Diffusion tensor imaging in mild cognitive impairment and Alzheimerʼs disease: a review

In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus

INTRODUCTION

TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification.

METHODS

Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics.

RESULTS

Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition.

DISCUSSION

Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory.

HIGHLIGHTS

Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.

Altered brain activity and cognitive impairment in patients with psoriasis

BACKGROUND

Patients with psoriasis may have cognitive impairment. However, there is limited information regarding intrinsic brain activity and cognitive function in patients with psoriasis.

OBJECTIVES

This study aim to assess alterations of intrinsic brain activity and its association with cognitive function in patients with psoriasis.

METHODS

A total of 222 patients with psoriasis aged 18-70 years and 144 age and gender-matched healthy controls (HCs) were enrolled into this study. All subjects underwent brain resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing. The rs-fMRI data were analysed for both intrinsic brain activity as indicated by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity (FC). Correlative analysis of brain activity with cognitive assessment was performed.

RESULTS

Compared with the HCs, patients with psoriasis had worse cognitive performance in the Trail Making Test, Digit Span Test and Stroop Color-Word Test (p < 0.05). Patients with psoriasis showed decreased ALFF in the left superior frontal gyrus, the left medial superior frontal gyrus and the right precuneus gyrus; as well as enhanced ALFF in the left paracentral lobule (pFWE  < 0.05). Significant correlations were noted between the altered ALFF in the four brain regions and cognitive assessment (p < 0.05). Moreover, patients with psoriasis had increased FC between the four brain regions with altered ALFF (seeds) and the left prefrontal gyrus, the left anterior cingulate gyrus, left superior parietal lobule and default mode network (DMN) regions such as the right precuneus gyrus, left inferior parietal lobule, right angular gyrus and bilateral inferior temporal gyrus (pFWE  < 0.05).

CONCLUSIONS

Patients with psoriasis had altered brain activity and connectivity in the key brain areas within the DMN-prefrontal circuit. These brain changes may be the underlying neural correlates for cognitive functioning in patients with psoriasis.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

BACKGROUND AND OBJECTIVE

Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue).

METHODS

A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications).

RESULTS

The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients.

CONCLUSION

The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Therapeutic Inhalation of Hydrogen Gas for Alzheimer’s Disease Patients and Subsequent Long-Term Follow-Up as a Disease-Modifying Treatment: An Open Label Pilot Study

(1) Background: Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disorder. Hydrogen gas (H2) is a therapeutic medical gas with multiple functions such as anti-oxidant, anti-inflammation, anti-cell death, and the stimulation of energy metabolism. To develop a disease-modifying treatment for AD through multifactorial mechanisms, an open label pilot study on H2 treatment was conducted. (2) Methods: Eight patients with AD inhaled 3% H2 gas for one hour twice daily for 6 months and then followed for 1 year without inhaling H2 gas. The patients were clinically assessed using the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). To objectively assess the neuron integrity, diffusion tensor imaging (DTI) with advanced magnetic resonance imaging (MRI) was applied to neuron bundles passing through the hippocampus. (3) Results: The mean individual ADAS-cog change showed significant improvement after 6 months of H2 treatment (−4.1) vs. untreated patients (+2.6). As assessed by DTI, H2 treatment significantly improved the integrity of neurons passing through the hippocampus vs. the initial stage. The improvement by ADAS-cog and DTI assessments were maintained during the follow-up after 6 months (significantly) or 1 year (non-significantly). (4) Conclusions: This study suggests that H2 treatment not only relieves temporary symptoms, but also has disease-modifying effects, despite its limitations.

Effectiveness of an Individualized Exergame-Based Motor-Cognitive Training Concept Targeted to Improve Cognitive Functioning in Older Adults With Mild Neurocognitive Disorder: Study Protocol for a Randomized Controlled Trial

BACKGROUND

Simultaneous motor-cognitive training is considered promising for preventing the decline in cognitive functioning in older adults with mild neurocognitive disorder (mNCD) and can be highly motivating when applied in the form of exergaming. The literature points to opportunities for improvement in the application of exergames in individuals with mNCD by developing novel exergames and exergame-based training concepts that are specifically tailored to patients with mNCD and ensuring the implementation of effective training components.

OBJECTIVE

This study systematically explores the effectiveness of a newly developed exergame-based motor-cognitive training concept (called "Brain-IT") targeted to improve cognitive functioning in older adults with mNCD.

METHODS

A 2-arm, parallel-group, single-blinded randomized controlled trial with a 1:1 allocation ratio (ie, intervention: control), including 34 to 40 older adults with mNCD will be conducted between May 2022 and December 2023. The control group will proceed with the usual care provided by the (memory) clinics where the patients are recruited. The intervention group will perform a 12-week training intervention according to the "Brain-IT" training concept, in addition to usual care. Global cognitive functioning will be assessed as the primary outcome. As secondary outcomes, domain-specific cognitive functioning, brain structure and function, spatiotemporal parameters of gait, instrumental activities of daily living, psychosocial factors, and resting cardiac vagal modulation will be assessed. Pre- and postintervention measurements will take place within 2 weeks before starting and after completing the intervention. A 2-way analysis of covariance or the Quade nonparametric analysis of covariance will be computed for all primary and secondary outcomes, with the premeasurement value as a covariate for the predicting group factor and the postmeasurement value as the outcome variable. To determine whether the effects are substantive, partial eta-squared (η²_p) effect sizes will be calculated for all primary and secondary outcomes.

RESULTS

Upon the initial submission of this study protocol, 13 patients were contacted by the study team. Four patients were included in the study, 2 were excluded because they were not eligible, and 7 were being informed about the study in detail. Of the 4 included patients, 2 already completed all premeasurements and were in week 2 of the intervention period. Data collection is expected to be completed by December 2023. A manuscript of the results will be submitted for publication in a peer-reviewed open-access journal in 2024.

CONCLUSIONS

This study contributes to the evidence base in the highly relevant area of preventing disability because of cognitive impairment, which has been declared a public health priority by the World Health Organization.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05387057; https://clinicaltrials.gov/ct2/show/NCT05387057.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/41173.

The mean diffusivity of forceps minor is useful to distinguish amnestic mild cognitive impairment from mild cognitive impairment caused by cerebral small vessel disease

Objectives

In recent years, the desire to make a more fine-grained identification on mild cognitive impairment (MCI) has become apparent, the etiological diagnosis of MCI in particular. Nevertheless, new methods for the etiological diagnosis of MCI are currently insufficient. The objective of this study was to establish discriminative measures for amnestic mild cognitive impairment (a-MCI) and MCI caused by cerebral small vessel disease (CSVD).

Materials and methods

In total, 20 normal controls (NCs), 33 a-MCI patients, and 25 CSVD-MCI patients performed comprehensive neuropsychological assessments concerning global cognitive function and five cognitive domains as well as magnetic resonance imaging scan with diffusion tensor imaging (DTI). Diffusion parameters including fractional anisotropy and mean diffusivity of 20 major white matter metrics were obtained by ROI-based analyses. The neuropsychological tests and diffusion measurements were compared and binary logistic regression was used to identify the best differential indicator for the two MCI subgroups. The discriminating power was calculated by receiver operating characteristic analysis.

Results

Amnestic mild cognitive impairment group showed significant impairment in memory and language function, while CSVD-MCI group revealed more deficits in multi-cognitive domains of memory, language, attention and executive function than controls. Compared to the a-MCI, CSVD-MCI was significantly dysfunctional in the executive function. The CSVD-MCI group had decreased fractional anisotropy and increased mean diffusivity values throughout widespread white matter areas. CSVD-MCI presented more severe damage in the anterior thalamic radiation, forceps major, forceps minor and right inferior longitudinal fasciculus compared with a-MCI group. No significant neuropsychological tests were found in the binary logistic regression model, yet the DTI markers showed a higher discriminative power than the neuropsychological tests. The Stroop test errors had moderate potential (AUC = 0.747; sensitivity = 76.0%; specificity = 63.6%; P = 0.001; 95% CI: 0.617–0.877), and the mean diffusivity value of forceps minor demonstrated the highest predictive power to discriminate each MCI subtype (AUC = 0.815; sensitivity = 88.0%; specificity = 72.7%; P &lt; 0.001; 95% CI: 0.698–0.932).

Conclusion

The mean diffusivity of forceps minor may serve as an optimal indicator to differentiate between a-MCI and CSVD-MCI.

Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer’s disease

While hippocampal atrophy and its regional susceptibility to Alzheimer’s disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-resolution resting-state functional, diffusion and structural MRI, cerebral spinal fluid (CSF), and neuropsychological evaluations were performed in healthy young adults (HY: n = 40) and healthy older adults with negative (HO−: n = 47) and positive (HO+ : n = 25) CSF biomarkers of AD. Morphometry, functional connectivity, and tissue microstructure were estimated from the structural, functional, and diffusion MRI images, respectively. Our results indicated that normal aging affected morphometry, connectivity, and microstructure in all hippocampal subfields, while the subiculum and CA1-3 demonstrated the greatest sensitivity to asymptomatic AD pathology. Tau, rather than amyloid-β, was closely associated with imaging-derived synaptic and microstructural measures. Microstructural metrics were significantly associated with neuropsychological assessments. These findings suggest that the subiculum and CA1-3 are the most vulnerable in asymptomatic AD and tau level is driving these early changes.

Diffusion tensor imaging analysis of rheumatoid arthritis patients with neuropsychiatric features to determine the alteration of white matter integrity due to vascular events

INTRODUCTION

The pathophysiology of neuropsychiatric manifestations in rheumatoid arthritis is not well known. The magnetic resonance imaging of the brain in rheumatoid arthritis demonstrates non-specific findings, and in the majority of cases, magnetic resonance imaging fails to detect an abnormality, even in rheumatoid arthritis patients with neuropsychiatric manifestations. Therefore, we aimed to assess microstructural integrity changes of white matter in patients with rheumatoid arthritis by using different diffusion tensor imaging parameters.

METHODS

Eighteen rheumatoid arthritis patients (10 with neuropsychiatric symptoms and 8 without any neuropsychiatric symptoms) and 14 controls were included. The volume of the T2 hyperintense lesions was assessed. Different diffusion tensor imaging parameters such as fractional anisotropy, apparent diffusion coefficient, trace, axial diffusivity, and radial diffusivity were obtained from six different regions of white matter. Inter group significant difference was determined by one-way analysis of variance followed by Tukey's post hoc test. The accuracy of diffusion tensor imaging matrices was evaluated from the receiver operating characteristic curve.

RESULTS

No significant difference in lesions' volume was detected between rheumatoid arthritis patients with or without neuropsychiatric symptoms. There was an increased apparent diffusion coefficient and radial diffusivity (p < 0.05) as well as decreased fractional anisotropy and axial diffusivity (p < 0.5) in rheumatoid arthritis patients with neuropsychiatric symptoms compared to controls. Moreover, the apparent diffusion coefficient (p < .05) was increased in both positive and negative MRI of patients with neuropsychiatric features compared to the control group. The sensitivity and specificity of the apparent diffusion coefficient parameters was 73% and 72%, respectively.

CONCLUSIONS

The various anisotropic metrics were altered in rheumatoid arthritis patients with neuropsychiatric symptoms by using diffusion tensor imaging analysis, representing that central nervous system vasculitis leads to tissue hypoxia resulting in vasogenic edema. This may lead to axonal and myelin degeneration of white matter fibers and neuronal cell disruption. Key Points • Our study confirms that neurovascular events are not uncommon in RA patients with NP features. Diffusion tensor imaging (DTI) is superior to conventional MRI scan for RA patients with NP features because it distinguishes between gray and white matter structures. • RA patients with NP features are more likely to have microstructural changes detected by DTI than by DWI, and it can provide comprehensive anatomical layouts describing regional disparities in neurodegeneration. • DTI's quantitative association of NP symptoms in a large patient cohort is an important study scope.

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer's disease

Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric-fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO-is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer's disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.

Altered Structural and Functional MRI Connectivity in Type 2 Diabetes Mellitus Related Cognitive Impairment: A Review

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment in many domains. There are several pieces of evidence that changes in neuronal neuropathies and metabolism have been observed in T2DM. Structural and functional MRI shows that abnormal connections and synchronization occur in T2DM brain circuits and related networks. Neuroplasticity and energy metabolism appear to be principal effector systems, which may be related to amyloid beta (Aβ) deposition, although there is no unified explanation that includes the complex etiology of T2DM with cognitive impairment. Herein, we assume that cognitive impairment in diabetes may lead to abnormalities in neuroplasticity and energy metabolism in the brain, and those reflected to MRI structural connectivity and functional connectivity, respectively.

Diffusion tension imaging is a good tool for assessing patients with dementia and behavioral problems and discriminating them from other dementia patients

Background

Dementia is one of the leading public health concerns as the world’s population ages. Although Alzheimer’s disease (AD) is the most common dementia diagnosis among older patients, some patients have additional behavioral symptoms. It is therefore important to provide an exact diagnosis, both to provide the best possible treatment for patients and to facilitate better understanding.

Purpose

To investigate whether magnetic resonance imaging (MRI) with fractional anisotropy (FA) can accurately find patients with behavioral symptoms within a group of AD patients.

Material and Methods

Forty-five patients from the geriatric outpatient clinic were recruited consecutively to form a group of patients with AD and behavioral symptoms (AD + BS) and a control group of 50 patients with established AD. All patients had a full assessment for dementia to establish the diagnosis according to ICD-10. MRI included 3D anatomical recordings for morphometric measurements, DTI for fiber tracking, and quantitative assessment of regional white matter integrity. The DTI analyses included computing of the diffusion tensor and its derived FA index.

Results

We found a significant difference in FA values between the patient groups’ frontal lobes. The FA was greater in the study group in both left (0.39 vs 0.09, p < 0.05) and right (0.40 vs 0.16, p < 0.05) frontal lobes.

Conclusion

MRI with FA will find damage in frontal tracts and may be used as a diagnostic tool and be considered a robust tool for the recognizing different types of dementia in the future.

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

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

A 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.

High-Definition Transcranial Direct Current Stimulation Improves Delayed Memory in Alzheimer's Disease Patients: A Pilot Study Using Computational Modeling to Optimize Electrode Position

BACKGROUND

The optimal stimulation parameters when using transcranial direct current stimulation (tDCS) to improve memory performance in patients with Alzheimer's disease (AD) are lacking. In healthy individuals, inter-individual differences in brain anatomy significantly influence current distribution during tDCS, an effect that might be aggravated by variations in cortical atrophy in AD patients.

OBJECTIVE

To measure the effect of individualized HD-tDCS in AD patients.

METHODS

Nineteen AD patients were randomly assigned to receive active or sham high-definition tDCS (HD-tDCS). Computational modeling of the HD-tDCS-induced electric field in each patient's brain was analyzed based on magnetic resonance imaging (MRI) scans. The chosen montage provided the highest net anodal electric field in the left dorsolateral prefrontal cortex (DLPFC). An accelerated HD-tDCS design was conducted (2 mA for 3×20 min) on two separate days. Pre- and post-intervention cognitive tests and T1 and T2-weighted MRI and diffusion tensor imaging data at baseline were analyzed.

RESULTS

Different montages were optimal for individual patients. The active HD-tDCS group improved significantly in delayed memory and MMSE performance compared to the sham group. Five participants in the active group had higher scores on delayed memory post HD-tDCS, four remained stable and one declined. The active HD-tDCS group had a significant positive correlation between fractional anisotropy in the anterior thalamic radiation and delayed memory score.

CONCLUSION

HD-tDCS significantly improved delayed memory in AD. Our study can be regarded as a proof-of-concept attempt to increase tDCS efficacy. The present findings should be confirmed in larger samples.

Difference in distribution functions: A new diffusion weighted imaging metric for estimating white matter integrity

Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly.

Tacrolimus Protects against Age-Associated Microstructural Changes in the Beagle Brain

The overexpression of calcineurin leads to astrocyte hyperactivation, neuronal death, and inflammation, which are characteristics often associated with pathologic aging and Alzheimer's disease. In this study, we tested the hypothesis that tacrolimus, a calcineurin inhibitor, prevents age-associated microstructural atrophy, which we measured using higher-order diffusion MRI, in the middle-aged beagle brain (n = 30, male and female). We find that tacrolimus reduces hippocampal (p = 0.001) and parahippocampal (p = 0.002) neurite density index, as well as protects against an age-associated increase in the parahippocampal (p = 0.007) orientation dispersion index. Tacrolimus also protects against an age-related decrease in fractional anisotropy in the prefrontal cortex (p < 0.0001). We also show that these microstructural alterations precede cognitive decline and gross atrophy. These results support the idea that calcineurin inhibitors may have the potential to prevent aging-related pathology if administered at middle age.SIGNIFICANCE STATEMENT Hyperactive calcineurin signaling causes neuroinflammation and other neurobiological changes often associated with pathologic aging and Alzheimer's disease (AD). Controlling the expression of calcineurin before gross cognitive deficits are observable might serve as a promising avenue for preventing AD pathology. In this study, we show that the administration of the calcineurin inhibitor, tacrolimus, over 1 year prevents age- and AD-associated microstructural changes in the hippocampus, parahippocampal cortex, and prefrontal cortex of the middle-aged beagle brain, with no noticeable adverse effects. Tacrolimus is already approved by the Food and Drug Administration for use in humans to prevent solid organ transplant rejection, and our results bolster the promise of this drug to prevent AD and aging-related pathology.

Disrupted Network Topology Contributed to Spatial Navigation Impairment in Patients With Mild Cognitive Impairment

Impairment in spatial navigation (SN) and structural network topology is not limited to patients with Alzheimer’s disease (AD) dementia and can be detected earlier in patients with mild cognitive impairment (MCI). We recruited 32 MCI patients (65.91 ± 11.33 years old) and 28 normal cognition patients (NC; 69.68 ± 10.79 years old), all of whom underwent a computer-based battery of SN tests evaluating egocentric, allocentric, and mixed SN strategies and diffusion-weighted and T₁-weighted Magnetic Resonance Imaging (MRI). To evaluate the topological features of the structural connectivity network, we calculated its measures such as the global efficiency, local efficiency, clustering coefficient, and shortest path length with GRETNA. We determined the correlation between SN accuracy and network topological properties. Compared to NC, MCI subjects demonstrated a lower egocentric navigation accuracy. Compared with NC, MCI subjects showed significantly decreased clustering coefficients in the left middle frontal gyrus, right rectus, right superior parietal gyrus, and right inferior parietal gyrus and decreased shortest path length in the left paracentral lobule. We observed significant positive correlations of the shortest path length in the left paracentral lobule with both the mixed allocentric–egocentric and the allocentric accuracy measured by the average total errors. A decreased clustering coefficient in the right inferior parietal gyrus was associated with a larger allocentric navigation error. White matter hyperintensities (WMH) did not affect the correlation between network properties and SN accuracy. This study demonstrated that structural connectivity network abnormalities, especially in the frontal and parietal gyri, are associated with a lower SN accuracy, independently of WMH, providing a new insight into the brain mechanisms associated with SN impairment in MCI.

Fascicle- and Glucose-Specific Deterioration in White Matter Energy Supply in Alzheimer's Disease

BACKGROUND

White matter energy supply to oligodendrocytes and the axonal compartment is crucial for normal axonal function. Although gray matter glucose hypometabolism is extensively reported in Alzheimer's disease (AD), glucose and ketones, the brain's two main fuels, are rarely quantified in white matter in AD.

OBJECTIVE

Using a dual-tracer PET method combined with a fascicle-specific diffusion MRI approach, robust to white matter hyper intensities and crossing fibers, we aimed to quantify both glucose and ketone metabolism in specific white matter fascicles associated with mild cognitive impairment (MCI; n = 51) and AD (n = 13) compared to cognitively healthy age-matched controls (Controls; n = 14).

METHODS

Eight white matter fascicles of the limbic lobe and corpus callosum were extracted and analyzed into fascicle profiles of five sections. Glucose (18F-fluorodeoxyglucose) and ketone (11C-acetoacetate) uptake rates, corrected for partial volume effect, were calculated along each fascicle.

RESULTS

The only fascicle with significantly lower glucose uptake in AD compared to Controls was the left posterior cingulate segment of the cingulum (-22%; p = 0.016). Non-significantly lower glucose uptake in this fascicle was also observed in MCI. In contrast to glucose, ketone uptake was either unchanged or higher in sections of the fornix and parahippocampal segment of the cingulum in AD.

CONCLUSION

To our knowledge, this is the first report of brain fuel uptake calculated along white matter fascicles in humans. Energetic deterioration in white matter in AD appears to be specific to glucose and occurs first in the posterior cingulum.

White matter connectivity for early prediction of Alzheimer's disease

BACKGROUND

Early diagnosis of Alzheimer's disease (AD) remains challenging. It is speculated that structural atrophy in white matter tracts commences prior to the onset of AD symptoms.

OBJECTIVE

We hypothesize that disruptions in white matter tract connectivity precedes the onset of AD symptoms and these disruptions could be leveraged for early prediction of AD.

METHODS

Diffusion tensor images (DTI) from 52 subjects with mild cognitive impairment (MCI) were selected. Subjects were dichotomized into two age and gender matched groups; the MCI-AD group (22 subjects who progressed to develop AD) and the MCI-control group (who did not develop AD). DTI images were anatomically parcellated into 90 distinct regions ROIs followed by tractography methods to obtain different biophysical networks. Features extracted from these networks were used to train predictive algorithms with the objective of discriminating the MCI-AD and MCI-control groups. Model performance and best features are reported.

RESULTS

Up to 80% prediction accuracy was achieved using a combination of features from the 'right anterior cingulum' and 'right frontal superior medial'. Additionally, local network features were more useful than global in improving the model's performance.

CONCLUSION

Connectivity-based characterization of white matter tracts offers potential for early detection of MCI-AD and in the discovery of novel imaging biomarkers.

Mitochondrial dysfunction: A potential target for Alzheimer's disease intervention and treatment

Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder which manifests as a progressive decline in cognitive function. Mitochondrial dysfunction plays a critical role in the early stages of AD, and advances the progression of this age-related neurodegenerative disorder. Therefore, it can be a potential target for interventions to treat AD. Several therapeutic strategies to target mitochondrial dysfunction have gained significant attention in the preclinical stage, but the clinical trials performed to date have shown little progress. Thus, we discuss the mechanisms and strategies of different therapeutic agents for targeting mitochondrial dysfunction in AD. We hope that this review will inspire and guide the development of efficient AD drugs in the future.

Default mode network integrity changes contribute to cognitive deficits in subcortical vascular cognitive impairment, no dementia

Vascular cognitive impairment, no dementia (VCIND) refers to cognitive deficits associated with underlying vascular causes that are insufficient to confirm a diagnosis of dementia. The default mode network (DMN) is a large-scale brain network of interacting brain regions involved in attention, working memory and executive function. The role of DMN white matter integrity in cognitive deficits of VCIND patients is unclear. Using diffusion tensor imaging (DTI), this study was carried out to investigate white matter microstructural changes in the DMN in VCIND patients and their contributions to cognitive deficits. Thirty-one patients with subcortical VCIND and twenty-two healthy elderly subjects were recruited. All patients underwent neuropsychological assessments and DTI examination. Voxel-based analyses were performed to extract fractional anisotropy (FA) and mean diffusivity (MD) measures in the DMN. Compared with the healthy elderly subjects, patients diagnosed with subcortical VCIND presented with abnormal white matter integrity in several key hubs of the DMN. The severity of damage in the white matter microstructure in the DMN significantly correlated with cognitive dysfunction. Mediation analyses demonstrated that DTI values could account for attention, executive and language impairments, and partly mediated global cognitive dysfunction in the subcortical VCIND patients. DMN integrity is significantly impaired in subcortical VCIND patients. The disrupted DMN connectivity could explain the attention, language and executive dysfunction, which indicates that the white matter integrity of the DMN may be a neuroimaging marker for VCIND.

Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease

Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

Accelerating Structural Degeneration in Temporal Regions and Their Effects on Cognition in Aging of MCI Patients

Age is the major risk factor for Alzheimer's disease (AD) and for mild cognitive impairment (MCI). However, there is limited evidence about MCI-specific aging-related simultaneous changes of the brain structure and their impact on cognition. We analyzed the brain imaging data from 269 subjects (97 MCI patients and 172 cognitively normal [CN] elderly) using voxel-based morphometry and tract-based spatial statistics procedures to explore the special structural pattern during aging. We found that the patients with MCI showed accelerated age-related reductions in gray matter volume in the left planum temporale, thalamus, and posterior cingulate gyrus. The similar age×group interaction effect was found in the fractional anisotropy of the bilateral parahippocampal cingulum white matter tract, which connects the temporal regions. Importantly, the age-related temporal gray matter and white matter alterations were more significantly related to performance in memory and attention tasks in MCI patients. The accelerated degeneration patterns in the brain structure provide evidence for different neural mechanisms underlying aging in MCI patients. Temporal structural degeneration may serve as a potential imaging marker for distinguishing the progression of the preclinical AD stage from normal aging.

Disrupted White Matter Networks from Subjective Memory Impairment to Amnestic Mild Cognitive Impairment

BACKGROUND AND OBJECTIVE

Subjective memory impairment (SMI) is a preclinical stage prior to amnestic mild cognitive impairment (aMCI) along with the Alzheimer's disease (AD) continuum. We hypothesized that SMI patients had white matter (WM) network disruptions similar to those in aMCI patients.

METHODS

We used diffusion-tensor magnetic resonance imaging and graph theory to construct, analyze, and compare the WM networks among 20 normal controls (NC), 20 SMI patients, and 20 aMCI patients.

RESULTS

Compared with the NC group, the SMI group had significantly decreased global and local efficiency and an increased shortest path length. Moreover, similar to the aMCI group, the SMI group had lower nodal efficiency in regions located in the frontal and parietal lobes, limbic systems, and caudate nucleus compared to that of the NC group.

CONCLUSION

Similar to aMCI patient, SMI patients exhibited WM network disruptions, and detection of these disruptions could facilitate the early detection of SMI.

Diffusion tensor imaging analysis in scrub typhus meningoencephalitis to determine the alteration of microstructural subcortical white-matter integrity

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) of the brain in scrub typhus meningoencephalitis is non-specific, and in the majority of the cases, conventional MRI fails to detect any abnormality. However, autopsy reports depict central nervous system involvement in almost all patients. There is therefore a need for research on the quantitative assessment of brain parenchyma that can detect microstructural abnormalities. The study aimed to assess the microstructural integrity changes of scrub typhus meningoencephalitis by using different diffusion tensor imaging (DTI) parameters.

METHODS

This was a retrospective analysis of scrub typhus meningoencephalitis. Seven patients and seven age- and sex-matched healthy controls were included. Different DTI parameters such as apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative anisotropy (RA), trace, volume ratio (VR) and geodesic anisotropy (GA) were obtained from six different regions of subcortical white matter at the level of the centrum semiovale. Intergroup significant difference was determined by one-way analysis of variance followed by Tukey's post hoc test. Receiver operating characteristic curves were constructed to determine the accuracy of the DTI matrices.

RESULTS

There was a significant decrease in FA, RA and GA as well as an increase in ADC and VR in the subcortical white matter in patients with scrub typhus meningoencephalitis compared to controls (p < 0.001). The maximum sensitivity of the DTI parameters was 85.7%, and the maximum specificity was 81%.

CONCLUSION

There was an alteration of subcortical white-matter integrity in scrub typhus meningoencephalitis that represents the axonal degeneration, myelin breakdown and neuronal degeneration. DTI may be a useful tool to detect white-matter abnormalities in scrub typhus meningoencephalitis in clinical practice, particularly in patients with negative conventional MRI.

Microstructural Injury to Left-Sided Perisylvian White Matter Predicts Language Decline After Brain Radiation Therapy

PURPOSE

Our purpose was to investigate the association between imaging biomarkers of radiation-induced white matter (WM) injury within perisylvian regions and longitudinal language decline in patients with brain tumors.

METHODS AND MATERIALS

Patients with primary brain tumors (n = 44) on a prospective trial underwent brain magnetic resonance imaging, diffusion-weighted imaging, and language assessments of naming (Boston Naming Test [BNT]) and fluency (Delis-Kaplan Executive Function System Category Fluency [DKEFS-CF]) at baseline and 3, 6, and 12 months after fractionated radiation therapy (RT). Reliable change indices of language function (0-6 months), accounting for practice effects (RCI-PE), evaluated decline. Bilateral perisylvian WM regions (superficial WM subadjacent to Broca's area and the superior temporal gyrus [STG], inferior longitudinal fasciculus [ILF], inferior fronto-occipital fasciculus [IFOF], and arcuate fasciculus) were autosegmented. We quantified volume and diffusion measures of WM microstructure: fractional anisotropy (FA; lower values indicate disruption) and mean diffusivity (MD; higher values indicate injury). Linear mixed-effects models assessed mean dose as predictor of imaging biomarker change and imaging biomarkers as longitudinal predictors of language scores.

RESULTS

DKEFS-CF scores declined at 6 months post-RT (RCI-PE, -0.483; P = .01), whereas BNT scores improved (RCI-PE, 0.262; P = .04). Higher mean dose to left and right regions was predictive of decreased volume (left-STG, P = .02; right-ILF and IFOF, P = .03), decreased FA (left-WM tracts, all P < .01; right-STG and IFOF, P < .02), and increased MD of left-WM tracts (all P < .03). Volume loss within left-Broca's area (P = .01), left-ILF (P = .01), left-IFOF (P = .01), and left-arcuate fasciculus (P = .04) was associated with lower BNT scores. Lower FA correlated with poorer DKEFS-CF and BNT scores within left-ILF (P = .02, not significant), left-IFOF (P = .02, .04), and left-arcuate fasciculus (P = .01, .01), respectively. Poorer DKEFS-CF scores correlated with increased MD values within the left-arcuate fasciculus (P = .03). Right-sided biomarkers did not correlate with language scores.

CONCLUSIONS

Patients with primary brain tumors experience language fluency decline post-RT. Poorer fluency and naming function may be explained by microstructural injury to left-sided perisylvian WM, representing potential dose-avoidance targets for language preservation.

Corpus callosum integrity loss predicts cognitive impairment in Leukoaraiosis

Objective

To investigate regional white matter fibers loss in Leukoaraiosis (LA) and its relationship with cognitive impairments.

Methods

Fifty‐six participants with LA and 38 healthy controls underwent clinical evaluations and MR scans. Participants with LA were classified as cognitively normal (LA‐NC, n = 18), vascular cognitive impairment of none dementia (LA‐VCIND, n = 24), and vascular dementia (LA‐VaD, n = 14) by Mini‐Mental State Examination and Clinical Dementia Rating. Cognitive domains including visual‐spatial, naming, attention, language, abstraction, memory, and orientation were assessed. With the use of Tract‐based spatial statistics, mean fractional anisotropy (FA) of major white matter fiber tracts were compared between LA and controls and among LA groups with varying levels of cognitive impairments. Regression analyses were performed to evaluate relationships between FA values and cognitive performance.

Results

Participants showed significant FA reduction in the corpus callosum (CC), bilateral corona radiata, anterior limb of the internal capsule, external capsule, posterior thalamic radiation, and superior longitudinal fasciculus compared to controls and across LA groups. The LA‐VaD group showed consistent damage in the body and genu of CC compared to the LA‐NC and LA‐VCIND groups. A positive correlation between visual‐spatial and FA reduction in right anterior corona radiates in LA‐VCIND and body of CC in LA‐ VaD.

Interpretation

We found regional fiber loss in the CC across the cognitive spectrum in patients with LA and correlations between FA and visuospatial impairment in the anterior corona radiata in patients with LA‐VCIND and in the body of CC in patients with LA‐VaD.

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.

Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models.

Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.

Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition

A history of depression is a risk factor for dementia. Despite strong epidemiologic evidence, the pathways linking depression and dementia remain unclear. We assessed structural brain alterations in white and gray matter of frontal-executive and corticolimbic circuitries in five groups of older adults putatively at-risk for developing dementia- remitted depression (MDD), non-amnestic MCI (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. We also examined two other groups: non-psychiatric ("healthy") controls (HC) and individuals with Alzheimer's dementia (AD). Magnetic resonance imaging (MRI) data were acquired on the same 3T scanner. Following quality control in these seven groups, from diffusion-weighted imaging (n = 300), we compared white matter fractional anisotropy (FA), mean diffusivity (MD), and from T1-weighted imaging (n = 333), subcortical volumes and cortical thickness in frontal-executive and corticolimbic regions of interest (ROIs). We also used exploratory graph theory analysis to compare topological properties of structural covariance networks and hub regions. We found main effects for diagnostic group in FA, MD, subcortical volume, and cortical thickness. These differences were largely due to greater deficits in the AD group and to a lesser extent aMCI compared with other groups. Graph theory analysis revealed differences in several global measures among several groups. Older individuals with remitted MDD and naMCI did not have the same white or gray matter changes in the frontal-executive and corticolimbic circuitries as those with aMCI or AD, suggesting distinct neural mechanisms in these disorders. Structural covariance global metrics suggested a potential difference in brain reserve among groups.

Baseline White Matter Is Associated With Physical Fitness Change in Preclinical Alzheimer's Disease

White matter (WM) microstructure is a sensitive marker to distinguish individuals at risk of Alzheimer's disease. The association of objective physical fitness (PF) measures and WM microstructure has not been explored and mixed results reported with physical activity (PA). Longitudinal studies of WM with PA and PF measures have had limited investigation. This study explored the relationship between objective PF measures over 24-months with "normal-appearing" WM microstructure. Data acquired on magnetic resonance imaging was used to measure "normal-appearing" WM microstructure at baseline and 24-months. Clinical variables such as cognitive and blood-based measures were collected longitudinally. Also, as part of the randomized controlled trial of a PA, extensive measures of PA and fitness were obtained over the 24 months. Bilateral corticospinal tracts (CST) and the corpus callosum showed a significant association between PF performance over 24-months and baseline WM microstructural measures. There was no significant longitudinal effect of the intervention or PF performance over 24-months. Baseline WM microstructural measures were significantly associated with PF performance over 24-months in this cohort of participants with vascular risk factors and at risk of Alzheimer's disease with distinctive patterns for each PF test.

CRJ, Knopman DS, Vemuri P. Relationship Between Risk Factors and Brain Reserve in Late Middle Age: Implications for Cognitive Aging

Background

Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve.

Objective

We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related cognitive disorders.

Methods

We examined multimodal [structural and diffusion Magnetic Resonance Imaging (MRI), FDG PET] neuroimaging measures in 50-65 year olds to examine the associations between risk factors (Intellectual/Physical Activity: education-occupation composite, physical, and cognitive-based activity engagement; General Health Factors: presence of cardiovascular and metabolic conditions (CMC), body mass index, hemoglobin A1c, smoking status (ever/never), CAGE Alcohol Questionnaire (>2, yes/no), Beck Depression Inventory score), brain reserve measures [Dynamic: genu corpus callosum fractional anisotropy (FA), posterior cingulate cortex FDG uptake, superior parietal cortex thickness, AD signature cortical thickness; Static: intracranial volume], and cognition (global, memory, attention, language, visuospatial) from a population-based sample. We quantified dynamic proxies of brain reserve (cortical thickness, glucose metabolism, microstructural integrity) and investigated various protective/risk factors.

Results

Education-occupation was associated with cognition and total intracranial volume (static measure of brain reserve), but was not associated with any of the dynamic neuroimaging biomarkers. In contrast, many general health factors were associated with the dynamic neuroimaging proxies of brain reserve, while most were not associated with cognition in this late middle aged group.

Conclusion

Brain reserve, as exemplified by the four dynamic neuroimaging features studied here, is itself at least partly influenced by general health status in midlife, but may be largely independent of education and occupation.

Association of white matter microstructural integrity with cognition and dementia

Late-life measures of white matter (WM) microstructural integrity may predict cognitive status, cognitive decline, and incident mild cognitive impairment (MCI) or dementia. We considered participants of the Atherosclerosis Risk in Communities study who underwent cognitive assessment and neuroimaging in 2011-2013 and were followed through 2016-2017 (n = 1775 for analyses of prevalent MCI and dementia, baseline cognitive performance, and longitudinal cognitive change and n = 889 for analyses of incident MCI, dementia, or death). Cross-sectionally, both overall WM fractional anisotropy and overall WM mean diffusivity were strongly associated with baseline cognitive performance and risk of prevalent MCI or dementia. Longitudinally, greater overall WM mean diffusivity was associated with accelerated cognitive decline, as well as incident MCI, incident dementia, and mortality, but WM fractional anisotropy was not robustly associated with cognitive change or incident cognitive impairment. Both cross-sectional and longitudinal associations were attenuated after additionally adjusting for likely downstream pathologic changes. Increased WM mean diffusivity may provide an early indication of dementia pathogenesis.

Multivariate Analysis of White Matter Structural Networks of Alzheimer's Disease

The connectome-wide association studies exploring association between brain connectome and disease phenotypes have suffered from massive number of comparisons. In this paper, we propose to apply a multivariate distance-based analytic framework on brain white matter (WM) structural networks invaded by Alzheimer's disease (AD). Eighty-three subjects including patients with AD, amnestic mild cognitive impairment (aMCI) and healthy subjects were scanned with dMRI. By constructing WM structural network for each individual, we used both multivariate and traditional univariate statistical models to complimentarily analyze network pattern and fiber strength changes due to AD. WM connections linked with several brain structures were found significantly changed between AD group and normal controls. No significant findings were observed between aMCI group and normal controls. Our results demonstrate the sensitivity of the combined connectome-based analytic framework in detecting abnormalities of structural brain network.

Brain microstructural abnormalities in type 2 diabetes mellitus: A systematic review of diffusion tensor imaging studies

Type 2 diabetes mellitus (T2DM) is associated with deficits in the structure and function of the brain. Diffusion tensor imaging (DTI) is a highly sensitive method for characterizing cerebral tissue microstructure. Using PRISMA guidelines, we identified 29 studies which have demonstrated widespread brain microstructural impairment and topological network disorganization in patients with T2DM. Most consistently reported structures with microstructural abnormalities were frontal, temporal, and parietal lobes in the lobar cluster; corpus callosum, cingulum, uncinate fasciculus, corona radiata, and internal and external capsules in the white matter cluster; thalamus in the subcortical cluster; and cerebellum. Microstructural abnormalities were correlated with pathological derangements in the endocrine profile as well as deficits in cognitive performance in the domains of memory, information-processing speed, executive function, and attention. Altogether, the findings suggest that the detrimental effects of T2DM on cognitive functions might be due to microstructural disruptions in the central neural structures.

Diagnostic Value of Diffusion Tensor Imaging and Positron Emission Tomography in Early Stages of Frontotemporal Dementia

BACKGROUND

Due to suboptimal sensitivity and specificity of structural and molecular neuroimaging tools, the diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging.

OBJECTIVE

Investigation of the sensitivity of diffusion tensor imaging (DTI) and fluorodeoxyglucose positron emission tomography (FDG-PET) to detect cerebral alterations in early stages of bvFTD despite inconspicuous conventional MRI.

METHODS

Thirty patients with early stages of bvFTD underwent a detailed neuropsychological examination, cerebral 3T MRI with DTI analysis, and FDG-PET. After 12 months of follow-up, all patients finally fulfilled the diagnosis of bvFTD. Individual FDG-PET data analyses showed that 20 patients exhibited a "typical" pattern for bvFTD with bifrontal and/or temporal hypometabolism (bvFTD/PET+), and that 10 patients showed a "non-typical"/normal pattern (bvFTD/PET-). DTI data were compared with 42 healthy controls in an individual and voxel-based group analysis. To examine the clinical relevance of the findings, associations between pathologically altered voxels of DTI or FDG-PET results and behavioral symptoms were estimated by linear regression analyses.

RESULTS

DTI voxel-based group analyses revealed microstructural degeneration in bifrontal and bitemporal areas in bvFTD/PET+ and bvFTD/PET- groups. However, when comparing the sensitivity of individual DTI data analysis with FDG-PET, DTI appeared to be less sensitive. Neuropsychological symptoms were considerably related to neurodegeneration within frontotemporal areas identified by DTI and FDG-PET.

CONCLUSION

DTI seems to be an interesting tool for detection of functionally relevant neurodegenerative alterations in early stages of bvFTD, even in bvFTD/PET- patients. However, at a single subject level, it seems to be less sensitive than FDG-PET. Thus, improvement of individual DTI analysis is necessary.

In search of multimodal brain alterations in Alzheimer's and Binswanger's disease

Structural and functional brain abnormalities have been widely identified in dementia, but with variable replicability and significant overlap. Alzheimer's disease (AD) and Binswanger's disease (BD) share similar symptoms and common brain changes that can confound diagnosis. In this study, we aimed to investigate correlated structural and functional brain changes in AD and BD by combining resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A group independent component analysis was first performed on the fMRI data to extract 49 intrinsic connectivity networks (ICNs). Then we conducted a multi-set canonical correlation analysis on three features, functional network connectivity (FNC) between ICNs, fractional anisotropy (FA) and mean diffusivity (MD). Two inter-correlated components show significant group differences. The first component demonstrates distinct brain changes between AD and BD. AD shows increased cerebellar FNC but decreased thalamic and hippocampal FNC. Such FNC alterations are linked to the decreased corpus callosum FA. AD also has increased MD in the frontal and temporal cortex, but BD shows opposite alterations. The second component demonstrates specific brain changes in BD. Increased FNC is mainly between default mode and sensory regions, while decreased FNC is mainly within the default mode domain and related to auditory regions. The FNC changes are associated with FA changes in posterior/middle cingulum cortex and visual cortex and increased MD in thalamus and hippocampus. Our findings provide evidence of linked functional and structural deficits in dementia and suggest that AD and BD have both common and distinct changes in white matter integrity and functional connectivity.

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This is the first study to explore multi-modalities changes in different dementia.

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A multimodal fusion method is applied to identify joint components.

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Brain abnormalities in different modalities are highly correlated.

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Alzheimer's and Binswanger's disease share similar brain changes.

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Alzheimer's and Binswanger's disease also have distinct brain changes.

Neuroimaging Biomarkers for Alzheimer's Disease

Currently, over five million Americans suffer with Alzheimer’s disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.

Gray matter T1-w/T2-w ratios are higher in Alzheimer's disease

Myelin determines the conduction of neuronal signals along axonal connections in networks of the brain. Loss of myelin integrity in neuronal circuits might result in cognitive decline in Alzheimer's disease (AD). Recently, the ratio of T1-weighted by T2-weighted MRI has been used as a proxy for myelin content in gray matter of the cortex. With this approach, we investigated whether AD dementia patients show lower cortical myelin content (i.e., a lower T1-w/T2-w ratio value). We selected structural T1-w and T2-w MR images of 293 AD patients and 172 participants with normal cognition (NC). T1-w/T2-w ratios were computed for the whole brain and within 90 automated anatomical labeling atlas regions using SPM12, compared between groups and correlated with the neuronal injury marker tau in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE). In contrast to our hypothesis, AD patients showed higher whole brain T1-w/T2-w ratios than NC, and regionally in 31 anatomical areas (p < .0005; d = 0.21 to 0.48), predominantly in the inferior parietal lobule, angular gyrus, anterior cingulate, and precuneus. Regional higher T1-w/T2-w values were associated with higher CSF tau concentrations (p < .0005; r = .16 to .22) and worse MMSE scores (p < .0005; r = -.16 to -.21). These higher T1-w/T2-w values in AD seem to contradict previous pathological findings of demyelination and disconnectivity in AD. Future research should further investigate the biological processes reflected by increases in T1-w/T2-w values.

Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease

Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown.

Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters.

Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%.

Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

Genetic influences on white matter and metabolism abnormal change in Alzheimer's disease: Meta-analysis for neuroimaging research on presenilin 1 mutation

Mutations in the presenilin1 (PSEN1) cause familial Alzheimer's disease (FAD), providing a special opportunity to study pre-symptomatic individuals who would be predicted to develop Alzheimer's disease (AD) in the future. However, whether presenilin1 (PSEN1) genotype and neuroimaging markers is a harbinger of AD remains controversial. We aimed to explore the association of PSEN1 genotype with neuroimaging markers of AD: white matter integrity, cerebral amyloid deposition and brain metabolism. We reviewed studies of diffusion tensor imaging (DTI), amyloid deposition and cerebral metabolism in patients with AD and control, in order to address the relative change of white matter microstructural associated with PSEN1 genotype. We performed a systematic meta-analysis and review of 11 cross-sectional studies identified in several database from 2008 to 2018 (n = 165). The pooled standard mean difference (SMD) value was calculated to estimate the association between PSEN1 and white matter change and brain metabolism. PSEN1 mutation carrier status was associated with mean diffusivity (MD) change (pooled SMD: 2.29; 95% CI 1.04 to 3.53; p < 0.001) and increased cerebral amyloid positron emission tomography tracer (pooled SMD: 3.78, 95% CI 1.04 to 6.53, p = 0.007). PSEN1 was not associated with white matter metabolism change (p = 0.069). PSEN1 was associated with mean diffusivity (MD) increase in DTI markers and decreased brain metabolism. Theses associations may suggest the potential role of the PSEN1 gene and imaging marker in Alzheimer's disease.

Regional Amyloid-β Load and White Matter Abnormalities Contribute to Hypometabolism in Alzheimer's Dementia

We investigated the association between amyloid-β deposition and white matter (WM) integrity as a determinant of brain glucose hypometabolism across the Alzheimer's disease (AD) spectrum. We assessed ninety-six subjects (27 cognitively normal, 49 mild cognitive impairment, and 20 AD dementia) who underwent [¹⁸F]FDG and [¹⁸F]Florbetapir positron emission tomography (PET) as well as magnetic resonance imaging (MRI) with diffusion tensor imaging. Among the regions with reduced fractional anisotropy (FA) in the AD group, we selected a voxel of interest in the angular bundle bilaterally for subsequent analyses. Using voxel-based interaction models at voxel level, we tested whether the regional hypometabolism is associated with FA in the angular bundle and regional amyloid-β deposition. In the AD patients, [¹⁸F]FDG hypometabolism in the striatum, mesiobasal temporal, orbitofrontal, precuneus, and cingulate cortices were associated with the interaction between high levels of [¹⁸F]Florbetapir standard uptake value ratios (SUVR) in these regions and low FA in the angular bundle. We found that the interaction between, rather than the independent effects of, high levels of amyloid-β deposition and WM integrity disruption determined limbic hypometabolism in patients with AD. This finding highlights a more integrative model for AD, where the interaction between partially independent processes determines the glucose hypometabolism.

Secondary prevention of Alzheimer's dementia: neuroimaging contributions

BACKGROUND

In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.

MAIN BODY

Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.

CONCLUSION

We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.