. Group differences in deep gray matter volumetry and cortical thickness were studied. PIB-negative people with DS have neurodevelopmentally atypical brain, characterized by disproportionately thicker frontal and occipitoparietal cortex and thinner motor cortex and temporal pole with larger putamina and smaller hippocampi than controls. In the presence of amyloid neuropathology, the DS brains demonstrated a strikingly similar pattern of posterior dominant cortical thinning and subcortical atrophy in the hippocampus, thalamus, and striatum, to that observed in non-DS Alzheimer's disease. Care must be taken to avoid underestimating amyloid-associated morphologic changes in DS due to disproportionate size of some subcortical structures and thickness of the cortex.

Relationship between Clinical Parameters and Brain Structure in Sporadic Amyotrophic Lateral Sclerosis Patients According to Onset Type: A Voxel-Based Morphometric Study

Background and purpose

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, phenotypically heterogeneous neurodegenerative disease affecting mainly the motor neuron system. The present voxel-based morphometry (VBM) study investigated whether patterns of brain atrophy differ among sporadic ALS subtypes.

Material and methods

Sporadic ALS patients (n = 62) with normal cognition and age-matched healthy controls (n = 57) were included in the study. ALS patients were divided into limb- and bulbar-onset groups according to clinical manifestations at symptom onset (n = 48 and 14, respectively). Clinical measures were ALS Functional Rating Scale-Revised (ALSFRS-R) score, disease duration, and forced vital capacity (FVC). Patterns of brain atrophy between ALS subgroups were compared by VBM.

Results

In limb-onset ALS patients, atrophy was largely confined to the motor cortex and adjacent pre- and postcentral regions. However, in the bulbar-onset group, affected regions were more widespread and included these same areas but also extended to the bilateral frontotemporal and left superior temporal and supramarginal gyri, and multiple regression analysis revealed that their ALSFRS-R scores were associated with extensive loss of gray matter while FVC was related to atrophy in subcortical regions of the left superior temporal gyrus. In limb-onset ALS patients, disease duration was related to the degree of atrophy in the motor and adjacent areas.

Conclusion

Sporadic ALS subtypes show different patterns of brain atrophy. Neural networks related to limb and bulbar motor functions in each ALS subtype may underlie their distinct patterns of cerebral atrophy. That is, more extensive cortical and subcortical atrophy is correlated with greater ALSFRS-R severity and shorter disease duration in the bulbar-onset subtype and may explain the poor prognosis of these patients.

Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment

Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.

Brain imaging of neurovascular dysfunction in Alzheimer's disease

Neurovascular dysfunction, including blood-brain barrier (BBB) breakdown and cerebral blood flow (CBF) dysregulation and reduction, are increasingly recognized to contribute to Alzheimer's disease (AD). The spatial and temporal relationships between different pathophysiological events during preclinical stages of AD, including cerebrovascular dysfunction and pathology, amyloid and tau pathology, and brain structural and functional changes remain, however, still unclear. Recent advances in neuroimaging techniques, i.e., magnetic resonance imaging (MRI) and positron emission tomography (PET), offer new possibilities to understand how the human brain works in health and disease. This includes methods to detect subtle regional changes in the cerebrovascular system integrity. Here, we focus on the neurovascular imaging techniques to evaluate regional BBB permeability (dynamic contrast-enhanced MRI), regional CBF changes (arterial spin labeling- and functional-MRI), vascular pathology (structural MRI), and cerebral metabolism (PET) in the living human brain, and examine how they can inform about neurovascular dysfunction and vascular pathophysiology in dementia and AD. Altogether, these neuroimaging approaches will continue to elucidate the spatio-temporal progression of vascular and neurodegenerative processes in dementia and AD and how they relate to each other.

Structural and diffusion imaging versus clinical assessment to monitor amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a progressive neurodegenerative disease that affects upper and lower motor neurons. Observational and intervention studies can be tracked using clinical measures such as the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) but for a complete understanding of disease progression, objective in vivo biomarkers of both central and peripheral motor pathway pathology are highly desirable. The aim of this study was to determine the utility of structural and diffusion imaging as central nervous system biomarkers compared to the standard clinical measure, ALSFRS-R, to track longitudinal evolution using three time-point measurements. N = 34 patients with ALS were scanned and clinically assessed three times at a mean of three month time intervals. The MRI biomarkers were structural T1-weighted volumes for cortical thickness measurement as well as deep grey matter volumetry, voxel-based morphometry and diffusion tensor imaging (DTI). Cortical thickness focused specifically on the precentral gyrus while quantitative DTI biomarkers focused on the corticospinal tracts. The evolution of imaging biomarkers and ALSFRS-R scores over time were analysed using a mixed effects model that accounted for the scanning interval as a fixed effect variable, and, the initial measurements and time from onset as random variables. The mixed effects model showed a significant decrease in the ALSFRS-R score, (p < 0.0001, and an annual rate of change (AROC) of − 7.3 points). Similarly, fractional anisotropy of the corticospinal tract showed a significant decrease (p = 0.009, AROC = − 0.0066) that, in turn, was driven by a significant increase in radial diffusivity combined with a trend to decrease in axial diffusivity. No significant change in cortical thickness of the precentral gyrus was found (p > 0.5). In addition, deep grey matter volumetry and voxel-based morphometry also identified no significant changes. Furthermore, the availability of three time points was able to indicate that there was a linear progression in both clinical and fractional anisotropy measures adding to the validity of these results. The results indicate that DTI is clearly a superior imaging marker compared to atrophy for tracking the evolution of the disease and can act as a central nervous biomarker in longitudinal studies. It remains, however, less sensitive than the ALSFRS-R score for monitoring decline over time.

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Three time points were used for the first time to assess imaging biomarkers in ALS.

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Fractional anisotropy of the corticospinal tract showed linear decline.

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No atrophy measure was useful to track change.

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The ALSFRS-R clinical scale remains more sensitive than imaging biomarkers.

Combining multiple anatomical MRI measures improves Alzheimer's disease classification

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

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Desperately seeking grey matter volume changes in sleep apnea: A methodological review of magnetic resonance brain voxel-based morphometry studies

Cognitive impairment related to obstructive sleep apnea might be explained by subtle changes in brain anatomy. This has been mainly investigated using magnetic resonance brain scans coupled with a voxel-based morphometry analysis. However, this approach is prone to several methodological pitfalls that may explain the large discrepancy in the results reported in the literature. We critically reviewed twelve papers addressing grey matter volume modifications in association with obstructive sleep apnea. Finally, based on strict methodological criteria, only three studies reported robust, but conflicting, results. No clear evidence has emerged and exploring brain alteration due to obstructive sleep apnea should thus be considered as an open field. We provide recommendations for designing additional robust voxel-based morphometry studies, notably the use of larger cohorts, which is the only way to solve the underpowered issue and the underestimated role of confounders in neuroimaging studies.

Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis

Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.

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Brain parcellation tools define structures automatically and convert images into standardized and quantitative matrices.

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We tested if an automated tool and the resultant vector of structural volumes can accurately capture anatomical phenotypes.

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The agreement between visual and automated atrophy detection was virtually the same as the inter-evaluator agreement.

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The quantitative indices captured the main anatomical features in brains with atrophy in different degrees and location.

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The image quantification has potential to be an effective, comprehensive, and easily translatable tool for clinical practice.

Mild cognitive impairment, poor episodic memory, and late-life depression are associated with cerebral cortical thinning and increased white matter hyperintensities

In various independent studies to date, cerebral cortical thickness and white matter hyperintensity (WMH) volume have been associated with episodic memory, depression, and mild cognitive impairment (MCI). The aim of this study was to uncover variations in cortical thickness and WMH volume in association with episodic memory, depressive state, and the presence of MCI simultaneously in a single study population. The participants were 186 individuals with MCI (clinical dementia rating [CDR] of 0.5) and 136 healthy elderly controls (HCs; CDR of 0) drawn from two community-based cohort studies in northern Japan. We computed cerebral cortical thickness and WMH volume by using MR scans and statistically analyzed differences in these indices between HCs and MCI participants. We also assessed the associations of these indices with memory performance and depressive state in participants with MCI. Compared with HCs, MCI participants exhibited thinner cortices in the temporal and inferior parietal lobes and greater WMH volumes in the corona radiata and semioval center. In MCI participants, poor episodic memory was associated with thinner cortices in the left entorhinal region and increased WMH volume in the posterior periventricular regions. Compared with non-depressed MCI participants, depressed MCI participants showed reduced cortical thickness in the anterior medial temporal lobe and gyrus adjacent to the amygdala bilaterally, as well as greater WMH volume as a percentage of the total intracranial volume (WMHr). A higher WMHr was associated with cortical thinning in the frontal, temporal, and parietal regions in MCI participants. These results demonstrate that episodic memory and depression are associated with both cortical thickness and WMH volume in MCI participants. Additional longitudinal studies are needed to clarify the dynamic associations and interactions among these indices.

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.