Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects

A comprehensive investigation of the associations between tensor-based morphometry indices and executive functions, memory, language, and visuospatial abilities in patients in the Alzheimer's disease continuum

OBJECTIVES

Based on the literature, tensor-based morphometry (TBM) parameters were related to neurocognitive functions such as memory, learning, language ability, and executive functions. The present study aims to evaluate the associations between TBM indices with executive functions, memory, language, and visuospatial abilities and the value of TBM in the clinical diagnosis of Alzheimer's disease (AD) among individuals with Alzheimer's disease continuum and mild cognitive impairment (MCI) from Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHODS

The authors used ADNI-memory (ADNI-MEM), ADNI-executive functions (ADNI-EF), ADNI-language (ADNI-LAN), and ADNI-visuospatial (ADNI-VS) composite scores. TBM parameters, including measure 1, which represents average within a statistically defined region-of-interest inside the temporal lobes and measure 2 which indicates average within an anatomically defined region-of-interest including bilateral temporal lobes were utilized in the current study. Statistical analysis was performed using IBM SPSS Statistics version 26, and Pearson's correlation, Bonferroni's correction, and multiple linear regression were utilized for data analysis. P <0.05 was considered statistically significant.

RESULTS

After screening 800 participants, 270 (151 men, 119 women) were selected for a study with TBM scores and cognition-related assessments at 6, 12, and 24 months. Groups included healthy controls (n=53), MCI (n=158), and Alzheimer's Disease (AD) (n=59). TBM indices correlated with cognitive scores in MCI and AD groups but not healthy controls. Changes in TBM indices and cognitive scores were significantly correlated in MCI and AD groups over 24 months. TBM indices were weak predictors of cognitive decline at all time points.

CONCLUSIONS

TBM can help physicians diagnose MCI and AD early. However, TBM could not strongly predict cognitive functions decline at all time points.

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation

This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provides useful insights and establishes connections between the methods, thereby facilitating a profound understanding of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images using traditional NIREP and Learn2Reg OASIS evaluation protocols with a focus on fairness, to establish equitable benchmarks and facilitate informed comparisons. Through a comprehensive analysis of the results, we address key questions, including the intricate relationship between accuracy and transformation quality in performance, the disentanglement of the influence of registration ingredients on performance, and the determination of benchmark methods and baselines. We offer valuable insights into the strengths and limitations of both traditional and deep-learning methods, shedding light on their comparative performance and guiding future advancements in the field.

Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

Brain Healthcare Quotient as a Tool for Standardized Approach in Brain Healthcare Interventions

In addressing the challenge of assessing healthy brain aging across diverse interventions, this study introduces the use of MRI-derived Brain Healthcare Quotients (BHQ) for comprehensive evaluation. We analyzed BHQ changes in 319 participants aged 24-69, who were allocated into dietary (collagen peptide, euglena, matcha, isohumulone, xanthophyll) and physical activity (hand massage with lavender oil, handwriting, office stretching, pink lens, clinical art) groups, alongside a control group, over a month. These interventions were specifically chosen to test the efficacy of varying health strategies on brain health, measured through BHQ indices: GM-BHQ for gray matter volume, and FA-BHQ for white matter integrity. Notably, significant improvements in FA-BHQ were observed in the collagen peptide group, with marginal increases in the hand massage and office stretching groups. These findings highlight BHQ's potential as a sensitive tool for detecting brain health changes, offering evidence that low-intensity, easily implemented interventions can have beneficial effects on brain health. Moreover, BHQ allows for the systematic evaluation of such interventions using standard statistical approaches, suggesting its value in future brain healthcare research.

Normal-Tension Glaucoma and Potential Clinical Links to Alzheimer's Disease

Glaucoma is a group of optic neuropathies and the world's leading cause of irreversible blindness. Normal-tension glaucoma (NTG) is a subtype of glaucoma that is characterized by a typical pattern of peripheral retinal loss, in which the patient's intraocular pressure (IOP) is considered within the normal range (<21 mmHg). Currently, the only targetable risk factor for glaucoma is lowering IOP, and patients with NTG continue to experience visual field loss after IOP-lowering treatments. This demonstrates the need for a better understanding of the pathogenesis of NTG and underlying mechanisms leading to neurodegeneration. Recent studies have found significant connections between NTG and cerebral manifestations, suggesting NTG as a neurodegenerative disease beyond the eye. Gaining a better understanding of NTG can potentially provide new Alzheimer's Disease diagnostics capabilities. This review identifies the epidemiology, current biomarkers, altered fluid dynamics, and cerebral and ocular manifestations to examine connections and discrepancies between the mechanisms of NTG and Alzheimer's Disease.

Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification

Background

The willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty.

Purpose

In this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different networks similar to the original one at each forward pass.

Methods

We combine our method with a tunable rejection-based approach that employs only the fraction of the data, i.e., the share that the model can classify with an uncertainty below a user-set threshold. We test our model in a large cohort of brain images from patients with Alzheimer's disease and healthy controls, discriminating the former and latter classes based on morphometric images exclusively.

Results

We demonstrate how combining estimated uncertainty with a rejection-based approach increases classification accuracy from 0.86 to 0.95 while retaining 75% of the test set. In addition, the model can select the cases to be recommended for, e.g., expert human evaluation due to excessive uncertainty. Importantly, our framework circumvents additional workload during the training phase by using our network "turned into Bayesian" to implicitly investigate the loss landscape in the neighborhood of each test sample in order to determine the reliability of the predictions.

Conclusion

We believe that being able to estimate the uncertainty of a prediction, along with tools that can modulate the behavior of the network to a degree of confidence that the user is informed about (and comfortable with), can represent a crucial step in the direction of user compliance and easier integration of deep learning tools into everyday tasks currently performed by human operators.

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization

Background

Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy.

Objective

Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker.

Methods

We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2.

Results

The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy.

Conclusions

The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease

Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature.For this reason, we aimed at providing a comprehensive study of RSF explainability with SHapley Additive exPlanations (SHAP) on biomarkers of stable and progressive patients (sMCI and pMCI) from Alzheimer's Disease Neuroimaging Initiative. We evaluated three global explanations-RSF feature importance, permutation importance and SHAP importance-and we quantitatively compared them with Rank-Biased Overlap (RBO). Moreover, we assessed whether multicollinearity among variables may perturb SHAP outcome. Lastly, we stratified pMCI test patients in high, medium and low risk grade, to investigate individual SHAP explanation of one pMCI patient per risk group.We confirmed that RSF had higher accuracy (0.890) than CPH (0.819), and its stability and robustness was demonstrated by high overlap (RBO > 90%) between feature rankings within first eight features. SHAP local explanations with and without correlated variables had no substantial difference, showing that multicollinearity did not alter the model. FDG, ABETA42 and HCI were the first important features in global explanations, with the highest contribution also in local explanation. FAQ, mPACCdigit, mPACCtrailsB and RAVLT immediate had the highest influence among all clinical and neuropsychological assessments in increasing progression risk, as particularly evident in pMCI patients' individual explanation. In conclusion, our findings suggest that RSF represents a useful tool to support clinicians in estimating conversion-to-AD risk and that SHAP explainer boosts its clinical utility with intelligible and interpretable individual outcomes that highlights key features associated with AD prognosis.

Effect of Comorbidities Features in Machine Learning Models for Survival Analysis to Predict Prodromal Alzheimer's Disease

Alzheimer's Disease (AD) is the most common form of dementia, specifically a progressive degenerative disorder affecting 47 million people worldwide and is only expected to grow in the elderly population. The detection of AD in its early stages is crucial to allow early intervention aiding in the prevention or slowing down of the disease. The effect of using comorbidity features in machine learning models to predict the time until a patient develops a prodrome was observed. In this study, we used Alzheimer's Disease Neuroimaging Initiative (ADNI) high-dimensional clinical data to compare the performance of six machine learning algorithms for survival analysis, combined with six feature selection methods trained on two settings: with and without comorbidities features. Our ridge model combined with permutation feature selection achieves maximum performance of 0.90 when using comorbidity features with the concordance index as a performance indicator. This demonstrated that incorporating comorbidities into the feature set enhances the performance of survival analysis for Alzheimer's disease. There is potential to identify risk factors (coronary artery disease) from comorbidities which could guide preventative care based on medical history.

Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI

Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.

Understanding the relationships between physiological and psychosocial stress, cortisol and cognition

Stress is viewed as a state of real or perceived threat to homeostasis, the management of which involves the endocrine, nervous, and immune systems. These systems work independently and interactively as part of the stress response. The scientific stress literature, which spans both animal and human studies, contains heterogeneous findings about the effects of stress on the brain and the body. This review seeks to summarise and integrate literature on the relationships between these systems, examining particularly the roles of physiological and psychosocial stress, the stress hormone cortisol, as controlled by the hypothalamic-pituitary-adrenal (HPA) axis, and the effects of stress on cognitive functioning. Health conditions related to impaired HPA axis functioning and their associated neuropsychiatric symptoms will also be considered. Lastly, this review will provide suggestions of clinical applicability for endocrinologists who are uniquely placed to measure outcomes related to endocrine, nervous and immune system functioning and identify areas of intervention.

Optimal transport features for morphometric population analysis

Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.

Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.

Validation of a rapid remote digital test for impaired cognition using clinical dementia rating and mini-mental state examination: An observational research study

Background

The Clinical Dementia Rating (CDR) and Mini-Mental State Examination (MMSE) are useful screening tools for mild cognitive impairment (MCI). However, these tests require qualified in-person supervision and the CDR can take up to 60 min to complete. We developed a digital cognitive screening test (M-CogScore) that can be completed remotely in under 5 min without supervision. We set out to validate M-CogScore in head-to-head comparisons with CDR and MMSE.

Methods

To ascertain the validity of the M-CogScore, we enrolled participants as healthy controls or impaired cognition, matched for age, sex, and education. Participants completed the 30-item paper MMSE Second Edition Standard Version (MMSE-2), paper CDR, and smartphone-based M-CogScore. The digital M-CogScore test is based on time-normalised scores from smartphone-adapted Stroop (M-Stroop), digit-symbols (M-Symbols), and delayed recall tests (M-Memory). We used Spearman's correlation coefficient to determine the convergent validity between M-CogScore and the 30-item MMSE-2, and non-parametric tests to determine its discriminative validity with a CDR label of normal (CDR 0) or impaired cognition (CDR 0.5 or 1). M-CogScore was further compared to MMSE-2 using area under the receiver operating characteristic curves (AUC) with corresponding optimal cut-offs.

Results

72 participants completed all three tests. The M-CogScore correlated with both MMSE-2 (rho = 0.54, p < 0.0001) and impaired cognition on CDR (Mann Whitney U = 187, p < 0.001). M-CogScore achieved an AUC of 0.85 (95% bootstrapped CI [0.80, 0.91]), when differentiating between normal and impaired cognition, compared to an AUC of 0.78 [0.72, 0.84] for MMSE-2 (p = 0.21).

Conclusion

Digital screening tests such as M-CogScore are desirable to aid in rapid and remote clinical cognitive evaluations. M-CogScore was significantly correlated with established cognitive tests, including CDR and MMSE-2. M-CogScore can be taken remotely without supervision, is automatically scored, has less of a ceiling effect than the MMSE-2, and takes significantly less time to complete.

Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI

BACKGROUND

Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM.

OBJECTIVE

In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls.

METHODS

By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons.

RESULTS

Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus.

CONCLUSION

This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.

Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement

Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.

Long-term structural brain changes in adult rats after mild ischaemic stroke

Preclinical studies of remote degeneration have largely focused on brain changes over the first few days or weeks after stroke. Accumulating evidence suggests that neurodegeneration occurs in other brain regions remote to the site of infarction for months and even years following ischaemic stroke. Brain atrophy appears to be driven by both axonal degeneration and widespread brain inflammation. The evolution and duration of these changes are increasingly being described in human studies, using advanced brain imaging techniques. Here, we sought to investigate long-term structural brain changes in a model of mild focal ischaemic stroke following injection of endothlin-1 in adult Long–Evans rats (n = 14) compared with sham animals (n = 10), over a clinically relevant time-frame of 48 weeks. Serial structural and diffusion-weighted MRI data were used to assess dynamic volume and white matter trajectories. We observed dynamic regional brain volume changes over the 48 weeks, reflecting both normal changes with age in sham animals and neurodegeneration in regions connected to the infarct following ischaemia. Ipsilesional cortical volume loss peaked at 24 weeks but was less prominent at 36 and 48 weeks. We found significantly reduced fractional anisotropy in both ipsi- and contralesional motor cortex and cingulum bundle regions of infarcted rats (P < 0.05) from 4 to 36 weeks, suggesting ongoing white matter degeneration in tracts connected to but distant from the stroke. We conclude that there is evidence of significant cortical atrophy and white matter degeneration up to 48 weeks following infarct, consistent with enduring, pervasive stroke-related degeneration.

Magnetic resonance imaging measures of brain volumes across the EXPEDITION trials in mild and moderate Alzheimer's disease dementia

Introduction

Solanezumab is a monoclonal antibody that preferentially binds soluble amyloid beta and promotes its clearance from the brain. The aim of this post hoc analysis was to assess the effect of low-dose solanezumab (400 mg) on global brain volume measures in patients with mild or moderate Alzheimer's disease (AD) dementia quantified using volumetric magnetic resonance imaging (vMRI) data from the EXPEDITION clinical trial program.

Methods

Patients with mild or moderate AD (EXPEDITION and EXPEDITION2) and mild AD (EXPEDITION3), were treated with either placebo or solanezumab (400 mg) every 4 weeks (Q4W) for 76 weeks. vMRI scans were acquired at baseline and at 80 weeks from 427 MRI facilities using a standardized imaging protocol. Whole brain volume (WBV) and ventricle volume (VV) changes were estimated at 80 weeks using either boundary shift integral (EXPEDITION and EXPEDITION2) or tensor-based morphometry (EXPEDITION3).

Results

The pooled cohort used for this study consisted of participants with vMRI at baseline and week 80 across the three trials. Analyzed patient subgroups comprised full patient cohort (N = 2933), apolipoprotein E (APOE) ε4+ carriers (N = 1835), and patients with mild (N = 2497) or moderate AD dementia (N = 428). No significant effect (all P-values ≥.05) of treatment was observed in the pooled sample, individual trials, or subgroups of patients with mild or moderate AD or APOE ε4 carriers, in either WBV or VV change.

Discussion

Analysis of patients with mild or moderate AD dementia from baseline to 80 weeks using vMRI measures of WBV and VV changes suggested that low-dose solanezumab was not linked to changes in volumes at 80 weeks. Analysis of the pooled cohort did not demonstrate an effect on brain volumes with treatment. Evaluation of a higher dose of solanezumab in the preclinical stage of AD is currently being undertaken.

Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's Disease Patients

Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.

Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework

This work proposes a unifying framework for extending PDE-constrained Large Deformation Diffeomorphic Metric Mapping (PDE-LDDMM) with the sum of squared differences (SSD) to PDE-LDDMM with different image similarity metrics. We focused on the two best-performing variants of PDE-LDDMM with the spatial and band-limited parameterizations of diffeomorphisms. We derived the equations for gradient-descent and Gauss-Newton-Krylov (GNK) optimization with Normalized Cross-Correlation (NCC), its local version (lNCC), Normalized Gradient Fields (NGFs), and Mutual Information (MI). PDE-LDDMM with GNK was successfully implemented for NCC and lNCC, substantially improving the registration results of SSD. For these metrics, GNK optimization outperformed gradient-descent. However, for NGFs, GNK optimization was not able to overpass the performance of gradient-descent. For MI, GNK optimization involved the product of huge dense matrices, requesting an unaffordable memory load. The extensive evaluation reported the band-limited version of PDE-LDDMM based on the deformation state equation with NCC and lNCC image similarities among the best performing PDE-LDDMM methods. In comparison with benchmark deep learning-based methods, our proposal reached or surpassed the accuracy of the best-performing models. In NIREP16, several configurations of PDE-LDDMM outperformed ANTS-lNCC, the best benchmark method. Although NGFs and MI usually underperformed the other metrics in our evaluation, these metrics showed potentially competitive results in a multimodal deformable experiment. We believe that our proposed image similarity extension over PDE-LDDMM will promote the use of physically meaningful diffeomorphisms in a wide variety of clinical applications depending on deformable image registration.

Explainable AI toward understanding the performance of the top three TADPOLE Challenge methods in the forecast of Alzheimer’s disease diagnosis

The Alzheimer′s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge is the most comprehensive challenge to date with regard to the number of subjects, considered features, and challenge participants. The initial objective of TADPOLE was the identification of the most predictive data, features, and methods for the progression of subjects at risk of developing Alzheimer′s. The challenge was successful in recognizing tree-based ensemble methods such as gradient boosting and random forest as the best methods for the prognosis of the clinical status in Alzheimer’s disease (AD). However, the challenge outcome was limited to which combination of data processing and methods exhibits the best accuracy; hence, it is difficult to determine the contribution of the methods to the accuracy. The quantification of feature importance was globally approached by all the challenge participant methods. In addition, TADPOLE provided general answers that focused on improving performance while ignoring important issues such as interpretability. The purpose of this study is to intensively explore the models of the top three TADPOLE Challenge methods in a common framework for fair comparison. In addition, for these models, the most meaningful features for the prognosis of the clinical status of AD are studied and the contribution of each feature to the accuracy of the methods is quantified. We provide plausible explanations as to why the methods achieve such accuracy, and we investigate whether the methods use information coherent with clinical knowledge. Finally, we approach these issues through the analysis of SHapley Additive exPlanations (SHAP) values, a technique that has recently attracted increasing attention in the field of explainable artificial intelligence (XAI).

Increased brain tissue sodium concentration in Friedreich ataxia: A multimodal MR imaging study

In patients with Friedreich ataxia, structural MRI is typically used to detect abnormalities primarily in the brainstem, cerebellum, and spinal cord. The aim of the present study was to additionally investigate possible metabolic changes in Friedreich ataxia using in vivo sodium MRI that may precede macroanatomical alterations, and to explore potential associations with clinical parameters of disease progression. Tissue sodium concentration across the whole brain was estimated from sodium MRI maps acquired at 3 T and compared between 24 patients with Friedreich ataxia (21-57 years old, 13 females) and 23 controls (21-60 years old, 12 females). Tensor-based morphometry was used to assess volumetric changes. Total sodium concentrations and volumetric data in brainstem and cerebellum were correlated with clinical parameters, such as severity of ataxia, activity of daily living and disability stage, age, age at onset, and disease duration. Compared to controls, patients showed reduced brain volume in the right cerebellar lobules I-V (difference in means: -0.039% of total intracranial volume [TICV]; Cohen's d = 0.83), cerebellar white matter (WM) (-0.105%TICV; d = 1.16), and brainstem (-0.167%TICV; d = 1.22), including pons (-0.102%TICV; d = 1.00), medulla (-0.036%TICV; d = 1.72), and midbrain (-0.028%TICV; d = 1.05). Increased sodium concentration was additionally detected in the total cerebellum (difference in means: 2.865 mmol; d = 0.68), and in several subregions with highest effect sizes in left (5.284 mmol; d = 1.01) and right cerebellar lobules I-V (5.456 mmol; d = 1.00), followed by increases in the vermis (4.261 mmol; d = 0.72), and in left (2.988 mmol; d = 0.67) and right lobules VI-VII (2.816 mmol; d = 0.68). In addition, sodium increases were also detected in all brainstem areas (3.807 mmol; d = 0.71 to 5.42 mmol; d = 1.19). After controlling for age, elevated total sodium concentrations in right cerebellar lobules IV were associated with younger age at onset (r = -0.43) and accordingly with longer disease duration in patients (r = 0.43). Our findings support the potential of in vivo sodium MRI to detect metabolic changes of increased total sodium concentration in the cerebellum and brainstem, the key regions in Friedreich ataxia. In addition to structural changes, sodium changes were present in cerebellar hemispheres and vermis without concomitant significant atrophy. Given the association with age at disease onset or disease duration, metabolic changes should be further investigated longitudinally and in larger cohorts of early disease stages to determine the usefulness of sodium MRI as a biomarker for early neuropathological changes in Friedreich ataxia and efficacy measure for future clinical trials.

Associations of deformation-based brain morphometry with cognitive level and decline within older Blacks without dementia

Blacks are at higher risk of developing cognitive impairment with age than non-Hispanic Whites, yet most brain morphometry and cognition research is performed with White samples or with mixed samples that control for race or compare across racial groups. A deeper understanding of the within-group variability in associations between brain structure and cognitive decline in Blacks is critically important for designing appropriate outcomes for clinical trials, predicting adverse outcomes, and developing interventions to preserve cognitive function, but no studies have examined these associations longitudinally within Blacks. We performed deformation-based morphometry in 376 older Black participants without dementia and examined associations of deformation-based morphometry with cognitive level and decline for global cognition and five cognitive domains. After correcting for widespread age-associated effects, there remained regions with less tissue and more cerebrospinal fluid associated with level and rate of decline in global cognition, memory, and perceptual speed. Further study is needed to examine the moderators of these associations, identify adverse outcomes predicted by brain morphometry, and deepen knowledge of underlying biological mechanisms.

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease

BACKGROUND

The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT).

OBJECTIVE

The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT.

METHODS

We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future.

RESULTS

Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups.

CONCLUSION

MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.

The effect of a post-scan processing denoising system on image quality and morphometric analysis

PURPOSE

MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelligent Quick Magnetic Resonance (iQMR), on MR image quality and brain morphometric analysis.

METHODS

We used 1.5T MP-RAGE MR images acquired from the Alzheimer's Disease Neuroimaging Initiative 1 database. The images of 21 subjects were used for cross-sectional analysis and 15 for longitudinal analysis. In the longitudinal analysis, two timepoints over a 2-year interval were used. Each subject was scanned twice at each timepoint. MR images processed with and without the denoising system were compared both visually and objectively using FreeSurfer cortical thickness analysis.

RESULTS

The denoising system reduced the noise with good white-gray matter contrast (noise: p < 0.001; contrast: p = 0.49). The mean intraclass correlation coefficients (ICCs) of cortical thickness were slightly better in the images processed with the denoising system (0.739/0.859/0.883; Gaussian smoothing kernel of full width at half maximum = 0/10/20) compared with the unprocessed images (0.718/0.854/0.880). In the longitudinal analysis, the mean ICCs of symmetrized percent change improved in images processed with the denoising system (0.202/0.349/0.431) compared with the unprocessed images (0.167/0.325/0.404). In addition, the detectability of significant cortical thickness atrophy improved with denoising.

CONCLUSION

We confirm that the AI-based denoising system could effectively reduce the noise while retaining the contrast. We also confirm the improvement of the reliability and detectability of brain morphometric analysis with the denoising system.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

Structural assessment of thalamus morphology in brain disorders: A review and recommendation of thalamic nucleus segmentation and shape analysis

The thalamus is a central brain structure crucially involved in cognitive, emotional, sensory, and motor functions and is often reported to be involved in the pathophysiology of neurological and psychiatric disorders. The functional subdivision of the thalamus warrants morphological investigation on the level of individual subnuclei. In addition to volumetric measures, the investigation of other morphological features may give additional insights into thalamic morphology. For instance, shape features offer a higher spatial resolution by revealing small, regional differences that are left undetected in volumetric analyses. In this review, we discuss the benefits and limitations of recent advances in neuroimaging techniques to investigate thalamic morphology in vivo, leading to our proposed methodology. This methodology consists of available pipelines for volume and shape analysis, focussing on the morphological features of volume, thickness, and surface area. We demonstrate this combined approach in a Parkinson's disease cohort to illustrate their complementarity. Considering our findings, we recommend a combined methodology as it allows for more sensitive investigation of thalamic morphology in clinical populations.

The Relations Between Physical Activity Level, Executive Function, and White Matter Microstructure in Older Adults

The population of older adults is increasing, indicating a need to examine factors that may prevent or mitigate age-related cognitive decline. The current study examined whether microstructural white matter characteristics mediated the relation between physical activity and executive function in older adults without any self-reported psychiatric and neurological disorders or cognitive impairment (N = 43, mean age = 73 y). Physical activity was measured by average intensity and number of steps via accelerometry. Diffusion tensor imaging was used to examine microstructural white matter characteristics, and neuropsychological testing was used to examine executive functioning. Parallel mediation models were analyzed using microstructural white matter regions of interest as mediators of the association between physical activity and executive function. Results indicated that average steps was significantly related to executive function (β = 0.0003, t = 2.829, P = .007), while moderate to vigorous physical activity was not (β = 0.0007, t = 1.772, P = .08). White matter metrics did not mediate any associations. This suggests that microstructural white matter characteristics alone may not be the mechanism by which physical activity impacts executive function in aging.

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR.

Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics

Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.

Convergent patterns of structural brain changes in rapid eye movement sleep behavior disorder and Parkinson's disease on behalf of the German rapid eye movement sleep behavior disorder study group

STUDY OBJECTIVES

Rapid eye movement sleep behavior disorder (RBD) is considered a prodromal state of Parkinson's disease (PD). We aimed to characterize patterns of structural brain changes in RBD and PD patients using multimodal MRI.

METHODS

A total of 30 patients with isolated RBD, 29 patients with PD, and 56 age-matched healthy controls (HC) underwent MRI at 3T, including tensor-based morphometry, diffusion tensor imaging, and assessment of cortical thickness.

RESULTS

RBD individuals showed increased volume of the right caudate nucleus compared with HC, and higher cerebellar volume compared with both PD subjects and HC. Similar to PD subjects, RBD patients displayed increased fractional anisotropy (FA) in the corticospinal tracts, several tracts mainly related to non-motor function, and reduced FA of the corpus callosum compared with HC. Further, RBD subjects showed higher FA in the cerebellar peduncles and brainstem compared with both, PD patients and HC. PD individuals exhibited lower than normal volume in the basal ganglia, midbrain, pedunculopontine nuclei, and cerebellum. In contrast, volume in PD subjects was increased in the thalamus compared with both HC and RBD subjects.

CONCLUSIONS

We found convergent patterns of structural brain alterations in RBD and PD patients compared with HC. The changes observed suggest a co-occurrence of neurodegeneration and compensatory mechanisms that fail with emerging PD pathology. Our findings strengthen the hypothesis of RBD and PD constituting a continuous disease spectrum.

Generalizing Longitudinal Age Effects on Brain Structure - A Two-Study Comparison Approach

Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults (n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present.

Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death.

INTRODUCTION

In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD.

AIM

In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed.

FINDINGS

Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure.

CONCLUSIONS

Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.

Use of a Sparse-Response Deep Belief Network and Extreme Learning Machine to Discriminate Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls Based on Amyloid PET/MRI Images

In recent years, interest has grown in using computer-aided diagnosis (CAD) for Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). However, existing CAD technologies often overfit data and have poor generalizability. In this study, we proposed a sparse-response deep belief network (SR-DBN) model based on rate distortion (RD) theory and an extreme learning machine (ELM) model to distinguish AD, MCI, and normal controls (NC). We used [¹⁸F]-AV45 positron emission computed tomography (PET) and magnetic resonance imaging (MRI) images from 340 subjects enrolled in the ADNI database, including 116 AD, 82 MCI, and 142 NC subjects. The model was evaluated using five-fold cross-validation. In the whole model, fast principal component analysis (PCA) served as a dimension reduction algorithm. An SR-DBN extracted features from the images, and an ELM obtained the classification. Furthermore, to evaluate the effectiveness of our method, we performed comparative trials. In contrast experiment 1, the ELM was replaced by a support vector machine (SVM). Contrast experiment 2 adopted DBN without sparsity. Contrast experiment 3 consisted of fast PCA and an ELM. Contrast experiment 4 used a classic convolutional neural network (CNN) to classify AD. Accuracy, sensitivity, specificity, and area under the curve (AUC) were examined to validate the results. Our model achieved 91.68% accuracy, 95.47% sensitivity, 86.68% specificity, and an AUC of 0.87 separating between AD and NC groups; 87.25% accuracy, 79.74% sensitivity, 91.58% specificity, and an AUC of 0.79 separating MCI and NC groups; and 80.35% accuracy, 85.65% sensitivity, 72.98% specificity, and an AUC of 0.71 separating AD and MCI groups, which gave better classification than other models assessed.

Dysfunction in nonsense-mediated decay, protein homeostasis, mitochondrial function, and brain connectivity in ALS-FUS mice with cognitive deficits

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) represent two ends of the same disease spectrum of adult-onset neurodegenerative diseases that affect the motor and cognitive functions, respectively. Multiple common genetic loci such as fused in sarcoma (FUS) have been identified to play a role in ALS and FTD etiology. Current studies indicate that FUS mutations incur gain-of-toxic functions to drive ALS pathogenesis. However, how the disease-linked mutations of FUS affect cognition remains elusive. Using a mouse model expressing an ALS-linked human FUS mutation (R514G-FUS) that mimics endogenous expression patterns, we found that FUS proteins showed an age-dependent accumulation of FUS proteins despite the downregulation of mouse FUS mRNA by the R514G-FUS protein during aging. Furthermore, these mice developed cognitive deficits accompanied by a reduction in spine density and long-term potentiation (LTP) within the hippocampus. At the physiological expression level, mutant FUS is distributed in the nucleus and cytosol without apparent FUS aggregates or nuclear envelope defects. Unbiased transcriptomic analysis revealed a deregulation of genes that cluster in pathways involved in nonsense-mediated decay, protein homeostasis, and mitochondrial functions. Furthermore, the use of in vivo functional imaging demonstrated widespread reduction in cortical volumes but enhanced functional connectivity between hippocampus, basal ganglia and neocortex in R514G-FUS mice. Hence, our findings suggest that disease-linked mutation in FUS may lead to changes in proteostasis and mitochondrial dysfunction that in turn affect brain structure and connectivity resulting in cognitive deficits.

Dissociating regional gray matter density and gray matter volume in autism spectrum condition

•

Local gray matter structure can be separated into density and volume.

•

Gray matter density increased and volume decreased with age in adolescence.

•

Increased gray matter density but reduced volume in girls relative to boys.

•

Autism is associated with increased gray matter volume, but not density.

•

Regions with increased volume align with prior studies in autistic youth.

Background

Despite decades of research, there is continued uncertainty regarding whether autism is associated with a specific profile of gray matter (GM) structure. This inconsistency may stem from the widespread use of voxel-based morphometry (VBM) methods that combine indices of GM density and GM volume. If GM density or volume, but not both, prove different in autism, the traditional VBM approach of combining the two indices may obscure the difference. The present study measures GM density and volume separately to examine whether autism is associated with alterations in GM volume, density, or both.

Methods

Differences in MRI-based GM density and volume were examined in 6–25 year-olds with a diagnosis of autism spectrum disorder (n = 213, 80.8% male, IQ 47–154) and a typically developing (TD) sample (n = 190, 71.6% male, IQ 67–155). High-resolution T1-weighted anatomical images were collected on a single MRI scanner. Regional density and volume were estimated via whole-brain parcellation comprised of 1625 parcels. Parcel-wise analyses were conducted using generalized additive models while controlling the false discovery rate (FDR, q < 0.05). Volume differences in the 68-region Desikan-Killiany atlas derived from Freesurfer were also examined, to establish the generalizability of findings across methods.

Results

No density differences were observed between the autistic and TD groups, either in individual parcels or whole brain mean density. Increased volume was observed in autism compared to the TD group when controlling for Age, Sex, and IQ, both at the level of the whole brain (total volume) and in 45 parcels (2.8% of total parcels). Parcels with greater volume included inferior, middle, and superior temporal gyrus, inferior and superior frontal gyrus, precuneus, and fusiform gyrus. Converging evidence from a standard Freesurfer pipeline also identified greater volume in a number of overlapping regions.

Limitations

The method for determining “density” is not a direct measure of neuronal density, and this study cannot reveal underlying cellular differences. While this study represents possibly the largest single-site sample of its kind, it is underpowered to detect very small differences.

Conclusions

These results provide compelling evidence that autism is associated with regional GM volumetric differences, which are more prominent than density differences. This underscores the importance of examining volume and density separately, and suggests that direct measures of volume (e.g. region-of-interest or tensor-based morphometry approaches) may be more sensitive to autism-relevant differences in neuroanatomy than concentration/density-based approaches.

Jacobian Mapping Reveals Converging Brain Substrates of Disruption and Repair in Response to Ethanol Exposure and Abstinence in 2 Strains of Rats

BACKGROUND

In a previous study using Jacobian mapping to evaluate the morphological effects on the brain of binge (4-day) intragastric ethanol (EtOH) on wild-type Wistar rats, we reported reversible thalamic shrinkage and lateral ventricular enlargement, but persistent superior and inferior colliculi shrinkage in response to binge EtOH treatment.

METHODS

Herein, we used similar voxel-based comparisons of Magnetic Resonance Images collected in EtOH-exposed relative to control animals to test the hypothesis that regardless of the intoxication protocol or the rat strain, the hippocampi, thalami, and colliculi would be affected.

RESULTS

Two experiments [binge (4-day) intragastric EtOH in Fisher 344 rats and chronic (1-month) vaporized EtOH in Wistar rats] showed similarly affected brain regions including retrosplenial and cingulate cortices, dorsal hippocampi, central and ventroposterior thalami, superior and inferior colliculi, periaqueductal gray, and corpus callosum. While most of these regions showed significant recovery, volumes of the colliculi and periaqueductal gray continued to show response to each proximal EtOH exposure but at diminished levels with repeated cycles.

CONCLUSIONS

Given the high metabolic rate of these enduringly affected regions, the current findings suggest that EtOH per se may affect cellular respiration leading to brain volume deficits. Further, responsivity greatly diminished likely reflecting neuroadaptation to repeated alcohol exposure. In summary, this unbiased, in vivo-based approach demonstrating convergent brain systems responsive to 2 EtOH exposure protocols in 2 rat strains highlights regions that warrant further investigation in both animal models of alcoholism and in humans with alcohol use disorder.

Registration quality filtering improves robustness of voxel-wise analyses to the choice of brain template

Motivation:

Many clinical and scientific conclusions that rely on voxel-wise analyses of neuroimaging depend on the accurate comparison of corresponding anatomical regions. Such comparisons are made possible by registration of the images of subjects of interest onto a common brain template, such as the Johns Hopkins University (JHU) template. However, current image registration algorithms are prone to errors that are distributed in a template-dependent manner. Therefore, the results of voxel-wise analyses can be sensitive to template choice. Despite this problem, the issue of appropriate template choice for voxel-wise analyses is not generally addressed in contemporary neuroimaging studies, which may lead to the reporting of spurious results.

Results:

We present a novel approach to determine the suitability of a brain template for voxel-wise analysis. The approach is based on computing a “distance” between automatically-generated atlases of the subjects of interest and templates that is indicative of the extent of subject-to-template registration errors. This allows for the filtering of subjects and candidate templates based on a quantitative measure of registration quality. We benchmark our approach by evaluating alternative templates for a voxel-wise analysis that reproduces the well-known decline in fractional anisotropy (FA) with age. Our results show that filtering registrations minimizes errors and decreases the sensitivity of voxel-wise analysis to template choice. In addition to carrying important implications for future neuroimaging studies, the developed framework of template induction can be used to evaluate robustness of data analysis methods to template choice.

Reliable change in neuropsychological test scores is associated with brain atrophy in older adults

The reliable change index (RCI) is a commonly used method for interpreting change in neuropsychological test scores over time. However, the RCI is a psychometric method that, to date, has not been validated against neuroanatomical changes. Longitudinal neuroimaging and neuropsychological data from baseline and one-year follow-up visits were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The RCI was used to identify participants showing reliable decline on memory (ADNI-Mem; N = 450) and executive functioning (ADNI-EF; N = 456) factor scores. For each factor score, two groups (reliable change vs. no reliable change) were matched on potential baseline confounding variables. Longitudinal neuroanatomical data were analysed using tensor-based morphometry. Analysis revealed that reliable change on ADNI-Mem was associated with atrophy in the medial temporal cortex, limbic cortex, temporal lobe and some regions of the parietal lobe. Similar atrophy patterns were found for reliable change on ADNI-EF, except that atrophy extended to the frontal lobe and the atrophy was more extensive and of higher magnitude. The current study not only validates clinical usage of the RCI with neuroanatomical evidence of associated underlying brain change but also suggests patterns of likely brain atrophy when reliable cognitive decline is detected.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Structural and biochemical imaging reveals systemic LPS-induced changes in the rat brain

Despite mounting evidence for the role of inflammation in Major Depressive Disorder (MDD), in vivo preclinical investigations of inflammation-induced negative affect using whole brain imaging modalities are scarce, precluding a valid model within which to evaluate pharmacological interventions. Here we used an E. coli lipopolysaccharide (LPS)-based model of inflammation-induced depressive signs in rats to explore brain changes using multimodal neuroimaging methods. During the acute phase of the LPS response (2 h post injection), prior to the emergence of a task-quantifiable depressive phenotype, striatal glutamine levels and splenial, retrosplenial, and peri-callosal hippocampal cortex volumes were greater than at baseline. LPS-induced depressive behaviors observed at 24 h, however, occurred concurrently with lower than control levels of striatal glutamine and a reversibility of volume expansion (i.e., shrinkage of splenial, retrosplenial, and peri-callosal hippocampal cortex to baseline volumes). In both striatum and hippocampus at 24 h, mRNA expression in LPS relative to control animals demonstrated alterations in enzymes and transporters regulating glutamine homeostasis. Collectively, the observed behavioral, in vivo structural and metabolic, and mRNA expression alterations suggest a critical role for astrocytic regulation of inflammation-induced depressive behaviors.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

•

A completely automated surface-based ventricular morphometry system.

•

Generate a whole connected 3D ventricular shape model.

•

Test-retest the system in two independent CU subject cohorts.

•

Subregional ventricular abnormalities prior to clinically memory decline.

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

Relationship of Cerebral Blood Flow to Cognitive Function and Recovery in Early Chronic Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of morbidity worldwide, for which biomarkers are needed to better understand the underlying pathophysiology. Microvascular injury represents a subset of pathological mechanisms contributing to cognitive dysfunction after TBI, which may also impair subsequent neural repair thereby inhibiting cognitive recovery. Magnetic resonance imaging (MRI)-based measurement of cerebral blood flow (CBF) by arterial spin labeling (ASL) provides an appealing means of assessing microvascular disruption in TBI; however, the relationship between CBF alterations in the early chronic post-TBI setting and cognitive dysfunction as well as subsequent cognitive recovery remain poorly understood. Structural MRI and ASL were performed in 42 TBI subjects 3 months post-injury and 35 matched healthy controls. Neuropsychological testing was performed in each subject, as well as in a subset of TBI patients (n = 33) at 6 and/or 12 months post-injury. TBI and control subject CBF data were compared between groups in a voxel-wise fashion while controlling for the effects of structural atrophy. A region-of-interest approach was then used to compare CBF to clinical and neuropsychological measures within the TBI group in a cross-sectional fashion, as well as to the degree of subsequent cognitive recovery among subjects with follow-up testing. At 3 months post-injury, the TBI group demonstrated lower performance in each cognitive domain (p < 0.05), as well as widespread reductions in gray matter CBF independent of structural atrophy (p < 0.05). Within the TBI group, CBF was moderately correlated with injury severity (r = -0.43; p = 0.009) and executive function (r = 0.43; p = 0.01). In the longitudinal analysis, there was a positive correlation between initial CBF and processing speed recovery (r = 0.43; p = 0.015) independent of age, education level, and initial test score. Early chronic TBI is associated with widespread gray matter CBF deficits, which are correlated with injury severity and cognitive dysfunction. CBF may predict subsequent recovery in some cognitive domains.

The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study

Background

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain.

Objective

This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics.

Methods

We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature.

Results

On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation.

Conclusions

Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

[Coupled convolutional and graph network-based diagnosis of Alzheimer's disease using MRI]

OBJECTIVE

To propose a coupled convolutional and graph convolutional network (CCGCN) model for diagnosis of Alzheimer's disease (AD) and its prodromal stage.

METHODS

The disease-related brain regions generated by group-wise comparison were used as the input. The convolutional neural networks (CNNs) were used to extract disease-related features from different locations on brain magnetic resonance (MR) images. The generated features via the graph convolutional network (GCN) were processed, and graph pooling was performed to analyze the inherent relationship between the brain topology and the diagnosis task adaptively. Through ADNI dataset, we acquired the accuracy, sensitivity and specificity of the diagnosis tasks for AD and its prodromal stages, followed by an ablation study on the model structure.

RESULTS

The CCGCN model outperformed the current state-of-the-art methods and showed a classification accuracy of 92.5% for AD with a sensitivity of 88.1% and a specificity of 96.0%.

CONCLUSIONS

Based on the structural and topological features of the brain MR images, the proposed CCGCN model shows excellent performance in AD diagnosis and is expected to provide important assistance to physicians in disease diagnosis.

Automated Deformation-Based Analysis of 3D Optical Coherence Tomography in Diabetic Retinopathy

Diabetic retinopathy (DR) is a significant microvascular complication of diabetes mellitus and a leading cause of vision impairment in working age adults. Optical coherence tomography (OCT) is a routinely used clinical tool to observe retinal structural and thickness alterations in DR. Pathological changes that alter the normal anatomy of the retina, such as intraretinal edema, pose great challenges for conventional layer-based analysis of OCT images. We present an alternative approach for the automated analysis of OCT volumes in DR research based on nonlinear registration. In this paper, we first obtain an anatomically consistent volume of interest (VOI) in different OCT images via carefully designed masking and affine registration. After that, efficient B-spline transformations are computed using stochastic gradient descent optimization. Using the OCT volumes of normal controls, for which layer-based segmentation works well, we demonstrate the accuracy of our registration-based analysis in aligning layer boundaries. By nonlinearly registering the OCT volumes of DR subjects to an atlas constructed from normal controls and measuring the Jacobian determinant of the deformation, we can simultaneously visualize tissue contraction and expansion due to DR pathology. Tensor-based morphometry (TBM) can also be performed for quantitative analysis of local structural changes. In our experimental results, we apply our method to a dataset of 105 subjects and demonstrate that volumetric OCT registration and TBM analysis can successfully detect local retinal structural alterations due to DR.