Quantitative structural MRI for early detection of Alzheimer's disease

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Evaluation of the biodistribution and preliminary safety profile of a novel brain-targeted manganese dioxide-based nanotheranostic system for Alzheimer's disease

A novel brain-targeted and reactive oxygen species-activatable manganese dioxide containing nanoparticle system functionalized with anti-amyloid-β antibody (named aAβ-BTRA-NC) developed by our group has shown great promise as a highly selective magnetic resonance imaging (MRI) contrast agent for early detection and multitargeted disease-modifying treatment of Alzheimer's disease (AD). To further evaluate the suitability of the formulation for future clinical application, we investigated the safety, biodistribution, and pharmacokinetic profile of aAβ-BTRA-NC in a transgenic TgCRND8 mouse AD model, wild type (WT) littermate, and CD-1 mice. Dose-ascending studies demonstrated that aAβ-BTRA-NC was well-tolerated by the animals up to 300 μmol Mn/kg body weight [b.w.], 3 times the efficacious dose for early AD detection without apparent adverse effects; Histopathological, hematological, and biochemical analyses indicated that a single dose of aAβ-BTRA-NC did not cause any toxicity in major organs. Immunotoxicity data showed that aAβ-BTRA-NC was safer than commercially available gadolinium-based MRI contrast agents at an equivalent dose of 100 μmol/kg b.w. of metal ions. Intravenously administered aAβ-BTRA-NC was taken up by main organs with the order of liver, kidneys, intestines, spleen, followed by other organs, and cleared after one day to one week post injection. Pharmacokinetic analysis indicated that the plasma concentration profile of aAβ-BTRA-NC followed a 2-compartmental model with faster clearance in the AD mice than in the WT mice. The results suggest that aAβ-BTRA-NC exhibits a strong safety profile as a nanotheranostic agent which warrants more robust preclinical development for future clinical applications.

Deep Learning-based Approach for Brainstem and Ventricular MR Planimetry: Application in Patients with Progressive Supranuclear Palsy

Purpose To develop a fast and fully automated deep learning (DL)-based method for the MRI planimetric segmentation and measurement of the brainstem and ventricular structures most affected in patients with progressive supranuclear palsy (PSP). Materials and Methods In this retrospective study, T1-weighted MR images in healthy controls (n = 84) were used to train DL models for segmenting the midbrain, pons, middle cerebellar peduncle (MCP), superior cerebellar peduncle (SCP), third ventricle, and frontal horns (FHs). Internal, external, and clinical test datasets (n = 305) were used to assess segmentation model reliability. DL masks from test datasets were used to automatically extract midbrain and pons areas and the width of MCP, SCP, third ventricle, and FHs. Automated measurements were compared with those manually performed by an expert radiologist. Finally, these measures were combined to calculate the midbrain to pons area ratio, MR parkinsonism index (MRPI), and MRPI 2.0, which were used to differentiate patients with PSP (n = 71) from those with Parkinson disease (PD) (n = 129). Results Dice coefficients above 0.85 were found for all brain regions when comparing manual and DL-based segmentations. A strong correlation was observed between automated and manual measurements (Spearman ρ > 0.80, P < .001). DL-based measurements showed excellent performance in differentiating patients with PSP from those with PD, with an area under the receiver operating characteristic curve above 0.92. Conclusion The automated approach successfully segmented and measured the brainstem and ventricular structures. DL-based models may represent a useful approach to support the diagnosis of PSP and potentially other conditions associated with brainstem and ventricular alterations. Keywords: MR Imaging, Brain/Brain Stem, Segmentation, Quantification, Diagnosis, Convolutional Neural Network Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Mohajer in this issue.

Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders

Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

PURPOSE

Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks.

METHODS

We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures.

CONCLUSION

Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer's Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort

BACKGROUND

Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker.

OBJECTIVE

We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients.

METHODS

One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform.

RESULTS

The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed.

CONCLUSION

Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.

Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant® and NeuroGage® in Patients With Traumatic Brain Injury

Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.

Impact of Chronic Conditions and Dementia in Rural West Texas: A Healthy Aging Study

Alzheimer's disease (AD) is a devastating illness in elderly individuals, that currently has no known cure. Causal genetic factors only account for 1-2% of AD patients. However, other causal factors are still unknown for a majority of AD patients. Currently, multiple factors are implicated in late-onset AD, including unhealthy diet, physical inactivity, traumatic brain injury, chronic conditions, epigenetic factors, and environmental exposures. Although clinical symptoms of dementia are common to all races and ethnic groups, conditions that lead to dementia are different in terms of lifestyle, genetic profile, and socio-economic conditions. Increasing evidence also suggests that some elderly individuals age without cognitive impairments in their 60-90s as seen in rural West Texas, while some individuals progress with chronic conditions and cognitive impairments into their 60s. To understand these discriminations, we assessed current literature on demographic features of health in rural West Texas. This paper also outlines our initiated clinical study with a purpose of understanding the factors that allow some individuals to live without cognitive impairments at the age of 60-90 years, whereas others develop deficits in cognitive function around or above 60 years. Our ongoing study hopes to determine the factors that delay aging in some individuals by investigating various aspects including genetics, epigenetics, ethnicity, biology, culture, and lifestyle. This will be achieved by gathering information about participants' ethnographic profiles, cognitive assessments, blood-profiles, brain scans, and blood-based genomic analyses in relation to lifestyle. The outcomes of our study will provide insights into healthy aging in rural West Texas.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis

The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study

Objectives

We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists’ accuracy and confidence in detecting volume loss, and in differentiating Alzheimer’s disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone.

Methods

Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, ‘non-clinical image analysts’) assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as ‘normal’ or ‘abnormal’ and if ‘abnormal’ as ‘AD’ or ‘FTD’.

Results

The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group’s accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters’ agreement (Cohen’s κ) with the ‘gold standard’ was not significantly affected by the QReport; only the consultant group improved significantly (κ_s 0.41➔0.55, p = 0.04*). Cronbach’s alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from ‘good’ to ‘excellent’.

Conclusion

Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses.

Key Points

• The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone.

• Consultant neuroradiologists’ assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports.

• First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07455-8.

Quantitative MRI Differences Between Early versus Late Onset Alzheimer's Disease

Investigators report greater parietal tau deposition and alternate frontoparietal network involvement in early onset Alzheimer's Disease (EOAD) with onset <65 years as compared with typical late onset AD (LOAD). To determine whether clinical brain MRI volumes reflect these differences in EOAD compared with LOAD. This study investigated the clinical MRI scans of 45 persons with Clinically Probable AD with onset <65 years, and compared them to 32 with Clinically Probable AD with onset ≥65 years. Brain volumes on their T1 MRI scans were quantified with a volumetric program. Receiver operating curve analyses were performed. Persons with EOAD had significantly smaller parietal lobes (volumetric percentiles) than LOAD. Late onset Alzheimer's Disease had a smaller left putamen and hippocampus. Area Under the Curve was 96.5% with brain region delineation of EOAD compared to LOAD. This study indicates parietal atrophy less than 30% of normal on clinical MRI scans is suggestive of EOAD compared to LOAD.

Comparative Utility of Manual versus Automated Segmentation of Hippocampus and Entorhinal Cortex Volumes in a Memory Clinic Sample

Structural neuroimaging is a useful non-invasive biomarker commonly employed to evaluate the integrity of mesial temporal lobe structures that are typically compromised in Alzheimer's disease. Advances in quantitative neuroimaging have permitted the development of automated segmentation protocols (e.g., FreeSurfer) with significantly increased efficiency compared to earlier manual techniques. While these protocols have been found to be suitable for large-scale, multi-site research studies, we were interested in assessing the practical utility and reliability of automated FreeSurfer protocols compared to manual volumetry on routinely acquired clinical scans. Independent validation studies with newer automated segmentation protocols are scarce. Two FreeSurfer protocols for each of two regions of interest-the hippocampus and entorhinal cortex-were compared against manual volumetry. High reliability and agreement was found between FreeSurfer and manual hippocampal protocols, however, there was lower reliability and agreement between FreeSurfer and manual entorhinal protocols. Although based on a the relatively small sample of subjects drawn from a memory clinic (n = 27), our study findings suggest further refinements to improve measurement error and most accurately depict true regional brain volumes using automated segmentation protocols are required, especially for non-hippocampal mesial temporal structures, to achieve maximal utility for routine clinical evaluations.

Epilepsy and aging

The intersection of epilepsy and aging has broad, significant implications. Substantial increases in seizures occur both in the elderly population, who are at a higher risk of developing new-onset epilepsy, and in those with chronic epilepsy who become aged. There are notable gaps in our understanding of aging and epilepsy at the basic and practical levels, which have important consequences. We are in the early stages of understanding the complex relationships between epilepsy and other age-related brain diseases such as stroke, dementia, traumatic brain injury (TBI), and cancer. Furthermore, the clinician must recognize that the presentation and treatment of epilepsy in the elderly are different from those of younger populations. Given the developing awareness of the problem and the capabilities of contemporary, multidisciplinary approaches to advance understanding about the biology of aging and epilepsy, it is reasonable to expect that we will unravel some of the intricacies of epilepsy in the elderly; it is also reasonable to expect that these gains will lead to further improvements in our understanding and treatment of epilepsy for all age groups.

Overview of MR Imaging Volumetric Quantification in Neurocognitive Disorders

This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias. Finally, an overview of general principles for protocol acquisition of magnetic resonance imaging for volumetric quantification will be discussed along with the current choices of FDA cleared algorithms for use in clinical practice.

Targeted metabolomic analysis of nitric oxide/L-arginine pathway metabolites in dementia: association with pathology, severity, and structural brain changes

L-Arginine/NO pathway is altered in Alzheimer disease (AD). Its clinical relevance and pathway status in vascular dementia (VaD) are unknown. Using targeted metabolomics (a liquid chromatography-mass spectrometry) we assessed L-arginine, L-citrulline, dimethylamine (DMA), asymmetric dimethyl arginine (ADMA) and symmetric dimethylarginine (SDMA) in AD (n = 48), mixed-type dementia (MD; n = 34), VaD (n = 40) and non-demented individuals (n = 140) and determined their clinical relevance (the association with dementia pathology, cognitive impairment, and structural brain damage). L-Arginine, ADMA, L-arginine/ADMA, and L-citrulline levels were decreased in dementia and L-arginine, L-citrulline, age and sex were its independent predictors correctly classifying 91% of cases. L-Arginine and L-arginine/ADMA were differentiating between VaD and AD with moderate accuracy. L-Arginine, L-arginine/ADMA, SDMA, and DMA reflected structural brain changes. DMA and L-citrulline were elevated in patients with strategic infarcts and SDMA, L-arginine/ADMA, and DMA were independent predictors of Hachinski ischemic score. ADMA and SDMA accumulation reflected severity of cognitive impairment. In summary, L-Arginine/NO pathway is altered in neurodegenerative and vascular dementia in association with neurodegenerative and vascular markers of brain damage and severity of cognitive impairment.

Segmentation errors and intertest reliability in automated and manually traced hippocampal volumes

Objective

To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy.

Methods

Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer.

Results

FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant.

Interpretation

Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.

The quantitative neuroradiology initiative framework: application to dementia

There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists' experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.

Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation

Brain volume measurements extracted from structural MRI data sets are a widely accepted neuroimaging biomarker to study mouse models of neurodegeneration. Whether to acquire and analyze data in vivo or ex vivo is a crucial decision during the phase of experimental designs, as well as data analysis. In this work, we extracted the brain structures for both longitudinal in vivo and single-time-point ex vivo MRI acquired from the same animals using accurate automatic multi-atlas structural parcellation, and compared the corresponding statistical and classification analysis. We found that most gray matter structures volumes decrease from in vivo to ex vivo, while most white matter structures volume increase. The level of structural volume change also varies between different genetic strains and treatment. In addition, we showed superior statistical and classification power of ex vivo data compared to the in vivo data, even after resampled to the same level of resolution. We further demonstrated that the classification power of the in vivo data can be improved by incorporating longitudinal information, which is not possible for ex vivo data. In conclusion, this paper demonstrates the tissue-specific changes, as well as the difference in statistical and classification power, between the volumetric analysis based on the in vivo and ex vivo structural MRI data. Our results emphasize the importance of longitudinal analysis for in vivo data analysis.

A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease

Motivation

Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging.

Results

We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.

Differential Neurotoxicity Related to Tetracycline Transactivator and TDP-43 Expression in Conditional TDP-43 Mouse Model of Frontotemporal Lobar Degeneration

Frontotemporal lobar degeneration (FTLD) is among the most prevalent dementias of early-onset. Pathologically, FTLD presents with tauopathy or TAR DNA-binding protein 43 (TDP-43) proteinopathy. A biallelic mouse model of FTLD was produced on a mix FVB/129SVE background overexpressing wild-type human TDP-43 (hTDP-43) using tetracycline transactivator (tTA), a system widely used in mouse models of neurological disorders. tTA activates hTDP-43, which is placed downstream of the tetracycline response element. The original study on this transgenic mouse found hippocampal degeneration following hTDP-43 expression, but did not account for independent effects of tTA protein. Here, we initially analyzed the neurotoxic effects of tTA in postweaning age mice of either sex using immunostaining and area measurements of select brain regions. We observed tTA-dependent toxicity selectively in the hippocampus affecting the dentate gyrus significantly more than CA fields, whereas hTDP-43-dependent toxicity in bigenic mice occurred in most other cortical regions. Atrophy was associated with inflammation, activation of caspase-3, and loss of neurons. The atrophy associated with tTA expression was rescuable by the tetracycline analog, doxycycline, in the diet. MRI studies corroborated the patterns of atrophy. tTA-induced degeneration was strain-dependent and was rescued by moving the transgene onto a congenic C57BL/6 background. Despite significant hippocampal atrophy, behavioral tests in bigenic mice revealed no hippocampally mediated memory impairment. Significant atrophy in most cortical areas due solely to TDP-43 expression indicates that this mouse model remains useful for providing critical insight into co-occurrence of TDP-43 pathology, neurodegeneration, and behavioral deficits in FTLD.SIGNIFICANCE STATEMENT The tTA expression system has been widely used in mice to model neurological disorders. The technique allows investigators to reversibly turn on or off disease causing genes. Here, we report on a mouse model that overexpresses human TDP-43 using tTA and attempt to recapitulate features of TDP-43 pathology present in human FTLD. The tTA expression system is problematic, resulting in dramatic degeneration of the hippocampus. Thus, our study adds a note of caution for the use of the tTA system. However, because FTLD is primarily characterized by cortical degeneration and our mouse model shows significant atrophy in most cortical areas due to human TDP-43 overexpression, our animal model remains useful for providing critical insight on this human disease.

Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease: Methodology and Baseline Sample Characteristics

OBJECTIVE

The Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) aimed to recruit 650 individuals, aged from 20 to 90 years, to search for new biomarkers of Alzheimer's disease (AD) and to investigate how multi-faceted lifetime experiences and bodily changes contribute to the brain changes or brain pathologies related to the AD process.

METHODS

All participants received comprehensive clinical and neuropsychological evaluations, multi-modal brain imaging, including magnetic resonance imaging, magnetic resonance angiography, [11C]Pittsburgh compound B-positron emission tomography (PET), and [18F]fluorodeoxyglucose-PET, blood and genetic marker analyses at baseline, and a subset of participants underwent actigraph monitoring and completed a sleep diary. Participants are to be followed annually with clinical and neuropsychological assessments, and biannually with the full KBASE assessment, including neuroimaging and laboratory tests.

RESULTS

As of March 2017, in total, 758 individuals had volunteered for this study. Among them, in total, 591 participants-291 cognitively normal (CN) old-aged individuals, 74 CN young- and middle-aged individuals, 139 individuals with mild cognitive impairment (MCI), and 87 individuals with AD dementia (ADD)-were enrolled at baseline, after excluding 162 individuals. A subset of participants (n=275) underwent actigraph monitoring.

CONCLUSION

The KBASE cohort is a prospective, longitudinal cohort study that recruited participants with a wide age range and a wide distribution of cognitive status (CN, MCI, and ADD) and it has several strengths in its design and methodologies. Details of the recruitment, study methodology, and baseline sample characteristics are described in this paper.

Association between low thyroid-stimulating hormone, posterior cortical atrophy and nitro-oxidative stress in elderly patients with cognitive dysfunction

INTRODUCTION

Cortical atrophy is known to be a valuable sign of cognitive decline. The purpose of this study was to assess the association between low thyroid-stimulating hormone (TSH), posterior cortical atrophy (Koedam score - KS) and nitro-oxidative stress in elderly patients.

MATERIAL AND METHODS

A study (SG) and a control group (CG), each subdivided by gender, were investigated. Subjects older than 59 years with low serum TSH level and with mild cognitive impairment were included in the SG. The CG was formed by subjects free of significant cortical atrophy and free or thyroid dysfunction. Demographic and clinical characteristics of the patients (Mini Mental State Examination, MMSE), Koedam score on cranial magnetic resonance imaging, and blood parameters (TSH, FT4, and nitric oxide - NOx) were assessed.

RESULTS

Subjects in the study group had fewer years of education above the 8th grade compared with the control group (p < 0.0001). A significantly higher percentage of subjects in the study group had a Koedam score of 2 or 3 compared with controls, who had in the majority of cases a Koedam score of zero (p < 0.02). Significantly higher NOx levels were observed when study groups of both genders were compared with corresponding controls (p < 0.001). No significant differences were observed with regard to FT4 (p > 0.70). Nitric oxide was found to be significantly associated with TSH (p < 0.03) and KS (p < 0.002) when the whole study group was considered as well as when just the non-smoker study group was investigated.

CONCLUSIONS

Our study revealed an association between subclinical thyroid hypofunction, nitro-oxidative stress, and posterior cortical atrophy as an early stage of global atrophy.

The Role of Neuroimaging in the Assessment of the Cognitively Impaired Elderly

This article reviews the current diagnostic tools that are available for structural, functional, and molecular imaging of the brain, summarizing some of the key findings that have been reported in individuals diagnosed with Alzheimer disease, mild cognitive impairment, prodromal AD, or other prevalent dementias. Given recent advances in the development of amyloid PET tracers, current guidelines for the use of amyloid PET imaging in patients with cognitive complaints are reviewed. In addition, data addressing the potential value of amyloid PET imaging in the clinical setting are highlighted.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

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Aim was to evaluate the potential of multi-center MRI data for individual PPA diagnosis.

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We used support vector machine classification in PPA variants and healthy controls.

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We compared a whole brain approach with a ROI (taken from meta-analyses) approach.

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Accuracies were overall quite high, for both, the whole brain and the ROI approach.

Magnetic Resonance Volumetry: Prediction of Subjective Memory Complaints and Mild Cognitive Impairment, and Associations with Genetic and Cardiovascular Risk Factors

Background/Aims

Subjective memory complaints (SMC) are strong predictors of mild cognitive impairment (MCI) and subsequent Alzheimer's disease. Our aims were to see if fully automated cerebral MR volume measurements could distinguish subjects with SMC and MCI from controls, and if probable parental late-onset Alzheimer's disease (LOAD), apolipoprotein E ε4 genotype, total plasma homocysteine, and cardiovascular risk factors were associated with MR volumetric findings.

Methods

198 stroke-free subjects comprised the control (n = 58), the SMC (n = 25) and the MCI (n = 115) groups. Analysis of covariance and receiver operating characteristic curve was used to see if MR volumetry distinguished subjects with SMC and MCI from controls.

Results

Subjects with SMC and MCI had significantly larger lateral ventricles and smaller hippocampal volumes than controls. The area under the curve in subjects with SMC and MCI compared to that of controls was less than 0.68 for all volumes of intracranial structures. There was an interaction between sex and probable parental LOAD for hippocampal volume, with a significant association between probable parental LOAD and hippocampal volume in women.

Conclusions

Fully automated MR volumetry can distinguish subjects with SMC and MCI from controls in a general population, but insufficiently to assume a clear clinical role. Research on sporadic LOAD might benefit from a sex-specific search for genetic risk factors.

Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease

AIMS

To assess how hippocampal volume (HV) from volumetric magnetic resonance imaging (vMRI) is related to the amyloid status at different stages of Alzheimer's disease (AD) and its relevance to patient care.

METHODS

We evaluated the ability of HV to predict the florbetapir positron emission tomography (PET) amyloid positive/negative status by group in healthy controls (HC, n = 170) and early/late mild cognitive impairment (EMCI, n = 252; LMCI, n = 136), and AD dementia (n = 75) subjects from the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity (ADNI-GO) and ADNI2. Logistic regression analyses, including elastic net classification modeling with 10-fold cross-validation, were used with age and education as covariates.

RESULTS

HV predicted amyloid status only in LMCI using either logistic regression [area under the curve (AUC) = 0.71, p < 0.001] or elastic net classification modeling [positive predictive value (PPV) = 72.7%]. In EMCI, age (AUC = 0.70, p < 0.0001) and age and/or education (PPV = 63.1%), but not HV, predicted amyloid status.

CONCLUSION

Using clinical neuroimaging, HV predicted amyloid status only in LMCI, suggesting that HV is not a biomarker surrogate for amyloid PET in clinical applications across the full diagnostic spectrum.

Sensitivity and specificity of executive function tests for Alzheimer's disease

Decline in executive function (EF) occurs early in Alzheimer's disease (AD) and can interfere with daily functioning. Unfortunately, little is known about the relative ability of traditional EF tests to detect these cognitive changes. Given that timely diagnosis and intervention are essential to improving functional outcome in this population, our aim was to identify the specific EF measures that best differentiated mild dementia from normal aging. Thirty-one patients with mild AD and 31 controls were administered 7 EF tests. Findings indicated significant between-group differences on all measures except Wisconsin Card Sorting Test. The remaining 6 tests displayed fair to good accuracy discriminating between AD cases and controls. Only category fluency and Tower of London test remained in the final regression model that yielded the highest AUC of 0.90, which was not statistically different from that of either test alone. Overall, most of the tests employed were valid for assessing mild EF disturbances. Specifically, the two measures can be used in isolation for quick screening or in combination to facilitate a more in-depth evaluation of EF performance. This study contributes to clinical field by testifying to the validity of various EF tests to identify AD-related compromises in this cognitive domain.

Structural Neuroimaging of the Medial Temporal Lobe in Alzheimer's Disease Clinical Trials

Atrophy in the medial temporal lobe (MTA) is being used as a criterion to support a diagnosis of Alzheimer's disease (AD). There are several structural neuroimaging approaches for quantifying MTA, including semiquantitative visual rating scales, volumetry (3D), planimetry (2D), and linear measures (1D). Current applications of structural neuroimaging in Alzheimer's disease clinical trials (ADCTs) incorporate it as a tool for improving the selection of subjects for enrollment or for stratification, for tracking disease progression, or providing evidence of target engagement for new therapeutic agents. It may also be used as a surrogate marker, providing evidence of disease-modifying effects. However, despite the widespread use of volumetric magnetic resonance imaging (MRI) in ADCTs, there are some important challenges and limitations, such as difficulties in the interpretation of results, limitations in translating results into clinical practice, and reproducibility issues, among others. Solutions to these issues may arise from other methodologies that are able to link the results of volumetric MRI from trials with conventional MRIs performed in routine clinical practice (linear or planimetric methods). Also of potential benefit are automated volumetry, using indices for comparing the relative rate of atrophy of different regions instead of absolute rates of atrophy, and combining structural neuroimaging with other biomarkers. In this review, authors present the existing structural neuroimaging approaches for MTA quantification. They then discuss solutions to the limitations of the different techniques as well as the current challenges of the field. Finally, they discuss how the current advances in AD neuroimaging can help AD diagnosis.

Imaging of neurodegenerative cognitive and behavioral disorders: practical considerations for dementia clinical practice

This chapter reviews clinical applications and imaging findings useful in medical practice relating to neurodegenerative cognitive/dementing disorders. The preponderance of evidence and consensus guidelines support an essential role of multitiered neuroimaging in the evaluation and management of neurodegenerative cognitive/dementia syndrome that range in severity from mild impairments to frank dementia. Additionally, imaging features are incorporated in updated clinical and research diagnostic criteria for most dementias, including Alzheimer's disease (AD), Dementia with Lewy bodies (DLB), Frontotemporal Lobar Degenerations/Frontotemporal Dementia (FTD), and Vascular Cognitive Impairment (VCI). Best clinical practices dictate that structural imaging, preferably with magnetic resonance imaging (MRI) when possible and computed tomography when not, be obtained as a first-tier approach during the course of a thorough clinical evaluation to improve diagnostic confidence and assess for nonneurodegenerative treatable conditions that may cause or substantially contribute to cognitive/behavioral symptoms or which may dictate a substantial change in management. These conditions include less common structural (e.g., mass lesions such as tumors and hematomas; normal-pressure hydrocephalus), inflammatory, autoimmune and infectious conditions, and more common comorbid contributing conditions (e.g., vascular cerebral injury causing leukoaraiosis, infarcts, or microhemorrhages) that can produce a mixed dementia syndrome. When, after appropriate clinical, cognitive/neuropsychologic, and structural neuroimaging assessment, a dementia specialist remains in doubt regarding etiology and appropriate management, second-tier imaging with molecular methods, preferably with fluorodexoyglucose positron emission tomography (PET) (or single-photon emission computed tomography if PET is unavailable) can provide more diagnostic specificity (e.g., help differentiate between atypical AD and FTD as the etiology for a frontal/dysexecutive syndrome). The potential clinical utility of other promising methods, whether already approved for use (e.g., amyloid PET) or as yet only used in research (e.g., tau PET, functional MRI, diffusor tensor imaging), remains to be proven for widespread use in community practice. However, these constitute unreimbursed third-tier options that merit further study for clinical and cost-effective utility. In the future, combination use of imaging methods will likely improve diagnostic accuracy.

Comparison of Automated Brain Volume Measures obtained with NeuroQuant and FreeSurfer

PURPOSE

To examine intermethod reliabilities and differences between FreeSurfer and the FDA-cleared congener, NeuroQuant, both fully automated methods for structural brain MRI measurements.

MATERIALS AND METHODS

MRI scans from 20 normal control subjects, 20 Alzheimer's disease patients, and 20 mild traumatically brain-injured patients were analyzed with NeuroQuant and with FreeSurfer. Intermethod reliability was evaluated.

RESULTS

Pairwise correlation coefficients, intraclass correlation coefficients, and effect size differences were computed. NeuroQuant versus FreeSurfer measures showed excellent to good intermethod reliability for the 21 regions evaluated (r: .63 to .99/ICC: .62 to .99/ES: -.33 to 2.08) except for the pallidum (r/ICC/ES = .31/.29/-2.2) and cerebellar white matter (r/ICC/ES = .31/.31/.08). Volumes reported by NeuroQuant were generally larger than those reported by FreeSurfer with the whole brain parenchyma volume reported by NeuroQuant 6.50% larger than the volume reported by FreeSurfer. There was no systematic difference in results between the 3 subgroups.

CONCLUSION

NeuroQuant and FreeSurfer showed good to excellent intermethod reliability in volumetric measurements for all brain regions examined with the only exceptions being the pallidum and cerebellar white matter. This finding was robust for normal individuals, patients with Alzheimer's disease, and patients with mild traumatic brain injury.

The Alzheimer's disease mitochondrial cascade hypothesis: progress and perspectives

Ten years ago we first proposed the Alzheimer's disease (AD) mitochondrial cascade hypothesis. This hypothesis maintains that gene inheritance defines an individual's baseline mitochondrial function; inherited and environmental factors determine rates at which mitochondrial function changes over time; and baseline mitochondrial function and mitochondrial change rates influence AD chronology. Our hypothesis unequivocally states in sporadic, late-onset AD, mitochondrial function affects amyloid precursor protein (APP) expression, APP processing, or beta amyloid (Aβ) accumulation and argues if an amyloid cascade truly exists, mitochondrial function triggers it. We now review the state of the mitochondrial cascade hypothesis, and discuss it in the context of recent AD biomarker studies, diagnostic criteria, and clinical trials. Our hypothesis predicts that biomarker changes reflect brain aging, new AD definitions clinically stage brain aging, and removing brain Aβ at any point will marginally impact cognitive trajectories. Our hypothesis, therefore, offers unique perspective into what sporadic, late-onset AD is and how to best treat it.

Imaging as a biomarker in drug discovery for Alzheimer's disease: is MRI a suitable technology?

This review provides perspectives on the utility of magnetic resonance imaging (MRI) as a neuroimaging approach in the development of novel treatments for Alzheimer's disease. These considerations were generated in a roundtable at a recent Wellcome Trust meeting that included experts from academia and industry. It was agreed that MRI, either structural or functional, could be used as a diagnostic, for assessing worsening of disease status, for monitoring vascular pathology, and for stratifying clinical trial populations. It was agreed also that MRI implementation is in its infancy, requiring more evidence of association with the disease states, test-retest data, better standardization across multiple clinical sites, and application in multimodal approaches which include other imaging technologies, such as positron emission tomography, electroencephalography, and magnetoencephalography.

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects.

An expanded role for neuroimaging in the evaluation of memory impairment

SUMMARY

Alzheimer disease affects millions of people worldwide. The neuropathologic process underlying this disease begins years, if not decades, before the onset of memory decline. Recent advances in neuroimaging suggest that it is now possible to detect Alzheimer-associated neuropathologic changes well before dementia onset. Here, we evaluate the role of recently developed in vivo biomarkers in the clinical evaluation of Alzheimer disease. We discuss how assessment strategies might incorporate neuroimaging markers to better inform patients, families, and clinicians when memory impairment prompts a search for diagnosis and management options.

Enrichment and stratification for predementia Alzheimer disease clinical trials

The tau and amyloid pathobiological processes underlying Alzheimer disease (AD) progresses slowly over periods of decades before clinical manifestation as mild cognitive impairment (MCI), then more rapidly to dementia, and eventually to end-stage organ failure. The failure of clinical trials of candidate disease modifying therapies to slow disease progression in patients already diagnosed with early AD has led to increased interest in exploring the possibility of early intervention and prevention trials, targeting MCI and cognitively healthy (HC) populations. Here, we stratify MCI individuals based on cerebrospinal fluid (CSF) biomarkers and structural atrophy risk factors for the disease. We also stratify HC individuals into risk groups on the basis of CSF biomarkers for the two hallmark AD pathologies. Results show that the broad category of MCI can be decomposed into subsets of individuals with significantly different average regional atrophy rates. By thus selectively identifying individuals, combinations of these biomarkers and risk factors could enable significant reductions in sample size requirements for clinical trials of investigational AD-modifying therapies, and provide stratification mechanisms to more finely assess response to therapy. Power is sufficiently high that detecting efficacy in MCI cohorts should not be a limiting factor in AD therapeutics research. In contrast, we show that sample size estimates for clinical trials aimed at the preclinical stage of the disorder (HCs with evidence of AD pathology) are prohibitively large. Longer natural history studies are needed to inform design of trials aimed at the presymptomatic stage.

Temporal lobe epilepsy: quantitative MR volumetry in detection of hippocampal atrophy

PURPOSE

To determine the ability of fully automated volumetric magnetic resonance (MR) imaging to depict hippocampal atrophy (HA) and to help correctly lateralize the seizure focus in patients with temporal lobe epilepsy (TLE).

MATERIALS AND METHODS

This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Volumetric MR imaging data were analyzed for 34 patients with TLE and 116 control subjects. Structural volumes were calculated by using U.S. Food and Drug Administration-cleared software for automated quantitative MR imaging analysis (NeuroQuant). Results of quantitative MR imaging were compared with visual detection of atrophy, and, when available, with histologic specimens. Receiver operating characteristic analyses were performed to determine the optimal sensitivity and specificity of quantitative MR imaging for detecting HA and asymmetry. A linear classifier with cross validation was used to estimate the ability of quantitative MR imaging to help lateralize the seizure focus.

RESULTS

Quantitative MR imaging-derived hippocampal asymmetries discriminated patients with TLE from control subjects with high sensitivity (86.7%-89.5%) and specificity (92.2%-94.1%). When a linear classifier was used to discriminate left versus right TLE, hippocampal asymmetry achieved 94% classification accuracy. Volumetric asymmetries of other subcortical structures did not improve classification. Compared with invasive video electroencephalographic recordings, lateralization accuracy was 88% with quantitative MR imaging and 85% with visual inspection of volumetric MR imaging studies but only 76% with visual inspection of clinical MR imaging studies.

CONCLUSION

Quantitative MR imaging can depict the presence and laterality of HA in TLE with accuracy rates that may exceed those achieved with visual inspection of clinical MR imaging studies. Thus, quantitative MR imaging may enhance standard visual analysis, providing a useful and viable means for translating volumetric analysis into clinical practice.

Biomarkers for the clinical evaluation of the cognitively impaired elderly: amyloid is not enough

The number of elderly patients seeking clinical treatment for memory problems will rise sharply in coming years as our population ages. These patients present a challenge for diagnosis and prognosis since cognitive problems in older patients can arise from many etiologies, some of which are curable. With the development of clinically available biomarkers for detecting Alzheimer's disease pathology in living patients, evaluation of cognitively impaired elderly patients is about to undergo a major paradigm shift. This article describes the two classes of biomarkers available for assessing Alzheimer's disease risk: those that indicate presence of amyloid pathology and those that provide evidence of neuronal injury and neurodegeneration. We argue that, currently, incorporation of biomarkers of neurodegeneration can help in patient prognosis whereas tests for amyloid, if used in isolation, have potential for harm. Amyloid tests are clinically useful only when evidence suggests progressive cognitive decline or neurodegeneration.

Reduced platelet amyloid precursor protein ratio (APP ratio) predicts conversion from mild cognitive impairment to Alzheimer's disease

Studies have shown that platelet APP ratio (representing the percentage of 120-130 kDa to 110 kDa isoforms of the amyloid precursor protein) is reduced in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). In the present study, we sought to determine if baseline APP ratio predicts the conversion from MCI to AD dementia after 4 years of longitudinal assessment. Fifty-five older adults with varying degrees of cognitive impairment (34 with MCI and 21 with AD) were assessed at baseline and after 4 years. MCI patients were re-classified according to the conversion status upon follow-up: 25 individuals retained the diagnostic status of MCI and were considered as stable cases (MCI-MCI); conversely, in nine cases the diagnosis of dementia due to AD was ascertained. The APP ratio (APPr) was determined by the Western blot method in samples of platelets collected at baseline. We found a significant reduction of APPr in MCI patients who converted to dementia upon follow-up. These individuals had baseline APPr values similar to those of demented AD patients. The overall accuracy of APPr to identify subjects with MCI who will progress to AD was 0.74 ± 0.10, p = 0.05. The cut-off of 1.12 yielded a sensitivity of 75 % and a specificity of 75 %. Platelet APPr may be a surrogate marker of the disease process in AD, with potential implications for the assessment of abnormalities in the APP metabolism in patients with and at risk for dementia. However, diagnostic accuracy was relatively low. Therefore, studies in larger samples are needed to determine whether APPr may warrant its use as a biomarker to support the early diagnosis of AD.

Predicting MCI outcome with clinically available MRI and CSF biomarkers

OBJECTIVE

To determine the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI).

METHODS

We stratified 192 MCI participants into positive and negative risk groups on the basis of 1) degree of learning impairment on the Rey Auditory Verbal Learning Test; 2) medial temporal atrophy, quantified from Food and Drug Administration-approved software for automated vMRI analysis; and 3) CSF biomarker levels(.) We also stratified participants based on combinations of risk factors. We computed Cox proportional hazards models, controlling for age, to assess 3-year risk of converting to AD as a function of risk group and used Kaplan-Meier analyses to determine median survival times.

RESULTS

When risk factors were examined separately, individuals testing positive showed significantly higher risk of converting to AD than individuals testing negative (hazard ratios [HR] 1.8-4.1). The joint presence of any 2 risk factors substantially increased risk, with the combination of greater learning impairment and increased atrophy associated with highest risk (HR 29.0): 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. The presence of medial temporal atrophy was associated with shortest median dementia-free survival (15 months).

CONCLUSIONS

Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD.

Multi-modal MRI analysis with disease-specific spatial filtering: initial testing to predict mild cognitive impairment patients who convert to Alzheimer's disease

BACKGROUND

Alterations of the gray and white matter have been identified in Alzheimer's disease (AD) by structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). However, whether the combination of these modalities could increase the diagnostic performance is unknown.

METHODS

Participants included 19 AD patients, 22 amnestic mild cognitive impairment (aMCI) patients, and 22 cognitively normal elderly (NC). The aMCI group was further divided into an "aMCI-converter" group (converted to AD dementia within 3 years), and an "aMCI-stable" group who did not convert in this time period. A T(1)-weighted image, a T(2) map, and a DTI of each participant were normalized, and voxel-based comparisons between AD and NC groups were performed. Regions-of-interest, which defined the areas with significant differences between AD and NC, were created for each modality and named "disease-specific spatial filters" (DSF). Linear discriminant analysis was used to optimize the combination of multiple MRI measurements extracted by DSF to effectively differentiate AD from NC. The resultant DSF and the discriminant function were applied to the aMCI group to investigate the power to differentiate the aMCI-converters from the aMCI-stable patients.

RESULTS

The multi-modal approach with AD-specific filters led to a predictive model with an area under the receiver operating characteristic curve (AUC) of 0.93, in differentiating aMCI-converters from aMCI-stable patients. This AUC was better than that of a single-contrast-based approach, such as T(1)-based morphometry or diffusion anisotropy analysis.

CONCLUSION

The multi-modal approach has the potential to increase the value of MRI in predicting conversion from aMCI to AD.

Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis

PURPOSE

To assess whether single-time-point and longitudinal volumetric magnetic resonance (MR) imaging measures provide predictive prognostic information in patients with amnestic mild cognitive impairment (MCI).

MATERIALS AND METHODS

This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from all participants or the participants' legal guardians. Cross-validated discriminant analyses of MR imaging measures were performed to differentiate 164 Alzheimer disease (AD) cases from 203 healthy control cases. Separate analyses were performed by using data from MR images obtained at one time point or by combining single-time-point measures with 1-year change measures. Resulting discriminant functions were applied to 317 MCI cases to derive individual patient risk scores. Risk of conversion to AD was estimated as a continuous function of risk score percentile. Kaplan-Meier survival curves were computed for risk score quartiles. Odds ratios (ORs) for the conversion to AD were computed between the highest and lowest quartile scores.

RESULTS

Individualized risk estimates from baseline MR examinations indicated that the 1-year risk of conversion to AD ranged from 3% to 40% (average group risk, 17%; OR, 7.2 for highest vs lowest score quartiles). Including measures of 1-year change in global and regional volumes significantly improved risk estimates (P = 001), with the risk of conversion to AD in the subsequent year ranging from 3% to 69% (average group risk, 27%; OR, 12.0 for highest vs lowest score quartiles).

CONCLUSION

Relative to the risk of conversion to AD conferred by the clinical diagnosis of MCI alone, MR imaging measures yield substantially more informative patient-specific risk estimates. Such predictive prognostic information will be critical if disease-modifying therapies become available.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101975/-/DC1.