Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer's disease

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer's disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer's disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages, 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepen the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

Fractal Analysis in Neurodegenerative Diseases

Neurodegenerative diseases are defined by progressive nervous system dysfunction and death of neurons. The abnormal conformation and assembly of proteins is suggested to be the most probable cause for many of these neurodegenerative disorders, leading to the accumulation of abnormally aggregated proteins, for example, amyloid β (Aβ) (Alzheimer's disease and vascular dementia), tau protein (Alzheimer's disease and frontotemporal lobar degeneration), α-synuclein (Parkinson's disease and Lewy body dementia), polyglutamine expansion diseases (Huntington disease), or prion proteins (Creutzfeldt-Jakob disease). An aberrant gain-of-function mechanism toward excessive intraparenchymal accumulation thus represents a common pathogenic denominator in all these proteinopathies. Moreover, depending upon the predominant brain area involvement, these different neurodegenerative diseases lead to either movement disorders or dementia syndromes, although the underlying mechanism(s) can sometimes be very similar, and on other occasions, clinically similar syndromes can have quite distinct pathologies. Non-Euclidean image analysis approaches such as fractal dimension (FD) analysis have been applied extensively in quantifying highly variable morphopathological patterns, as well as many other connected biological processes; however, their application to understand and link abnormal proteinaceous depositions to other clinical and pathological features composing these syndromes is yet to be clarified. Thus, this short review aims to present the most important applications of FD in investigating the clinical-pathological spectrum of neurodegenerative diseases.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

DWI-based MR thermometry: could it discriminate Alzheimer's disease from mild cognitive impairment and healthy subjects?

PURPOSE

The aim of this study is to compare lateral ventricular cerebrospinal fluid (CSF) temperature of the patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy subjects (HS) using diffusion-weighted imaging (DWI)-based magnetic resonance (MR) thermometry.

METHODS

Seventy-two patients (37 AD, 19 MCI, 16 HS) who underwent 3-T MR examination from September 2018 to August 2019 were included in this study. Smoking habits, education level, disease duration, and comorbidity status were recorded. Patients were assessed using Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) score. Brain temperatures were measured using DWI-based MR thermometry. Group comparisons of brain temperature were performed using the Pearson chi-square, Mann-Whitney, and Kruskal-Wallis tests. Further analysis was performed using the post hoc Bonferroni test. Receiver operating characteristic (ROC) analysis was also used.

RESULTS

A CDR score of 0.5, 1, and 2 was 2 (5.4%), 14 (37.8%), and 21 (56.8%) in AD, respectively. The median MMSE score had significant differences among groups and also in pairwise comparisons. The median CSF temperature (°C) values showed statistically significant difference among groups (HS: 38.5 °C, MCI: 38.17 °C, AD: 38.0 °C). The post hoc Mann-Whitney U test indicated a significant difference between AD patients and HS (p = 0.009). There were no significant CSF temperature differences in other pairwise comparisons.

CONCLUSION

Lower CSF temperatures were observed in AD patients than in HS, probably due to decreased brain metabolism in AD. DWI-based MR thermometry as a noninvasive imaging method enabling the measurement of CSF temperatures may contribute to the diagnosis of AD.

A novel deep learning based hippocampus subfield segmentation method

The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.

Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines

In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model

Objective:

This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD).

Methods:

357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T₁WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD.

Results:

Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798.

Conclusion:

The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases

Background

Longitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans.

Methods

Different analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington’s disease patients, detected by atlas-based volumetry (ABV).

Results

For the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis.

Conclusions

This theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.

Brain-targeting by optimized 99mTc-olanzapine: in vivo and in silico studies

Purpose: Olanzapine (OLZ) is an atypical antipsychotic agent that is characterized by low brain porousness. The present work aimed to develop radiolabeled olanzapine (OLZ) without colloidal impurities and evaluate its biodistribution following intravenous (I.V.) and intranasal (I.N.) administration as a potential agent for brain diagnosis. Materials and methods: OLZ was radiolabeled with technetium-99m by using sodium dithionite as the reducing agent. Biodistribution of ^99mTc-OLZ complex in mice following I.V. and I.N. administrations was examined. Furthermore, a molecular docking study was performed.Results: Sodium dithionite labeling procedure resulted in highest radiochemical yield (96.30 ± 0.09%) and in vitro stability in serum up to 8 h. Biodistribution study of ^99mTc-OLZ complex showed high brain uptake following I.N. (6.2 ± 0.12% ID/g) and I.V. (5.5 ± 0.09% ID/g) at 0.5 and 1 h post administration (P.I.), respectively. Docking into two brain targets predicts higher affinity of ^99mTc-OLZ than free OLZ. Additionally, docking to P-glycoproteins shows less affinity for the radiolabelled OLZ and hence it is expected to be associated with better brain exposure than free OLZ.Conclusion: These chemical and preliminary biological merits strongly suggest that the ^99mTc-OLZ complex with new reducing agent could be used as a potential diagnostic agent for brain.

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

Resting State Abnormalities of the Default Mode Network in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis

BACKGROUND

Large-scale brain networks such as the default mode network (DMN) are often disrupted in Alzheimer's disease (AD). Numerous studies have examined DMN functional connectivity in those with mild cognitive impairment (MCI), a presumed AD precursor, to discover a biomarker of AD risk. Prior reviews were qualitative or limited in scope or approach.

OBJECTIVE

We aimed to systematically and quantitatively review DMN resting state fMRI studies comparing MCI and healthy comparison (HC) groups.

METHODS

PubMed was searched for relevant articles. Study characteristics were abstracted and the number of studies showing no group difference or hyper- versus hypo-connnectivity in MCI was tallied. A voxel-wise (ES-SDM) meta-analysis was conducted to identify regional group differences.

RESULTS

Qualitatively, our review of 57 MCI versus HC comparisons suggests substantial inconsistency; 9 showed no group difference, 8 showed MCI > HC and 22 showed HC > MCI across the brain, and 18 showed regionally-mixed directions of effect. The meta-analysis of 31 studies revealed areas of significant hypo- and hyper-connectivity in MCI, including hypoconnectivity in the posterior cingulate cortex/precuneus (z = -3.1, p < 0.0001). Very few individual studies, however, showed patterns resembling the meta-analytic results. Methodological differences did not appear to explain inconsistencies.

CONCLUSIONS

The pattern of altered resting DMN function or connectivity in MCI is complex and variable across studies. To date, no index of DMN connectivity qualifies as a useful biomarker of MCI or risk for AD. Refinements to MCI diagnosis, including other biological markers, or longitudinal studies of progression to AD, might identify DMN alterations predictive of AD risk.

Longitudinal Progression Markers of Parkinson's Disease: Current View on Structural Imaging

PURPOSE OF REVIEW

Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson's disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence.

RECENT FINDINGS

Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD. A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.

Test-retest variability of resting-state networks in healthy aging and prodromal Alzheimer's disease

In recent years, changes in resting-state networks (RSN), identified by functional magnetic resonance imaging (fMRI), have gained increasing attention as potential biomarkers and trackers of neurological disorders such as Alzheimer's disease (AD). Intersession reliability of RSN is fundamental to this approach.

In this study, we investigated the test-retest reliability of three memory related RSN (i.e., the default mode, salience, and executive control network) in 15 young, 15 healthy seniors (HS), and 15 subjects affected by mild cognitive impairment (MCI) with positive biomarkers suggestive of incipient AD (6 females each). FMRI was conducted on three separate occasions. Independent Component Analysis decomposed the resting-state data into RSNs. Comparisons of variation in functional connectivity between groups were made applying different thresholds in an explorative approach. Intersession test-retest reliability was evaluated by intraclass correlation coefficient (ICC) comparisons. To assess the effect of gray matter volume loss, motion, cerebrospinal fluid based biomarkers and the time gap between sessions on intersession variation, the former four were correlated separately with the latter.

Data showed that i) young subjects ICCs (relative to HS/MCI-subjects) had higher intersession reliability, ii) stringent statistical thresholds need to be applied to prevent false-positives, iii) both HS and MCI-subjects (relative to young) showed significantly more clusters of intersession variation in all three RSN, iv) while intersession variation was highly correlated with head motion, it was also correlated with biomarkers (especially phospho-tau), the time gap between sessions and local GMV. Results indicate that time gaps between sessions should be kept constant and that head motion must be taken into account when using RSN to assess aging and neurodegeneration. In patients with prodromal AD, re-test reliability may be increased by accouting for overall disease burden by including biomarkers of neuronal injury (especially phospho-tau) in statistical analyses. Local atrophy however, does not seem to play a major role in regards to reliability, but should be used as covariate depending on the research question.

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Intersession reliability of resting state networks is highest in young subjects.

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Test-Retest Variability increases with aging and in MCI.

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Motion and csf-biomarkers correlate with increased variability.

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Motion and biomarkers should be included as confounders in the statistical models.

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Stringent statistical thresholds should be applied to prevent type I-errors.

MR-assisted PET motion correction in simultaneous PET/MRI studies of dementia subjects

BACKGROUND

Subject motion in positron emission tomography (PET) studies leads to image blurring and artifacts; simultaneously acquired magnetic resonance imaging (MRI) data provides a means for motion correction (MC) in integrated PET/MRI scanners.

PURPOSE

To assess the effect of realistic head motion and MR-based MC on static [¹⁸ F]-fluorodeoxyglucose (FDG) PET images in dementia patients.

STUDY TYPE

Observational study.

POPULATION

Thirty dementia subjects were recruited.

FIELD STRENGTH/SEQUENCE

3T hybrid PET/MR scanner where EPI-based and T₁ -weighted sequences were acquired simultaneously with the PET data.

ASSESSMENT

Head motion parameters estimated from high temporal resolution MR volumes were used for PET MC. The MR-based MC method was compared to PET frame-based MC methods in which motion parameters were estimated by coregistering 5-minute frames before and after accounting for the attenuation-emission mismatch. The relative changes in standardized uptake value ratios (SUVRs) between the PET volumes processed with the various MC methods, without MC, and the PET volumes with simulated motion were compared in relevant brain regions.

STATISTICAL TESTS

The absolute value of the regional SUVR relative change was assessed with pairwise paired t-tests testing at the P = 0.05 level, comparing the values obtained through different MR-based MC processing methods as well as across different motion groups. The intraregion voxelwise variability of regional SUVRs obtained through different MR-based MC processing methods was also assessed with pairwise paired t-tests testing at the P = 0.05 level.

RESULTS

MC had a greater impact on PET data quantification in subjects with larger amplitude motion (higher than 18% in the medial orbitofrontal cortex) and greater changes were generally observed for the MR-based MC method compared to the frame-based methods. Furthermore, a mean relative change of ∼4% was observed after MC even at the group level, suggesting the importance of routinely applying this correction. The intraregion voxelwise variability of regional SUVRs was also decreased using MR-based MC. All comparisons were significant at the P = 0.05 level.

DATA CONCLUSION

Incorporating temporally correlated MR data to account for intraframe motion has a positive impact on the FDG PET image quality and data quantification in dementia patients.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1288-1296.

Effects of pharmacological and nonpharmacological treatments on brain functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment: a critical review

BACKGROUND

A growing number of pharmacological and nonpharmacological trials have been performed to test the efficacy of approved or experimental treatments in Alzheimer disease (AD) and mild cognitive impairment (MCI). In this context, functional magnetic resonance imaging (fMRI) may be a good candidate to detect brain changes after a short period of treatment.

MAIN BODY

This critical review aimed to identify and discuss the available studies that have tested the efficacy of pharmacological and nonpharmacological treatments in AD and MCI cases using task-based or resting-state fMRI measures as primary outcomes. A PubMed-based literature search was performed with the use of the three macro-areas: 'disease', 'type of MRI', and 'type of treatment'. Each contribution was individually reviewed according to the Cochrane Collaboration's tool for assessing risk of bias. Study limitations were systematically detected and critically discussed. We selected 34 pharmacological and 13 nonpharmacological articles. According to the Cochrane Collaboration's tool for assessing risk of bias, 40% of these studies were randomized but only a few described clearly the randomization procedure, 36% declared the blindness of participants and personnel, and only 21% reported the blindness of outcome assessment. In addition, 28% of the studies presented more than 20% drop-outs at short- and/or long-term assessments. Additional common shortcomings of the reviewed works were related to study design, patient selection, sample size, choice of outcome measures, management of drop-out cases, and fMRI methods.

CONCLUSION

There is an urgent need to obtain efficient treatments for AD and MCI. fMRI is powerful enough to detect even subtle changes over a short period of treatment; however, the soundness of methods should be improved to enable meaningful data interpretation.

Multimodal PET Imaging of Amyloid and Tau Pathology in Alzheimer Disease and Non-Alzheimer Disease Dementias

Biomarkers of the molecular pathology underpinning dementia syndromes are increasingly recognized as crucial for diagnosis and development of disease-modifying treatments. Amyloid PET imaging is an integral part of the diagnostic assessment of Alzheimer disease. Its use has also deepened understanding of the role of amyloid pathology in Lewy body disorders and aging. Tau PET imaging is an imaging biomarker that will likely play an important role in the diagnosis, monitoring, and treatment in dementias. Using tau PET imaging to examine how tau pathology relates to amyloid and other markers of neurodegeneration will serve to better understand the pathophysiologic cascade that leads to dementia.

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning

There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson’s disease patients according to cognitive status using machine learning. Two independent subject samples were assessed with resting-state fMRI. The first (training) sample comprised 38 healthy controls and 70 Parkinson’s disease patients (27 with mild cognitive impairment). The second (validation) sample included 25 patients (8 with mild cognitive impairment). The Brainnetome atlas was used to reconstruct the functional connectomes. Using a support vector machine trained on features selected through randomized logistic regression with leave-one-out cross-validation, a mean accuracy of 82.6% (p < 0.002) was achieved in separating patients with mild cognitive impairment from those without it in the training sample. The model trained on the whole training sample achieved an accuracy of 80.0% when used to classify the validation sample (p = 0.006). Correlation analyses showed that the connectivity level in the edges most consistently selected as features was associated with memory and executive function performance in the patient group. Our results demonstrate that connection-wise patterns of functional connectivity may be useful for discriminating Parkinson’s disease patients according to the presence of cognitive deficits.

Brain imaging in dementia

The introduction of MRI and positron emission tomography (PET) brain imaging has contributed significantly to the understanding of different dementia syndromes. Over the past 20 years these imaging techniques have been increasingly used for clinical characterisation and differential diagnosis, and to provide insight into the effects on functional capacity of the brain, patterns of spatial distribution of different dementia syndromes and their natural history and evolution over time. Brain imaging is also increasingly used in clinical trials, as part of inclusion criteria and/or as a surrogate outcome measure. Here we review all the relatively specific findings that can be identified with different MRI and PET techniques in each of the most frequent dementing disorders.

MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials

The goal of this work was to assess statistical power to detect treatment effects in Alzheimer’s disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer’s Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid−/ApoE4− group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.

Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study

Background and Purpose

The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer’s. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0).

Methods

Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques.

Results

The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet’s. Cross-sectionally, Freesurfer’s effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC.

Conclusions

Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage.

Multimodal Voxel-Based Meta-Analysis of White Matter Abnormalities in Alzheimer's Disease

An increasing number of MRI investigations suggest that patients with Alzheimer's disease (AD) show not only gray matter decreases but also white matter (WM) abnormalities, including WM volume (WMV) deficits and integrity disruption of WM pathways. In this study, we applied multimodal voxel-wise meta-analytical methods to study WMV and fractional anisotropy in AD. Fourteen studies including 723 participants (340 with AD and 383 controls) were involved. The meta-analysis was performed using effect size signed differential mapping. Significant WMV reductions were observed in bilateral inferior temporal gyrus, splenium of corpus callosum, right parahippocampal gyrus, and hippocampus. Decreased fractional anisotropy was identified mainly in left posterior limb of internal capsule, left anterior corona radiata, left thalamus, and left caudate nucleus. Significant decreases of both WMV and fractional anisotropy were found in left caudate nucleus, left superior corona radiata, and right inferior temporal gyrus. Most findings showed to be highly replicable in the jackknife sensitivity analyses. In conclusion, AD patients show widespread WM abnormalities mainly in bilateral structures related to advanced mental and nervous activities.

Disease Staging and Milestones

This new section to the guidelines was added due to the recognition that clinical milestones are useful indices of the progression of dementia in patients with Alzheimer's disease and could help in the development of stage-specific targeted therapy. This review specifically looks at clinical milestones that could be used in clinical trials, such as global function, function, behaviour, caregiver burden, and quality of life milestones. It also addresses the possible use of biological and surrogate markers for use as milestones - which may eventually replace clinical milestones. It concludes that current definitions of dementia must be broadened beyond cognition alone to include some of the domains listed.

Texture analysis improves level set segmentation of the anterior abdominal wall

PURPOSE

The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention.

METHODS

In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors' approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.

RESULTS

Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2-5 mm) for methods that did not use the texture.

CONCLUSIONS

The authors' approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

Neuroimaging and the search for a cure for Alzheimer disease

As radiologists, our role in the workup of the dementia patient has long been limited by the sensitivity of our imaging tools and lack of effective treatment options. Over the past 30 years, we have made tremendous strides in understanding the genetic, molecular, and cellular basis of Alzheimer disease (AD). We now know that the pathologic features of AD are present 1 to 2 decades prior to development of symptoms, though currently approved symptomatic therapies are administered much later in the disease course. The search for true disease-modifying therapy continues and many clinical trials are underway. Current outcome measures, based on cognitive tests, are relatively insensitive to pathologic disease progression, requiring long, expensive trials with large numbers of participants. Biomarkers, including neuroimaging, have great potential to increase the power of trials by matching imaging methodology with therapeutic mechanism. One of the most important advances over the past decade has been the development of in vivo imaging probes targeted to amyloid beta protein, and one agent is already available for clinical use. Additional advances include automated volumetric imaging methods to quantitate cerebral volume loss. Use of such techniques in small, early phase trials are expected to significantly increase the number and quality of candidate drugs for testing in larger trials. In addition to a critical role in trials, structural, molecular, and functional imaging techniques can give us a window on the etiology of AD and other neurodegenerative diseases. This combination of developments has potential to bring diagnostic radiology to the forefront in AD research, therapeutic trials, and patient care.

White matter abnormalities associated with Alzheimer's disease and mild cognitive impairment: a critical review of MRI studies

In this article, the authors aim to present a critical review of recent MRI studies addressing white matter (WM) abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI), by searching PubMed and reviewing MRI studies evaluating subjects with AD or MCI using WM volumetric methods, diffusion tensor imaging and assessment of WM hyperintensities. Studies have found that, compared with healthy controls, AD and MCI samples display WM volumetric reductions and diffusion tensor imaging findings suggestive of reduced WM integrity. These changes affect complex networks relevant to episodic memory and other cognitive processes, including fiber connections that directly link medial temporal structures and the corpus callosum. Abnormalities in cortico-cortical and cortico-subcortical WM interconnections are associated with an increased risk of progression from MCI to dementia. It can be concluded that WM abnormalities are detectable in early stages of AD and MCI. Degeneration of WM networks causes disconnection among neural cells and the degree of such changes is related to cognitive decline.

Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study

The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24-43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments; notably, quantitative metrics based on image-processing are not used. We propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. To date, automated segmentation algorithms have not been presented to quantify the abdominal wall and potential hernias. In this pilot study with four clinically acquired CT scans on post-operative patients, we demonstrate a novel approach to geometric classification of the abdominal wall and essential abdominal features (including bony landmarks and skin surfaces). Our approach uses a hierarchical design in which the abdominal wall is isolated in the context of the skin and bony structures using level set methods. All segmentation results were quantitatively validated with surface errors based on manually labeled ground truth. Mean surface errors for the outer surface of the abdominal wall was less than 2mm. This approach establishes a baseline for characterizing the abdominal wall for improving VH care.

Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders

Neurological and psychiatric disorders disturb higher cognitive functions and are accompanied by aberrant cortico-cortical axonal pathways or synchronizations of neural activity. A large proportion of neuroimaging studies have focused on examining the focal morphological abnormalities of various gray and white matter structures or the functional activities of brain areas during goal-directed tasks or the resting state, which provides vast quantities of information on both the structural and functional alterations in the patients' brain. However, these studies often ignore the interactions among multiple brain regions that constitute complex brain networks underlying higher cognitive function. Information derived from recent advances of noninvasive magnetic resonance imaging (MRI) techniques and computational methodologies such as graph theory have allowed researchers to explore the patterns of structural and functional connectivity of healthy and diseased brains in vivo. In this article, we summarize the recent advances made in the studies of both structural (gray matter morphology and white matter fibers) and functional (synchronized neural activity) brain networks based on human MRI data pertaining to neuropsychiatric disorders. These studies bring a systems-level perspective to the alterations of the topological organization of complex brain networks and the underlying pathophysiological mechanisms. Specifically, noninvasive imaging of structural and functional brain networks and follow-up graph-theoretical analyses demonstrate the potential to establish systems-level biomarkers for clinical diagnosis, progression monitoring, and treatment effects evaluation for neuropsychiatric disorders.

Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis

Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small-world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age-matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self-referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD.

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer's patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.

Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer's disease: a resting-state fMRI study

Recent studies have shown that cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with losses of small-world attributes. However, few studies have investigated the characteristics of the whole brain networks in individuals with mild cognitive impairment (MCI). In this functional magnetic resonance imaging (fMRI) study, we investigated the topological properties of the whole brain networks in 18 AD patients, 16 MCI patients, and 18 age-matched healthy subjects. Among the three groups, AD patients showed the longest characteristic path lengths and the largest clustering coefficients, while the small-world measures of MCI networks exhibited intermediate values. The finding was not surprising, given that MCI is considered to be the prodromal stage of AD. Compared with normal controls, MCI patients showed decreased nodal centrality mainly in the medial temporal lobe as well as increased nodal centrality in the occipital regions. In addition, we detected increased nodal centrality in the medial temporal lobe and frontal gyrus, and decreased nodal centrality mainly in the amygdala in MCI patients compared with AD patients. The results suggested a widespread rewiring in AD and MCI patients. These findings concerning AD and MCI may be an integrated reflection of reorganization of the brain networks accompanied with the cognitive decline that may lead to AD.

Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study

The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.

Using cognitive decline in novel trial designs for primary prevention and early disease-modifying therapy trials of Alzheimer's disease

BACKGROUND

Ideally putative disease-modifying therapies for Alzheimer's disease (AD) should be tested in patients who have minimal morbidity. Current barriers to such trials in early disease include the lack of disease-specific early biomarkers, insensitivity of quantitative cognitive outcome measures, and expensive trial designs requiring large sample sizes and long duration. This paper describes principles and progress towards a novel trial design that overcomes these problems, utilizing wide-scale cognitive performance screening to define pre-trial cognitive decline trajectories which can serve as trial outcome measures to assess AD disease-modifying efficacy.

METHODS

Theoretical principles important for the detection of intra-individual cognitive decline and a practical example are described.

RESULTS

Serial evaluations of community-based volunteers demonstrate how a screening tool method to detect subtle cognitive decline can predict in vivo amyloid pathology as a trigger for etiological evaluation. Trajectories of decline appear consistent over at least two years, suggesting they could be used as a trial inclusion criterion and ameliorable outcome measure together with other AD biomarkers. Informative trial durations could be 6-12 months, or extend to incorporate staggered random withdrawal or start designs, with as few as 20 individuals per treatment arm.

CONCLUSIONS

This trial methodology offers significant advantages over current AD trial designs, including treatment at earlier stages of disease, shorter trial duration, obviation of informed consent difficulties, smaller sample sizes, reduced cost and--given adequate screening programs--sufficient subjects for multiple simultaneous trials. Importantly, it allows the rapid evaluation of putative treatments that may only be efficacious in pre-dementia states.

Latrepirdine increases cerebral glucose utilization in aged mice as measured by [18F]-fluorodeoxyglucose positron emission tomography

Latrepirdine is hypothesized to exert a unique mechanism of action involving stabilization of mitochondria that may have utility in treating Alzheimer's disease. However, the ability of latrepirdine to improve cognition in Alzheimer's disease (AD) is controversial due to a discrepancy between the positive signal reported in the multi-site phase II clinical trial where latrepirdine met all primary and secondary endpoints [Doody et al. (2008) Lancet 372:207-215], and the subsequent null effect observed in a multicenter, phase III trial. While dysfunction of mitochondria and abnormal energy metabolism has been linked to AD pathology, no studies have been reported that investigate latrepirdine's effect on cerebral glucose utilization (CGU). Glucose metabolism, following acute latrepirdine administration, can be used to help dose selection in Phase I dose-ranging studies. The aim of the current study was to assess changes in CGU in young and aged mice in vivo using [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) after acute treatment with latrepirdine. Two ages of B6SJLF2 mice (5 and 20 months old) were tested. Three test-retest FDG-PET baseline scans were assessed across all subjects. As CGU was heterogeneous in aged mice, compared to young mice, aged subjects were rank ordered and then counterbalanced into two CGU homogenous groups. In Studies 1 and 2, latrepirdine (1.0 mg/kg) significantly enhanced CGU in aged mice. In contrast, Study 3 revealed that latrepirdine did not modulate CGU in young mice. Monitoring changes in CGU in response to acute drug administration may represent an imaging biomarker for dose selection in AD. Further studies that would establish the translation from mice to non-human primates to humans need to be investigated to confirm the utility of FDG-PET in dose-selection for mitochondrial modulators.

Quantitating severity and progression in primary progressive aphasia

Primary progressive aphasia (PPA) is an insidiously progressive clinical syndrome that includes at its core an impairment in language. From a clinical perspective, there are a variety of diagnostic challenges; international consensus has only recently been reached on the nomenclature for specific clinical subtypes. There are at present no established treatments, and efforts to develop treatments have been hampered by the lack of standardized methods to monitor progression of the illness. This is further complicated by the multiplicity of underlying neuropathologies. Although measures developed from work with stroke aphasia and from work with disorders such as Alzheimer's disease and frontotemporal dementia have provided a valuable foundation for monitoring progression, PPA presents unique challenges to clinicians aiming to quantify impairments for the purposes of full characterization and monitoring, and ultimately with the goal of designing clinical trials of interventions to make a meaningful difference in patients' lives. In this review, I will summarize the main points made in my presentation at the 2010 International Conference on Frontotemporal Dementia, expand from there to summarize our current approach to monitoring progression of PPA, and finally will outline some ideas about goals for the development of better tools for this purpose.

Biomarkers in Alzheimer's disease drug development

Developing new therapies for Alzheimer's disease (AD) is critically important to avoid the impending public health disaster imposed by this common disorder. Means must be found to prevent, delay the onset, or slow the progression of AD. These goals will be achieved by identifying disease-modifying therapies and testing them in clinical trials. Biomarkers play an increasingly important role in AD drug development. In preclinical testing, they assist in decisions to develop an agent. Biomarkers in phase I provide insights into toxic responses and drug metabolism and in Phase II proof-of-concept trials they facilitate go/no-go decisions and dose finding. Biomarkers can play a role in identifying presymptomatic patients or specific patient subgroups. They can provide evidence of target engagement before clinical changes can be expected. Brain imaging can serve as a primary outcome in Phase II trials and as a key secondary outcome in Phase III trials. Magnetic resonance imaging is currently best positioned for use in large multicenter clinical trials. Cerebrospinal fluid (CSF) measures of amyloid beta protein (Aβ), tau protein, and hyperphosphorylated tau (p-tau) protein are sensitive and specific to the diagnosis of AD and may serve as inclusion criteria and possibly as outcomes in clinical trials targeting relevant pathways. Plasma measures of Aβ are of limited diagnostic value but may provide important information as a measure of treatment response. A wide variety of measures of detectable products of cellular processes are being developed as possible biomarkers accessible in the cerebrospinal fluid and plasma or serum. Surrogate markers that can function as outcomes in pivotal trials and reliably predict clinical outcomes are needed to facilitate primary prevention trials of asymptomatic persons where clinical measures may be of limited value. Fit-for-purpose biomarkers are increasingly available to guide AD drug development decisions.

Quantization and analysis of hippocampal morphometric changes due to dementia of Alzheimer type using metric distances based on large deformation diffeomorphic metric mapping

The metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm is used to quantize changes in morphometry of brain structures due to neuropsychiatric diseases. For illustrative purposes we consider changes in hippocampal morphometry (shape and size) due to very mild dementia of the Alzheimer type (DAT). LDDMM, which was previously used to calculate dense one-to-one correspondence vector fields between hippocampal shapes, measures the morphometric differences with respect to a template hippocampus by assigning metric distances on the space of anatomical images thereby allowing for direct comparison of morphometric differences. We characterize what information the metric distances provide in terms of size and shape given the hippocampal, brain and intracranial volumes. We demonstrate that metric distance is a measure of morphometry (i.e., shape and size) but mostly a measure of shape, while volume is mostly a measure of size. Moreover, we show how metric distances can be used in cross-sectional, longitudinal analysis, as well as left-right asymmetry comparisons, and provide how the metric distances can serve as a discriminative tool using logistic regression. Thus, we show that metric distances with respect to a template computed via LDDMM can be a powerful tool in detecting differences in shape.

Monitoring progression of primary progressive aphasia: current approaches and future directions

SUMMARY Primary progressive aphasia (PPA) is a gradually progressive syndrome that robs patients of the ability to communicate. There are a variety of diagnostic challenges; international consensus has only recently been reached on the nomenclature for specific subtypes and there are a variety of underlying neurodegenerative pathologies. There are at present no established treatments, and efforts to develop treatments have been hampered by the lack of standardized methods to monitor progression of the illness. Although measures developed from work with stroke aphasia and with disorders such as Alzheimer’s disease have provided a valuable foundation for monitoring progression, PPA presents unique challenges to clinicians aiming to counsel patients and families on clinical status and prognosis, and to experts aiming to design clinical trials of potential interventions. Here we review some of the issues facing the field of PPA clinical research, summarize our current approach to monitoring progression of PPA and outline some ideas regarding goals for the development of better tools for this purpose.