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

Axonal transport impairment and its relationship with diffusion tensor imaging metrics of a murine model of p301L tau induced tauopathy

Diffusion Tensor Imaging (DTI) and Manganese Enhanced MRI (MEMRI) are noninvasive tools to characterize neural fiber microstructure and axonal transport. A combination of both may provide novel insights into the progress of neurodegeneration. To investigate the relationship of DTI and MEMRI in white matter of tauopathy, twelve optic nerves of 11-month-old p301L tau mice were imaged and finished with postmortem immunohistochemistry. MEMRI was used to quantify Mn2+ accumulation rates in the optic nerve (ON, termed ONAR) and the Superior Colliculus (SC, termed SCAR), the primary terminal site of ON in mice. We found that both ONAR and SCAR revealed a significant linear correlation with mean diffusion (mD) and radial diffusion (rD) but not with other DTI quantities. Immunohistochemistry findings showed that ONAR, mD, and rD are significantly correlated with the myelin content (Myelin Basic Protein, p < 0.05) but not with the axonal density (SMI-31), tubulin density, or tau aggregates (AT8 staining). In summary, slower axonal transport appeared to have less myelinated axons and thinner remaining axons, associated with reduced rD and mD of in vivo DTI. A combination of in vivo MEMRI and DTI can provide critical information to delineate the progress of white matter deficits in neurodegenerative diseases.

Differential and subtype-specific neuroimaging abnormalities in amnestic and nonamnestic mild cognitive impairment: A systematic review and meta-analysis

While mild cognitive impairment (MCI) has been classified into amnestic MCI (aMCI) and nonamnestic MCI (naMCI), the neuropathological bases of these two subtypes remain elusive. Here, we performed a systematic review and meta-analysis to determine the subtype specificity of neuroimaging abnormalities in MCI and to identify neural features that may differ between aMCI and naMCI. We synthesized 50 studies that used common neuroimaging modalities, including magnetic resonance imaging and positron emission tomography, to compare brain atrophy, white matter abnormalities, cortical thinning, cerebral hypometabolism, amyloid/tau deposition, or other features among aMCI, naMCI, and normal cognition. Compared with normal cognition, aMCI shows diverse neuroimaging abnormalities of large effect sizes. In contrast, naMCI exhibits restricted abnormalities of small effect sizes. Some features, including medial temporal lobe atrophy and white matter abnormalities, are shared by the two MCI subtypes. Overall, brain abnormalities are worse, if not similar, in aMCI than in naMCI. The only neuroimaging abnormality specific to aMCI is increased amyloid burden; no feature specific to naMCI was found. Taken together, our findings have elucidated the neuropathological changes that occur in aMCI and naMCI. Clarifying the neuroimaging profiles of aMCI and naMCI can improve the early identification, differentiation, and intervention of prodromal dementia.

Episodic Past, Future, and counterfactual thinking in Relapsing-Remitting Multiple sclerosis

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Performance in episodic past, future or counterfactual thinking in relapsing-remitting MS and controls was explored.

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Behavioral and diffusion weighted imaging were used to evaluate associations between white matter integrity and group differences in performance.

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Relative to controls, MS patients showed reductions in episodic details across all three simulations.

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Reduced white matter integrity in three association tracts predicted this reduction in episodic details during counterfactual simulations.

Multiple sclerosis (MS) is a progressive disease characterized by widespread white matter lesions in the brain and spinal cord. In addition to well-characterized motor deficits, MS results in cognitive impairments in several domains, notably in episodic autobiographical memory. Recent studies have also revealed that patients with MS exhibit deficits in episodic future thinking, i.e., our capacity to imagine possible events that may occur in our personal future. Both episodic memory and episodic future thinking have been shown to share cognitive and neural mechanisms with a related kind of hypothetical simulation known as episodic counterfactual thinking: our capacity to imagine alternative ways in which past personal events could have occurred but did not. However, the extent to which episodic counterfactual thinking is affected in MS is still unknown. The current study sought to explore this issue by comparing performance in mental simulation tasks involving either past, future or counterfactual thoughts in relapsing-remitting MS. Diffusion weighted imaging (DWI) measures were also extracted to determine whether changes in structural pathways connecting the brain’s default mode network (DMN) would be associated with group differences in task performance. Relative to controls, patients showed marked reductions in the number of internal details across all mental simulations, but no differences in the number of external and semantic-based details. It was also found that, relative to controls, patients with relapsing-remitting MS reported reduced composition ratings for episodic simulations depicting counterfactual events, but not so for actual past or possible future episodes. Additionally, three DWI measures of white matter integrity—fractional anisotropy, radial diffusivity and streamline counts—showed reliable differences between patients with relapsing-remitting MS and matched healthy controls. Importantly, DWI measures associated with reduced white matter integrity in three association tracts on the DMN—the right superior longitudinal fasciculus, the left hippocampal portion of the cingulum and the left inferior longitudinal fasciculus—predicted reductions in the number of internal details during episodic counterfactual simulations. Taken together, these results help to illuminate impairments in episodic simulation in relapsing-remitting MS and show, for the first time, a differential association between white matter integrity and deficits in episodic counterfactual thinking in individuals with relapsing-remitting MS.

Modeling the Properties of White Matter Tracts Using Diffusion Tensor Imaging to Characterize Patterns of Injury in Aging and Neurodegenerative Disease

Diffusion tensor imaging (DTI) is a relatively novel magnetic resonance-based imaging methodology that can provide valuable insight into the microstructure of white matter tracts of the brain. In this paper, we evaluated the reliability and reproducibility of deriving a semi-automated pseudo-atlas DTI tractography method vs. standard atlas-based analysis alternatives, for use in clinical cohorts with neurodegeneration and ventriculomegaly. We showed that the semi-automated pseudo-atlas DTI tractography method was reliable and reproducible across different cohorts, generating 97.7% of all tracts. However, DTI metrics obtained from both methods were significantly different across the majority of cohorts and white matter tracts (p &lt; 0.001). Despite this, we showed that both methods produced patterns of white matter injury that are consistent with findings reported in the literature and with DTI profiles generated from these methodologies. Scatter plots comparing DTI metrics obtained from each methodology showed that the pseudo-atlas method produced metrics that implied a more preserved neural structure compared to its counterpart. When comparing DTI metrics against a measure of ventriculomegaly (i.e., Evans’ Index), we showed that the standard atlas-based method was able to detect decreasing white matter integrity with increasing ventriculomegaly, while in contrast, metrics obtained using the pseudo-atlas method were sensitive for stretch or compression in the posterior limb of the internal capsule. Additionally, both methods were able to show an increase in white matter disruption with increasing ventriculomegaly, with the pseudo-atlas method showing less variability and more specificity to changes in white matter tracts near to the ventricles. In this study, we found that there was no true gold-standard for DTI methodologies or atlases. Whilst there was no congruence between absolute values from DTI metrics, differing DTI methodologies were still valid but must be appreciated to be variably sensitive to different changes within white matter injury occurring concurrently. By combining both atlas and pseudo-atlas based methodologies with DTI profiles, it was possible to navigate past such challenges to describe white matter injury changes in the context of confounders, such as neurodegenerative disease and ventricular enlargement, with transparency and consistency.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

Diffusion spectrum imaging predicts hippocampal sclerosis in mesial temporal lobe epilepsy patients

Objectives

Epileptic patients suffer from seizure recurrence after surgery due to the challenging localization. Improvement of the noninvasive imaging‐based approach for a better definition of the abnormalities would be helpful for a better outcome.

Methods

The quantitative anisotropy (QA) of diffusion spectrum imaging (DSI) is a quantitative scalar of evaluating the water diffusivity. Herein, we investigated the association between neuronal diameters or density acquired in literature and QA of DSI as well as the seizure localization in temporal lobe epilepsy. Thirty healthy controls (HCs) and 30 patients with hippocampal sclerosis (HS) were retrospectively analyzed. QA values were calculated and interactively compared between the areas with different neuronal diameter/density acquired from literature in the HCP‐1021 template. Diagnostic tests were performed on Z‐transformed asymmetry indices (AIs) of QA (which exclude physical asymmetry) among HS patients to evaluate its clinical value.

Results

The QA values in HCs conformed with different pyramidal cell distributions ranged from giant to small; corresponding groups were the motor‐sensory, associative, and limbic groups, respectively. Additionally, the QA value was correlated with the neuronal diameter/density in cortical layer IIIc (correlation coefficient with diameter: 0.529, p = 0.035; density: −0.678, p = 0.011). Decreases in cingulum hippocampal segments (Chs) were consistently observed on the sclerosed side in patients. The area under the curve of the Z‐transformed AI in Chs to the lateralization of HS was 0.957 (sensitivity: 0.909, specificity: 0.895).

Interpretation

QA based on DSI is likely to be useful to provide information to reflect the neuronal diameter/density and further facilitate localization of epileptic tissues.

Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment

White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.

Neuroimaging of Mouse Models of Alzheimer’s Disease

Magnetic resonance imaging (MRI) and positron emission tomography (PET) have made great strides in the diagnosis and our understanding of Alzheimer’s Disease (AD). Despite the knowledge gained from human studies, mouse models have and continue to play an important role in deciphering the cellular and molecular evolution of AD. MRI and PET are now being increasingly used to investigate neuroimaging features in mouse models and provide the basis for rapid translation to the clinical setting. Here, we provide an overview of the human MRI and PET imaging landscape as a prelude to an in-depth review of preclinical imaging in mice. A broad range of mouse models recapitulate certain aspects of the human AD, but no single model simulates the human disease spectrum. We focused on the two of the most popular mouse models, the 3xTg-AD and the 5xFAD models, and we summarized all known published MRI and PET imaging data, including contrasting findings. The goal of this review is to provide the reader with broad framework to guide future studies in existing and future mouse models of AD. We also highlight aspects of MRI and PET imaging that could be improved to increase rigor and reproducibility in future imaging studies.

A time-course study of actively stained mouse brains: Diffusion tensor imaging parameters and connectomic stability over 1 year

While the application of diffusion tensor imaging (DTI), tractography, and connectomics to fixed tissue is a common practice today, there have been limited studies examining the effects of fixation on brain microstructure over extended periods. This mouse model time-course study reports the changes of regional brain volumes and diffusion scalar parameters, such as fractional anisotropy, across 12 representative brain regions as measures of brain structural stability. The scalar DTI parameters and regional volumes were highly variable over the first 2 weeks after fixation. The same parameters were consistent over a 2-8-week window after fixation, which means confounds from tissue stability over that scanning window were minimal. Quantitative connectomes were analyzed over the same time with extension out to 1 year. While there was some change in the scalar metrics at 1 year after fixation, these changes were sufficiently small, particularly in white matter, to support reproducible connectomes over a period ranging from 2-weeks to 1-year post-fixation. These findings delineate a scanning period, during which brain volumes, diffusion scalar metrics, and connectomes are remarkably consistent.

Multimodal Classification of Alzheimer's Disease and Amnestic Mild Cognitive Impairment: Integrated 18F-FDG PET and DTI Study

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients.

OBJECTIVE

To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality.

METHODS

A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen's criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC).

RESULTS

For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers.

CONCLUSION

Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.

Bilateral Distance Partition of Periventricular and Deep White Matter Hyperintensities: Performance of the Method in the Aging Brain

RATIONALE AND OBJECTIVES

Periventricular and deep white matter hyperintensities (WMHs) in the elderly have been reported with distinctive roles in the progression of cognitive decline and dementia. However, the definition of these two subregions of WMHs is arbitrary and varies across studies. Here, we evaluate three partition methods for WMH subregions, including two widely used conventional methods (CV & D10) and one novel method based on bilateral distance (BD).

MATERIALS AND METHODS

The three partition methods were assessed on the MRI scans of 60 subjects, with 20 normal control, 20 mild cognitive impairment, and 20 Alzheimer's disease (AD). Resulting WMH subregional volumes were (1) compared among different partition methods and subject groups, and (2) tested for clinical associations with cognition and dementia. Inter-rater, intrarater, and interscan reproducibility of WMHs volumes were tested on 12 randomly selected subjects from the 60.

RESULTS

For all three partition methods, increased periventricular WMHs were found for AD subjects over normal control. For BD and D10, but not CV method, increased Periventricular WMHs were found for AD subjects over mild cognitive impairment. Significant correlations were found between PVWMHs and Mini-Mental State Examination, Montreal Cognitive Assessment, and Clinical Dementia Rating scores. Furthermore, PVWMHs under BD partition showed higher correlations than D10 and CV. High intrarater and interscan reproducibility (ICCA = 0.998 and 0.992 correspondingly) and substantial inter-rater reproducibility (ICCA = 0.886) were detected.

CONCLUSION

Different WMH partition methods showed comparable diagnostic abilities. The proposed BD method showed advantages in quantifying PVWMH over conventional CV and D10 methods, in terms of higher consistency, larger contrast, and higher diagnosis accuracy. Furthermore, the PVWMH under BD partition showed stronger clinical correlations than conventional methods.

Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment

White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called "Director Field Analysis" (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.

Going round in circles-The Papez circuit in Alzheimer's disease

The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.

18F-florbetapir PET as a marker of myelin integrity across the Alzheimer's disease spectrum

Purpose

Recent evidence suggests that PET imaging with amyloid-β (Aβ) tracers can be used to assess myelin integrity in cerebral white matter (WM). Alzheimer’s disease (AD) is characterized by myelin changes that are believed to occur early in the disease course. Nevertheless, the extent to which demyelination, as measured with Aβ PET, contributes to AD progression remains unexplored.

Methods

Participants with concurrent ¹⁸F-florbetapir (FBP) PET, MRI, and cerebrospinal fluid (CSF) examinations were included (241 cognitively normal, 347 Aβ-positive cognitively impaired, and 207 Aβ-negative cognitively impaired subjects). A subset of these participants had also available diffusion tensor imaging (DTI) images (n = 195). We investigated cross-sectional associations of FBP retention in the white matter (WM) with MRI-based markers of WM degeneration, AD clinical progression, and fluid biomarkers. In longitudinal analyses, we used linear mixed models to assess whether FBP retention in normal-appearing WM (NAWM) predicted progression of WM hyperintensity (WMH) burden and clinical decline.

Results

In AD-continuum individuals, FBP retention in NAWM was (1) higher compared with WMH regions, (2) associated with DTI-based measures of WM integrity, and (3) associated with longitudinal progression of WMH burden. FBP uptake in WM decreased across the AD continuum and with increasingly abnormal CSF biomarkers of AD. Furthermore, FBP retention in the WM was associated with large-calibre axon degeneration as reflected by abnormal plasma neurofilament light chain levels. Low FBP uptake in NAWM predicted clinical decline in preclinical and prodromal AD, independent of demographics, global cortical Aβ, and WMH burden. Most of these associations were also observed in Aβ-negative cognitively impaired individuals.

Conclusion

These results support the hypothesis that FBP retention in the WM is myelin-related. Demyelination levels progressed across the AD continuum and were associated with clinical progression at early stages, suggesting that this pathologic process might be a relevant degenerative feature in the disease course.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05493-y.

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer's Disease

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD.

OBJECTIVE

To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI.

METHODS

We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI.

RESULTS

The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores.

CONCLUSION

The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

Conductance-Based Structural Brain Connectivity in Aging and Dementia

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic assumptions of static q-space sampling during training and reconstruction. Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography. We propose a generative adversarial translation framework for high-quality DWI synthesis with arbitrary q-space sampling given commonly acquired structural images (e.g., B0, T1, T2). Our translation network linearly modulates its internal representations conditioned on continuous q-space information, thus removing the need for fixed sampling schemes. Moreover, this approach enables downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Code is available at https://github.com/mengweiren/q-space-conditioned-dwi-synthesis.

Neural correlates of the impulse dyscontrol domain of mild behavioral impairment

OBJECTIVES

Agitation and aggression are common in dementia and pre-dementia. The dementia risk syndrome mild behavioral impairment (MBI) includes these symptoms in the impulse dyscontrol domain. However, the neural circuitry associated with impulse dyscontrol in neurodegenerative disease is not well understood. The objective of this work was to investigate if regional micro- and macro-structural brain properties were associated with impulse dyscontrol symptoms in older adults with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD).

METHODS

Clinical, neuropsychiatric, and T1-weighted and diffusion-tensor magnetic resonance imaging (DTI) data from 80 individuals with and 123 individuals without impulse dyscontrol were obtained from the AD Neuroimaging Initiative. Linear mixed effect models were used to assess if impulse dyscontrol was related to regional DTI and volumetric parameters.

RESULTS

Impulse dyscontrol was present in 17% of participants with NC, 43% with MCI, and 66% with AD. Impulse dyscontrol was associated with: (1) lower fractional anisotropy (FA), and greater mean, axial, and radial diffusivity in the fornix; (2) lesser FA and greater radial diffusivity in the superior fronto-occipital fasciculus; (3) greater axial diffusivity in the cingulum; (4) greater axial and radial diffusivity in the uncinate fasciculus; (5) gray matter atrophy, specifically, lower cortical thickness in the parahippocampal gyrus.

CONCLUSION

Our findings provide evidence that well-established atrophy patterns of AD are prominent in the presence of impulse dyscontrol, even when disease status is controlled for, and possibly in advance of dementia. Our findings support the growing evidence for impulse dyscontrol symptoms as an early manifestation of AD.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease. The sample in this study consisted of 123 first-degree Alzheimer's disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer's disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer's disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer's disease structural-connectome-trait presents a posterior-posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.

In vivo restricted diffusion imaging (RDI) is sensitive to differences in axonal density in typical children and adults

The ability to dissociate axonal density in vivo from other microstructural properties is important for the diagnosis and treatment of neurologic disease, and new methods to do so are being developed. We investigated one such method-restricted diffusion imaging (RDI)-to see whether it can more accurately replicate histological axonal density patterns in the corpus callosum (CC) of adults and children compared to diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and generalized q-sampling imaging (GQI) methods. To do so, we compared known axonal density patterns defined by histology to diffusion-weighted imaging (DWI) scans of 840 healthy 20- to 40-year-old adults, and to DWI scans of 129 typically developing 7-month-old to 18-year-old children and adolescents. Contrast analyses were used to compare pattern similarities between the in vivo metric and previously published histological density models. We found that RDI was effective at mapping axonal density of small (Cohen's d = 2.60) and large fiber sizes (Cohen's d = 2.84) in adults. The same pattern was observed in the developing sample (Cohen's d = 3.09 and 3.78, respectively). Other metrics, notably NODDI's intracellular volume fraction in adults and GQI generalized fractional anisotropy in children, were also sensitive metrics. In conclusion, the study showed that the novel RDI metric is sensitive to density of small and large axons in adults and children, with both single- and multi-shell acquisition DWI data. Its effectiveness and availability to be used on standard as well as advanced DWI acquisitions makes it a promising method in clinical settings.

Cerebellar White Matter Disruption in Alzheimer's Disease Patients: A Diffusion Tensor Imaging Study

The cognitive role of the cerebellum has recently gained much attention, and its pivotal role in Alzheimer's disease (AD) has now been widely recognized. Diffusion tensor imaging (DTI) has been used to evaluate the disruption of the microstructural milieu in AD, and though several white matter (WM) tracts such as corpus callosum, inferior and superior longitudinal fasciculus, cingulum, fornix, and uncinate fasciculus have been evaluated in AD, data on cerebellar WM tracts are currently lacking. We performed a tractography-based DTI reconstruction of the middle cerebellar peduncle (MCP), and the left and right superior cerebellar peduncles separately (SCPL and SCPR) and addressed the differences in fractional anisotropy (FA), axial diffusivity (Dax), radial diffusivity (RD), and mean diffusivity (MD) in the three tracts between 50 patients with AD and 25 healthy subjects. We found that AD patients showed a lower FA and a higher RD compared to healthy subjects in MCP, SCPL, and SCPR. Moreover, higher MD was found in SCPR and SCPL and higher Dax in SCPL. This result is important as it challenges the traditional view that WM bundles in the cerebellum are unaffected in AD and might identify new targets for therapeutic interventions.

Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706)

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10^-05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10^-12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 × 10^-10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health traits.

Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

MV2Net: Multi-Variate Multi-View Brain Network Comparison over Uncertain Data

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

Alzheimer's disease diagnosis framework from incomplete multimodal data using convolutional neural networks

Alzheimer's disease (AD) is a severe irreversible neurodegenerative disease that has great sufferings on patients and eventually leads to death. Early detection of AD and its prodromal stage, mild cognitive impairment (MCI) which can be either stable (sMCI) or progressive (pMCI), is highly desirable for effective treatment planning and tailoring therapy. Recent studies recommended using multimodal data fusion of genetic (single nucleotide polymorphisms, SNPs) and neuroimaging data (magnetic resonance imaging (MRI) and positron emission tomography (PET)) to discriminate AD/MCI from normal control (NC) subjects. However, missing multimodal data in the cohort under study is inevitable. In addition, data heterogeneity between phenotypes and genotypes biomarkers makes learning capability of the models more challenging. Also, the current studies mainly focus on identifying brain disease classification and ignoring the regression task. Furthermore, they utilize multistage for predicting the brain disease progression. To address these issues, we propose a novel multimodal neuroimaging and genetic data fusion for joint classification and clinical score regression tasks using the maximum number of available samples in one unified framework using convolutional neural network (CNN). Specifically, we initially perform a technique based on linear interpolation to fill the missing features for each incomplete sample. Then, we learn the neuroimaging features from MRI, PET, and SNPs using CNN to alleviate the heterogeneity among genotype and phenotype data. Meanwhile, the high learned features from each modality are combined for jointly identifying brain diseases and predicting clinical scores. To validate the performance of the proposed method, we test our method on 805 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Also, we verify the similarity between the synthetic and real data using statistical analysis. Moreover, the experimental results demonstrate that the proposed method can yield better performance in both classification and regression tasks. Specifically, our proposed method achieves accuracy of 98.22%, 93.11%, and 97.35% for NC vs. AD, NC vs. sMCI, and NC vs. pMCI, respectively. On the other hand, our method attains the lowest root mean square error and the highest correlation coefficient for different clinical scores regression tasks compared with the state-of-the-art methods.

Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits

Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10-8. The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10-31) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction.

Apolipoprotein E homozygous ε4 allele status: Effects on cortical structure and white matter integrity in a young to mid-age sample

Apolipoprotein E (APOE) genotype is the strongest single gene predictor of Alzheimer's disease (AD) and has been frequently associated with AD-related brain structural alterations before the onset of dementia. While previous research has primarily focused on hippocampal morphometry in relation to APOE, sporadic recent findings have questioned the specificity of the hippocampus and instead suggested more global effects on the brain. With the present study we aimed to investigate associations between homozygous APOE ε4 status and cortical gray matter structure as well as white matter microstructure. In our study, we contrasted n = 31 homozygous APOE ε4 carriers (age=34.47 years, including a subsample of n = 12 subjects with depression) with a demographically matched sample without an ε4 allele (resulting total sample: N = 62). Morphometry analyses included a) Freesurfer based cortical segmentations of thickness and surface area measures and b) tract based spatial statistics of DTI measures. We found pronounced and widespread reductions in cortical surface area of ε4 homozygotes in 57 out of 68 cortical brain regions. In contrast, no differences in cortical thickness were observed. Furthermore, APOE ε4 homozygous carriers showed significantly lower fractional anisotropy in the corpus callosum, the right internal and external capsule, the left corona radiata and the right fornix. The present findings support a global rather than regionally specific effect of homozygous APOE ε4 allele status on cortical surface area and white matter microstructure. Future studies should aim to delineate the clinical implications of these findings.

Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis

PURPOSE

AGel amyloidosis is systemic amyloidosis caused by pathogenic variants in the GSN gene. In this study, we sought to characterize the clinical and brain magnetic resonance image (MRI) features of Korean patients with AGel amyloidosis.

MATERIALS AND METHODS

We examined 13 patients with AGel amyloidosis from three unrelated families. Brain MRIs were performed in eight patients and eight age- and sex-matched healthy controls. Therein, we analyzed gray and white matter content using voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and FreeSurfer.

RESULTS

The median age at examination was 73 (interquartile range: 64-76) years. The median age at onset of cutis laxa was 20 (interquartile range: 15-30) years. All patients over that age of 60 years had dysarthria, cutis laxa, dysphagia, and facial palsy. Two patients in their 30s had only mild cutis laxa. The median age at dysarthria onset was 66 (interquartile range: 63.5-70) years. Ophthalmoparesis was observed in three patients. No patient presented with muscle weakness of the limbs. Axial fluid-attenuated inversion recovery images of the brain showed no significant differences between the patient and control groups. Also, analysis of VBM, TBSS, and FreeSurfer revealed no significant differences in cortical thickness between patients and healthy controls at the corrected significance level.

CONCLUSION

Our study outlines the clinical manifestations of prominent bulbar palsy and early-onset cutis laxa in 13 Korean patients with AGel amyloidosis and confirms that AGel amyloidosis mainly affects the peripheral nervous system rather than the central nervous system.

Radial diffusivity is the best global biomarker able to discriminate healthy elders, mild cognitive impairment, and Alzheimer's disease: A diagnostic study of DTI-derived data

Introduction

For the past two decades, diffusion tensor imaging (DTI)-derived metrics allowed the characterization of Alzheimer's disease (AzD). Previous studies reported only a few parameters (most commonly fractional anisotropy, mean diffusivity, and axial and radial diffusivities measured at selected regions). We aimed to assess the diagnostic performance of 11 DTI-derived tensor metrics by using a global approach.

Materials and Methods

A prospective study performed in 34 subjects: 12 healthy elders, 11 mild cognitive impairment (MCI) patients, and 11 patients with AzD. Postprocessing of DTI magnetic resonance imaging allowed the calculation of 11 tensor metrics. Anisotropies included fractional (FA), and relative (RA). Diffusivities considered simple isotropic diffusion (p), simple anisotropic diffusion (q), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Tensors included the diffusion tensor total magnitude (L); and the linear (Cl), planar (Cp), and spherical tensors (Cs). We performed a multivariate discriminant analysis and diagnostic tests assessment.

Results

RD was the only variable selected to assemble a predictive model: Wilks' λ = 0.581, χ² (2) = 14.673, P = 0.001. The model's overall accuracy was 64.5%, with areas under the curve of 0.81, 0.73 and 0.66 to diagnose AzD, MCI, and healthy brains, respectively.

Conclusions

Global DTI-derived RD alone can discriminate between healthy elders, MCI, and AzD patients. Although this study proves evidence of a potential biomarker, it does not provide clinical guidance yet. Additional studies comparing DTI metrics might determine their usefulness to monitor disease progression, measure outcome in drug trials, and even perform the screening of pre-AzD subjects.

Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer's Disease

White matter microstructural changes in Alzheimer's disease (AD) are often assessed using fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI). FA depends on the acquisition and analysis methods, including the fitting algorithm. In this study, we compared FA maps from different acquisitions and fitting algorithms in AD, mild cognitive impairment (MCI), and healthy controls (HCs) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Three acquisitions from two vendors were compared (Siemens 30, GE 48, and Siemens 54 directions). DTI data were fit using nine fitting algorithms (four linear least squares (LLS), two weighted LLS (WLLS), and three non-linear LLS (NLLS) from four software tools (FSL, DSI-Studio, CAMINO, and AFNI). Different cluster volumes and effect-sizes were observed across acquisitions and fits, but higher consistency was observed as the number of diffusion directions increased. Significant differences were observed between HC and AD groups for all acquisitions, while significant differences between HC and MCI groups were only observed for GE48 and SI54. Using the intraclass correlation coefficient, AFNI-LLS and CAMINO-RESTORE were the least consistent with the other algorithms. By combining data across all three acquisitions and nine fits, differences between AD and HC/MCI groups were observed in the fornix and corpus callosum, indicating FA differences in these regions may be robust DTI-based biomarkers. This study demonstrates that comparisons of FA across aging populations could be confounded by variability in acquisitions and fit methodologies and that identifying the most robust DTI methodology is critical to provide more reliable DTI-based neuroimaging biomarkers for assessing microstructural changes in AD.

Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer's Disease Stages

Background

Subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI) are regarded to be at high risk of converting to Alzheimer's disease (AD). Amplitude of low-frequency fluctuations (ALFF) can reflect functional deterioration while diffusion tensor imaging (DTI) is capable of detecting white matter integrity. Our study aimed to investigate the structural and functional alterations to further reveal convergence and divergence among SCD, naMCI, and aMCI and how these contribute to cognitive deterioration.

Methods

We analyzed ALFF under slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands and white matter fiber integrity among normal controls (CN), SCD, naMCI, and aMCI groups. Correlation analyses were further utilized among paired DTI alteration, ALFF deterioration, and cognitive decline.

Results

For ALFF calculation, ascended ALFF values were detected in the lingual gyrus (LING) and superior frontal gyrus (SFG) within SCD and naMCI patients, respectively. Descended ALFF values were presented mainly in the LING, SFG, middle frontal gyrus, and precuneus in aMCI patients compared to CN, SCD, and naMCI groups. For DTI analyses, white matter alterations were detected within the uncinate fasciculus (UF) in aMCI patients and within the superior longitudinal fasciculus (SLF) in naMCI patients. SCD patients presented alterations in both fasciculi. Correlation analyses revealed that the majority of these structural and functional alterations were associated with complicated cognitive decline. Besides, UF alterations were correlated with ALFF deterioration in the SFG within aMCI patients.

Conclusions

SCD shares structurally and functionally deteriorative characteristics with aMCI and naMCI, and tends to convert to either of them. Furthermore, abnormalities in white matter fibers may be the structural basis of abnormal brain activation in preclinical AD stages. Combined together, it suggests that structural and functional integration may characterize the preclinical AD progression.

Early Segmental White Matter Fascicle Microstructural Damage Predicts the Corresponding Cognitive Domain Impairment in Cerebral Small Vessel Disease Patients by Automated Fiber Quantification

Objective: To characterize earlier damage pattern of white matter (WM) microstructure in cerebral small vessel disease (CSVD) and its relationship with cognitive domain dysfunction.

Methods: A total of 144 CSVD patients and 100 healthy controls who underwent neuropsychological measurements and diffusion tensor imaging (DTI) examination were recruited. Cognitive function, emotion, and gait were assessed in each participant. The automated fiber quantification (AFQ) technique was used to extract different fiber properties between groups, and partial correlation and general linear regression analyses were performed to assess the relationship between position-specific WM microstructure and cognitive function.

Results: Specific segments in the association fibers, commissural WM regions of interest (ROIs), and projection fibers were damaged in the CSVD group [P < 0.05, family-wise error (FWE) correction], and these damaged segments showed interhemispheric symmetry. In addition, the damage to specific tract profiles [including the posteromedial component of the right cingulum cingulate (CC), the occipital lobe portion of the callosum forceps major, the posterior portion of the left superior longitudinal fasciculus (SLF), and the bilateral anterior thalamic radiation (ATR)] was related to the dysfunction in specific cognitive domains. Among these tracts, we found the ATR to be the key set of tracts whose profiles were most associated with cognitive dysfunction. The left ATR was a specific fiber bundle associated with episode memory and language function, whereas the fractional anisotropy (FA) values of the intermediate component of the right ATR were negatively correlated with executive function and gait evaluation. It should be noted that the abovementioned relationships could not survive the Bonferroni correction (p < 0.05/27), so we chose more liberal uncorrected statistical thresholds.

Conclusions: Damage to the WM fiber bundles showed extensive interhemispheric symmetry and was limited to particular segments in CSVD patients. Disruption of strategically located fibers was associated with different cognitive deficits, especially the bilateral ATR.

Neuroimaging Advances in Diagnosis and Differentiation of HIV, Comorbidities, and Aging in the cART Era

In the "cART era" of more widely available and accessible treatment, aging and HIV-related comorbidities, including symptoms of brain dysfunction, remain common among HIV-infected individuals on suppressive treatment. A better understanding of the neurobiological consequences of HIV infection is essential for developing thorough treatment guidelines and for optimizing long-term neuropsychological outcomes and overall brain health. In this chapter, we first summarize magnetic resonance imaging (MRI) methods used in over two decades of neuroHIV research. These methods evaluate brain volumetric differences and circuitry disruptions in adults living with HIV, and help map clinical correlations with brain function and tissue microstructure. We then introduce and discuss aging and associated neurological complications in people living with HIV, and processes by which infection may contribute to the risk for late-onset dementias. We describe how new technologies and large-scale international collaborations are helping to disentangle the effect of genetic and environmental risk factors on brain aging and neurodegenerative diseases. We provide insights into how these advances, which are now at the forefront of Alzheimer's disease research, may advance the field of neuroHIV. We conclude with a summary of how we see the field of neuroHIV research advancing in the decades to come and highlight potential clinical implications.

A probabilistic atlas of locus coeruleus pathways to transentorhinal cortex for connectome imaging in Alzheimer's disease

According to the latest Braak staging of Alzheimer's disease (AD), tau pathology occurs earliest in the brain in the locus coeruleus (LC) of the brainstem, then propagates to the transentorhinal cortex (TEC), and later to other neocortical regions. Recent animal and in vivo human brain imaging research also support the trans-axonal propagation of tau pathology. In addition, neurochemical studies link norepinephrine to behavioral symptoms in AD. It is thus critical to examine the integrity of the LC-TEC pathway in studying the early development of the disease, but there has been limited work in this direction. By leveraging the high-resolution and multi-shell diffusion MRI data from the Human Connectome Project (HCP), in this work we develop a novel method for the reconstruction of the LC-TEC pathway in a cohort of 40 HCP subjects carefully selected based on rigorous quality control of the residual distortion artifacts in the brainstem. A probabilistic atlas of the LC-TEC pathway of both hemispheres is then developed in the MNI152 space and distributed publicly on the NITRC website. To apply our atlas on clinical imaging data, we develop an automated approach to calculate the medial core of the LC-TEC pathway for localized analysis of connectivity changes. In a cohort of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the detection of the decreased fiber integrity in the LC-TEC pathways with increasing disease severity.

Resting state functional connectivity abnormalities and delayed recall performance in patients with amnestic mild cognitive impairment

Amnestic Mild Cognitive Impairment (aMCI) represents the transition between healthy aging and Alzheimer's dementia (AD) wherein gradual impairment of cognitive abilities, especially memory sets in. Impairment in episodic memory, especially delayed recall, is a hallmark of AD and therefore, patients with aMCI with more severe impairment in episodic memory are considered to be at greater risk of imminent conversion to AD. Brain structural and functional abnormalities were examined by comparing gray matter volumes, white matter micro-structural integrity and resting state functional connectivity (rsFC), between patients with aMCI (n = 46) having lower vs. higher episodic memory delayed recall (EM-DR) performance scores, correcting the influences of age, sex, number of years of formal education and total brain volumes using voxel-based morphometry, whole-brain tract based spatial statistics and dual regression analysis respectively. 'Low' performers (n = 27) when compared to 'high' performers (n = 19) showed significantly increased rsFC in the dorsal attention network (DAN) and central executive network (CEN) in the absence of demonstrable gray matter volumetric or white matter micro-structural integrity differences at family-wise error (FWE) corrected (p < 0.05) significance threshold. Follow-up data available for 38 (low performers = 22; high performers = 16) of the above 46 subjects (82.60% follow-up rate) over a median follow-up period of 24.5 months revealed that 7 subjects (18.42%) had converted to dementia. These converted subjects included 5 of the 22 low performers (22.72%) and 2 of the 16 high performers (12.5%) within the follow-up sample (n = 38). The results of the study indicate that imminent conversion of aMCI to dementia is higher in low performers in comparison to high performers, which may be characterized by increased rsFC in task positive networks, viz., DAN and CEN, as opposed to gray or white matter structural changes. This finding, therefore, might be considered as a prognostic indicator of progression from aMCI to dementia.

Regional segmentation strategy for DTI analysis of human corpus callosum indicates motor function deficit in mild cognitive impairment

BACKGROUND

The corpus callosum is the largest white matter tract in the human brain, involved in inter-hemispheric transfer and integration of lateralised visual, sensory-motor, language, and cognitive information. Microstructural alterations are implicated in ageing as well as various neurological conditions.

NEW METHOD

Cross-sectional diffusion-weighted images of 107 healthy adults were used to create a linear regression model of the ageing corpus callosum and its sub-regions to evaluate the impact of analysis by sub-region, and to test for deviations from healthy ageing parameters in 28 subjects with mild cognitive impairment (MCI). Alterations in diffusion properties including fractional anisotropy, mean, radial and axial diffusivities were investigated as a function of age.

RESULTS

Changes in DTI parameters showed age-dependent regional differences, likely arising from axonal diameter variation across cross-sectional regions of interest in the corpus callosum. Patterns suggestive of degeneration with healthy ageing were observed in all regions. Diffusion parameters in sub-regions projecting to pre-motor, primary, and supplementary motor areas of the brain differed for MCI versus healthy controls, and MCI subjects were more likely than healthy controls to experience a reduction in motor skills.

COMPARISON WITH EXISTING METHODS

Statistical analyses of the corpus callosum by five manually-defined sub-regions, instead of a single manually-defined region of interest, revealed region-specific changes in microstructure in healthy ageing and MCI, and accounted for clinically-evaluated differences in motor skills between cohorts.

CONCLUSION

This method will support future studies of corpus callosum, enabling identification and measurement of white matter changes that are undetectable with the single ROI approach.

Identification and Classification of Alzheimer's Disease Patients Using Novel Fractional Motion Model

Most diffusion magnetic resonance imaging (dMRI) techniques use the mono-exponential model to describe the diffusion process of water in the brain. However, the observed dMRI signal decay curve deviates from the mono-exponential form. To solve this problem, the fractional motion (FM) model has been developed, which is regarded as a more appropriate model for describing the complex diffusion process in brain tissue. It is still unclear in the identification and classification of Alzheimer's disease (AD) patients using the FM model. The purpose of this study was to investigate the potential feasibility of FM model for differentiating AD patients from healthy controls and grading patients with AD. Twenty-four patients with AD and 11 healthy controls were included. The left and right hippocampus were selected as regions of interest (ROIs). The apparent diffusion coefficient (ADC) values and FM-related parameters, including the Noah exponent (α), the Hurst exponent (H), and the memory parameter (μ=H-1/α), were calculated and compared between AD patients and healthy controls and between mild AD and moderate AD patients using a two-sample t-test. The correlations between FM-related parameters α, H, μ, and ADC values and the cognitive functions assessed by mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scales were investigated using Pearson partial correlation analysis in patients with AD. The receiver-operating characteristic analysis was used to assess the differential performance. We found that the FM-related parameter α could be used to distinguish AD patients from healthy controls (P < 0.05) with greater sensitivity and specificity (left ROI, 0.917 and 0.636; right ROI, 0.917 and 0.727) and grade AD patients (P < 0.05) showed higher sensitivity and specificity (right ROI, 0.917, 0.75). The α was found to be positively correlated with MMSE (P < 0.05) and MoCA (P < 0.05) scores in patients with AD, indicating that the α values in the bilateral hippocampus were a potential MRI-based biomarker of disease severity in AD patients. This novel diffusion model may be useful for further understanding neuropathologic changes in patients with AD.

Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer's disease

OBJECTIVE

In vivo functional changes in white matter during the progression of Alzheimer's disease (AD) have not been previously reported. Our objectives are to measure changes in white matter functional connectivity (FC) in an elderly population undergoing cognitive decline as AD develops, to establish their relationship to neuropsychological scores of cognitive abilities, and to assess the performance in prediction of AD using white matter FC measures as features.

METHODS

Analyses were conducted using resting state functional MRI and neuropsychological data from 383 ADNI participants, including 136 cognitive normal (CN) controls, 46 with significant memory concern, 83 with early mild cognitive impairment (MCI), 37 with MCI, 46 with late MCI, and 35 with AD dementia. FC metrics between segregated white matter tracts and discrete gray matter volumes or between white matter tracts were quantitatively analyzed and characterized, along with their relationships to 6 cognitive measures. Finally, supervised machine learning was implemented on white matter FCs to classify the participants and performance of the classification was evaluated.

RESULTS

Significant decreases in FC measures were found in white matter with prominent, specific, regional deficits appearing in late MCI and AD dementia patients from CN. These changes significantly correlated with neuropsychological measurements of impairments in cognition and memory. The sensitivity and specificity of distinguishing AD dementia and CN using white matter FCs were 0.83 and 0.81 respectively.

CONCLUSIONS AND RELEVANCE

The white matter FC decreased in late MCI and AD dementia patients compared to CN participants, and this decrease was correlated with cognitive measures. White matter FC is valuable in the prediction of AD. All these findings suggest that white matter FC may be a promising avenue for understanding functional impairments in white matter tracts during AD progression.

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b ​= ​0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b ​= ​0 images and 21 DWIs for the primary eigenvector derived from DTI and two b ​= ​0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6 × ​acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 ​b ​= ​0 images and 90 DWIs measures around 1-1.5 ​mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology

Studying older adults with excellent cognitive capacities (Supernormals) provides a unique opportunity for identifying factors related to cognitive success - a critical topic across lifespan. There is a limited understanding of Supernormals' neural substrates, especially whether any of them attends shaping and supporting superior cognitive function or confer resistance to age-related neurodegeneration such as Alzheimer's disease (AD). Here, applying a state-of-the-art diffusion imaging processing pipeline and finite mixture modelling, we longitudinally examine the structural connectome of Supernormals. We find a unique structural connectome, containing the connections between frontal, cingulate, parietal, temporal, and subcortical regions in the same hemisphere that remains stable over time in Supernormals, relatively to typical agers. The connectome significantly classifies positive vs. negative AD pathology at 72% accuracy in a new sample mixing Supernormals, typical agers, and AD risk [amnestic mild cognitive impairment (aMCI)] subjects. Among this connectome, the mean diffusivity of the connection between right isthmus cingulate cortex and right precuneus most robustly contributes to predicting AD pathology across samples. The mean diffusivity of this connection links negatively to global cognition in those Supernormals with positive AD pathology. But this relationship does not exist in typical agers or aMCI. Our data suggest the presence of a structural connectome supporting cognitive success. Cingulate to precuneus white matter integrity may be useful as a structural marker for monitoring neurodegeneration and may provide critical information for understanding how some older adults maintain or excel cognitively in light of significant AD pathology.

Dissecting the Fornix in Basic Memory Processes and Neuropsychiatric Disease: A Review

Background: The fornix is the primary axonal tract of the hippocampus, connecting it to modulatory subcortical structures. This review reveals that fornix damage causes cognitive deficits that closely mirror those resulting from hippocampal lesions. Methods: We reviewed the literature on the fornix, spanning non-human animal lesion research, clinical case studies of human patients with fornix damage, as well as diffusion-weighted imaging (DWI) work that evaluates fornix microstructure in vivo. Results: The fornix is essential for memory formation because it serves as the conduit for theta rhythms and acetylcholine, as well as providing mnemonic representations to deep brain structures that guide motivated behavior, such as when and where to eat. In rodents and non-human primates, fornix lesions lead to deficits in conditioning, reversal learning, and navigation. In humans, damage to the fornix manifests as anterograde amnesia. DWI research reveals that the fornix plays a key role in mild cognitive impairment and Alzheimer's Disease, and can potentially predict conversion from the former to the latter. Emerging DWI findings link perturbations in this structure to schizophrenia, mood disorders, and eating disorders. Cutting-edge research has investigated how deep brain stimulation of the fornix can potentially attenuate memory loss, control epileptic seizures, and even improve mood. Conclusions: The fornix is essential to a fully functioning memory system and is implicated in nearly all neurological functions that rely on the hippocampus. Future research needs to use optimized DWI methods to study the fornix in vivo, which we discuss, given the difficult nature of fornix reconstruction. Impact Statement The fornix is a white matter tract that connects the hippocampus to several subcortical brain regions and is pivotal for episodic memory functioning. Functionally, the fornix transmits essential neurotransmitters, as well as theta rhythms, to the hippocampus. In addition, it is the conduit by which memories guide decisions. The fornix is biomedically important because lesions to this tract result in irreversible anterograde amnesia. Research using in vivo imaging methods has linked fornix pathology to cognitive aging, mild cognitive impairment, psychosis, epilepsy, and, importantly, Alzheimer's Disease.

Identification of Superficial White Matter Abnormalities in Alzheimer's Disease and Mild Cognitive Impairment Using Diffusion Tensor Imaging

BACKGROUND

Diffusion tensor imaging (DTI) estimates the microstructural alterations of the brain, as a magnetic resonance imaging (MRI)-based neuroimaging technique. Prior DTI studies reported decreased structural integrity of the superficial white matter (SWM) in the brain diseases.

OBJECTIVE

This study aimed to determine the diffusion characteristics of SWM in Alzheimer's disease (AD) and mild cognitive impairment (MCI) using tractography and region of interest (ROI) approaches.

METHODS

The diffusion MRI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database on 24 patients with AD, 24 with MCI, and 24 normal control (NC) subjects. DTI processing was performed using DSI Studio software. First, for ROI-based analysis, The superficial white matter was divided into right and left frontal, parietal, temporal, insula, limbic and occipital regions by the Talairach Atlas, Then, for tractography-based analysis, the tractography of each of these regions was performed with 100000 seeds. Finally, the average diffusion values were extracted from voxels within the ROIs and tracts.

RESULTS

Both tractography and ROI analyses showed a significant difference in radial, axial and mean diffusivity values between the three groups (p < 0.05) across most of the SWM. Furthermore, The Mini-Mental State Examination was significantly correlated with radial, axial, and mean diffusivity values in parietal and temporal lobes SWM in the AD group (p < 0.05).

CONCLUSION

DTI provided information indicating microstructural changes in the SWM of patients with AD and MCI. Therefore, assessment of the SWM using DTI may be helpful for the clinical diagnosis of patients with AD and MCI.

Fractional Anisotropy changes in Parahippocampal Cingulum due to Alzheimer's Disease

Current treatments for Alzheimer's disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer's disease. Thus, non-invasive techniques to predict the individual's risk to develop Alzheimer's disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer's disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer's disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer's disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer's disease and controls, and 90% between Alzheimer's disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer's disease, specially when focusing on the parahippocampal cingulum.

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer's disease

BACKGROUND

The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear.

METHODS

Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions.

RESULTS

We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10-9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10-2 and 4.9 × 10-4 for left and right respectively).

CONCLUSIONS

In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression.

METHODS

We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD.

RESULTS

We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions.

DISCUSSION

Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD.

Microstructural disruption of the right inferior fronto-occipital and inferior longitudinal fasciculus contributes to WMH-related cognitive impairment

Aims

White matter hyperintensity (WMH) is the most common neuroimaging manifestation of cerebral small vessel disease and is related to cognitive dysfunction or dementia. This study aimed to investigate the mechanism and effective indicators to predict WMH‐related cognitive impairment.

Methods

We recruited 22 healthy controls (HC), 25 cases of WMH with normal cognition (WMH‐NC), and 23 cases of WMH with mild cognitive impairment (WMH‐MCI). All individuals underwent diffusion tensor imaging (DTI) and a standardized neuropsychological assessment. Automated Fiber Quantification was used to extract altered DTI metrics between groups, and partial correlation was performed to assess the associations between WM integrity and cognitive performance. Furthermore, machine learning analyses were performed to determine underlying imaging markers of WMH‐related cognitive impairment.

Results

Our study found that mean diffusivity (MD) values of several fiber bundles including the bilateral anterior thalamic radiation (ATR), the left inferior fronto‐occipital fasciculus (IFOF), the right inferior longitudinal fasciculus (ILF), and the right superior longitudinal fasciculus (SLF) were negatively correlated with memory function, while that of the anterior component of the right IFOF and the posterior and intermediate component of the right ILF showed significant negative correlation with MMSE and episodic memory, respectively. Furthermore, machine learning analyses showed that the accuracy of recognizing WMH‐MCI patients from the WMH populations was up to 80.5% and the intermediate and posterior components of the right ILF and the anterior component of the right IFOF contribute the most.

Conclusions

Changes in the properties of DTI may be the potential mechanism of WMH‐related MCI, especially the right IFOF and the right ILF, which may become imaging markers for predicting WMH‐related cognitive dysfunction.

Protein-based amide proton transfer-weighted MR imaging of amnestic mild cognitive impairment

Amide proton transfer-weighted (APTw) MRI is a novel molecular imaging technique that can noninvasively detect endogenous cellular proteins and peptides in tissue. Here, we demonstrate the feasibility of protein-based APTw MRI in characterizing amnestic mild cognitive impairment (aMCI). Eighteen patients with confirmed aMCI and 18 matched normal controls were scanned at 3 Tesla. The APTw, as well as conventional magnetization transfer ratio (MTR), signal differences between aMCI and normal groups were assessed by the independent samples t-test, and the receiver-operator-characteristic analysis was used to assess the diagnostic performance of APTw. When comparing the normal control group, aMCI brains typically had relatively higher APTw signals. Quantitatively, APTw intensity values were significantly higher in nine of 12 regions of interest in aMCI patients than in normal controls. The largest areas under the receiver-operator-characteristic curves were 0.88 (gray matter in occipital lobe) and 0.82 (gray matter in temporal lobe, white matter in occipital lobe) in diagnosing aMCI patients. On the contrary, MTR intensity values were significantly higher in only three of 12 regions of interest in the aMCI group. Additionally, the age dependency analyses revealed that these cross-sectional APTw/MTR signals had an increasing trend with age in most brain regions for normal controls, but a decreasing trend with age in most brain regions for aMCI patients. Our early results show the potential of the APTw signal as a new imaging biomarker for the noninvasive molecular diagnosis of aMCI.