Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

Fixel-Based Analysis Reveals Tau-Related White Matter Changes in Early Stages of Alzheimer's Disease

Several studies have shown white matter (WM) abnormalities in Alzheimer's disease (AD) using diffusion tensor imaging (DTI). Nonetheless, robust characterization of WM changes has been challenging due to the methodological limitations of DTI. We applied fixel-based analyses (FBA) to examine microscopic differences in fiber density (FD) and macroscopic changes in fiber cross-section (FC) in early stages of AD (N = 393, 212 females). FBA was also compared with DTI, free-water corrected (FW)-DTI and diffusion kurtosis imaging (DKI). We further investigated the correlation of FBA and tensor-derived metrics with AD pathology and cognition. FBA metrics were decreased in the entire cingulum bundle, uncinate fasciculus and anterior thalamic radiations in Aβ-positive patients with mild cognitive impairment compared to control groups. Metrics derived from DKI, and FW-DTI showed similar alterations whereas WM degeneration detected by DTI was more widespread. Tau-PET uptake in medial temporal regions was only correlated with reduced FC mainly in the parahippocampal cingulum in Aβ-positive individuals. This tau-related WM alteration was also associated with impaired memory. Despite the spatially extensive between-group differences in DTI-metrics, the link between WM and tau aggregation was only revealed using FBA metrics implying high sensitivity but low specificity of DTI-based measures in identifying subtle tau-related WM degeneration. No relationship was found between amyloid load and any diffusion-MRI measures. Our results indicate that early tau-related WM alterations in AD are due to macrostructural changes specifically captured by FBA metrics. Thus, future studies assessing the effects of AD pathology in WM tracts should consider using FBA metrics.

Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models

Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

Introduction

Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry.

Methods

Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure.

Results

In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia.

Discussion

We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

ComBating inter-site differences in field strength: harmonizing preclinical traumatic brain injury MRI data

Integrating datasets from multiple sites and scanners can increase statistical power for neuroimaging studies but can also introduce significant inter-site confounds. We evaluated the effectiveness of ComBat, an empirical Bayes approach, to combine longitudinal preclinical MRI data acquired at 4.7 or 9.4 T at two different sites in Australia. Male Sprague Dawley rats underwent MRI on Days 2, 9, 28, and 150 following moderate/severe traumatic brain injury (TBI) or sham injury as part of Project 1 of the NIH/NINDS-funded Centre Without Walls EpiBioS4Rx project. Diffusion-weighted and multiple-gradient-echo images were acquired, and outcomes included QSM, FA, and ADC. Acute injury measures including apnea and self-righting reflex were consistent between sites. Mixed-effect analysis of ipsilateral and contralateral corpus callosum (CC) summary values revealed a significant effect of site on FA and ADC values, which was removed following ComBat harmonization. Bland-Altman plots for each metric showed reduced variability across sites following ComBat harmonization, including for QSM, despite appearing to be largely unaffected by inter-site differences and no effect of site observed. Following harmonization, the combined inter-site data revealed significant differences in the imaging metrics consistent with previously reported outcomes. TBI resulted in significantly reduced FA and increased susceptibility in the ipsilateral CC, and significantly reduced FA in the contralateral CC compared with sham-injured rats. Additionally, TBI rats also exhibited a reversal in ipsilateral CC ADC values over time with significantly reduced ADC at Day 9, followed by increased ADC 150 days after injury. Our findings demonstrate the need for harmonizing multi-site preclinical MRI data and show that this can be successfully achieved using ComBat while preserving phenotypical changes due to TBI.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Purpose

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

Approach

As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.

Results

ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.

Conclusion

Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

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.

Cortical microstructural associations with CSF amyloid and pTau

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage by design.

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum

BACKGROUND

White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations.

METHODS

We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥ 55 years, including 276 cognitively normal older adults (CN), 142 with mild cognitive impairment (MCI), and 87 AD patients, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University.

RESULTS

Compared to CN, AD and MCI subjects showed significantly higher RD, MD, and AxD values (all p-values < 0.001) and significantly lower FA values (left p ≤ 0.002, right p ≤ 0.015) after Bonferroni adjustment for multiple comparisons. Most tests of cognition and mood (p < 0.001) as well as higher medial temporal amyloid burden (p < 0.001) were associated with poorer WM integrity in the CBH after Bonferroni adjustment.

CONCLUSION

These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease

BACKGROUND

Alzheimer's disease is a neurodegenerative condition associated with the accumulation of two misfolded proteins, amyloid-beta (A[Formula: see text]) and tau. We study their effect on neuronal activity, with the aim of assessing their individual and combined impact.

METHODS

We use a whole-brain dynamic model to find the optimal parameters that best describe the effects of A[Formula: see text] and tau on the excitation-inhibition balance of the local nodes.

RESULTS

We found a clear dominance of A[Formula: see text] over tau in the early disease stages (MCI), while tau dominates over A[Formula: see text] in the latest stages (AD). We identify crucial roles for A[Formula: see text] and tau in complex neuronal dynamics and demonstrate the viability of using regional distributions to define models of large-scale brain function in AD.

CONCLUSIONS

Our study provides further insight into the dynamics and complex interplay between these two proteins, opening the path for further investigations on biomarkers and candidate therapeutic targets in-silico.

APOE4 poses opposite effects of plasma LDL on white matter integrity in older adults

INTRODUCTION

APOE4 is a strong genetic risk factor of Alzheimer's disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown.

MEHODS

In the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging.

RESULTS

The increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]).

DISCUSSION

The results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

Multi-spectral diffusion MRI mega-analysis in genetic generalized epilepsy: Relation to outcomes

BACKGROUND AND OBJECTIVES

Genetic generalized epilepsy (GGE) is the most common form of generalized epilepsy. Although individual patients with GGE typically present without structural alterations, group differences have been demonstrated in GGE and some GGE subtypes like juvenile myoclonic epilepsy (GGE-JME). Previous studies usually involved only small cohorts from single centers and therefore could not assess imaging markers of multiple GGE subtypes.

METHODS

We performed a diffusion MRI mega-analysis in 192 participants consisting of 126 controls and 66 patients with GGE from four different cohorts and two different epilepsy centers. We applied whole-brain multi-site harmonization and analyzed fractional anisotropy (FA), as well as mean, radial and axial diffusivity (MD/RD/AD) to assess differences between controls, patients with GGE and the common GGE subtypes, i.e. GGE with generalized tonic-clonic seizures only (GGE-GTCS), GGE-JME and absence epilepsy (GGE-AE). We also analyzed relationships with patients' response to anti-seizure-medication (ASM).

RESULTS

Relative to controls, we identified decreased anisotropy and increased RD in patients with GGE. We found no significant effects of disease duration, age of onset or seizure frequency on diffusion metrics. Patients with JME had increased MD and RD when compared to controls, while patients with GGE-GTCS showed decreased MD/AD when compared to controls. Compared to patients with GGE-AE, patients with GGE-GTCS had lower AD/MD. Compared to patients with GGE-GTCS, patients with GGE-JME had higher MD/RD and AD. Moreover, we found lower FA in patients with refractory when compared to patients with non-refractory GGE in the right cortico-spinal tract, but no significant differences in patients with active versus controlled epilepsy.

DISCUSSION

We provide evidence that clinically defined GGE as a whole and GGE-subtypes harbor marked microstructural differences detectable with diffusion MRI. Moreover, we found an association between microstructural changes and treatment resistance. Our findings have important implications for future full-resolution multi-site studies when assessing GGE, its subtypes and ASM refractoriness.

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers

Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.

Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting

White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.

Application of data harmonization and tract-based spatial statistics reveals white matter structural abnormalities in pediatric patients with focal cortical dysplasia

Our study assessed diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) in pediatric subjects with epilepsy secondary to Focal Cortical Dysplasia (FCD) to improve our understanding of structural network changes associated with FCD related epilepsy. We utilized a data harmonization (DH) approach to minimize confounding effects induced by MRI protocol differences. We also assessed correlations between DTI metrics and neurocognitive measures of the fluid reasoning index (FRI), verbal comprehension index (VCI), and visuospatial index (VSI). Data (n = 51) from 23 FCD patients and 28 typically developing controls (TD) scanned clinically on either 1.5T, 3T, or 3T-wide-bore MRI were retrospectively analyzed. Tract-based spatial statistics (TBSS) with threshold-free cluster enhancement and permutation testing with 100,000 permutations were used for statistical analysis. To account for imaging protocol differences, we employed non-parametric data harmonization prior to permutation testing. Our analysis demonstrates that DH effectively removed MRI protocol-based differences typical in clinical acquisitions while preserving group differences in DTI metrics between FCD and TD subjects. Furthermore, DH strengthened the association between DTI metrics and neurocognitive indices. Fractional anisotropy, MD, and RD metrics showed stronger correlation with FRI and VSI than VCI. Our results demonstrate that DH is an integral step to reduce the confounding effect of MRI protocol differences during the analysis of white matter tracts and highlights biological differences between FCD and healthy control subjects. Characterization of white matter changes associated with FCD-related epilepsy may better inform prognosis and treatment approaches.

Cortical microstructural associations with CSF amyloid and pTau

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI, in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8±6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+/- participants. Conversely, cortical thickness was more tightly linked with pTau181. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI measures sensitive to early AD pathogenesis and microstructural damage may elucidate mechanisms underlying cognitive decline.

White matter integrity is associated with cognition and amyloid burden in older adult Koreans along the Alzheimer's disease continuum

BACKGROUND

White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations.

METHODS

We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged ≥55 years, including 276 cognitively normal older adults (CN), 142 mild cognitive impairment (MCI), and 87 AD, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University.

RESULTS

Compared to CN, AD and MCI subjects showed decreased WM integrity in the bilateral CBH. Cognition, mood, and higher amyloid burden were also associated with poorer WM integrity in the CBH.

CONCLUSION

These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.

N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI-1H-MRS Pilot Study

We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (¹H-MRS). The regions of interest (ROIs) for ¹H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.

Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory

In human Alzheimer's disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.

Twelve Weeks of Intermittent Caloric Restriction Diet Mitigates Neuroinflammation in Midlife Individuals with Multiple Sclerosis: A Pilot Study with Implications for Prevention of Alzheimer's Disease

BACKGROUND

Multiple sclerosis (MS) is a prototype neuroinflammatory disorder with increasingly recognized role for neurodegeneration. Most first-line treatments cannot prevent the progression of neurodegeneration and the resultant disability. Interventions can improve symptoms of MS and might provide insights into the underlying pathology.

OBJECTIVE

To investigate the effect of intermittent caloric restriction on neuroimaging markers of MS.

METHODS

We randomized ten participants with relapsing remitting MS to either a 12-week intermittent calorie restriction (iCR) diet (n = 5) or control (n = 5). Cortical thickness and volumes were measured through FreeSurfer, cortical perfusion was measured by arterial spin labeling and neuroinflammation through diffusion basis spectrum imaging.

RESULTS

After 12 weeks of iCR, brain volume increased in the left superior and inferior parietal gyri (p: 0.050 and 0.049, respectively) and the banks of the superior temporal sulcus (p: 0.01). Similarly in the iCR group, cortical thickness improved in the bilateral medial orbitofrontal gyri (p: 0.04 and 0.05 in right and left, respectively), the left superior temporal gyrus (p: 0.03), and the frontal pole (p: 0.008) among others. Cerebral perfusion decreased in the bilateral fusiform gyri (p: 0.047 and 0.02 in right and left, respectively) and increased in the bilateral deep anterior white matter (p: 0.03 and 0.013 in right and left, respectively). Neuroinflammation, demonstrated through hindered and restricted water fractions (HF and RF), decreased in the left optic tract (HF p: 0.02), and the right extreme capsule (RF p: 0.007 and HF p: 0.003).

CONCLUSION

These pilot data suggest therapeutic effects of iCR in improving cortical volume and thickness and mitigating neuroinflammation in midlife adults with MS.

Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition

Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.

Sample size requirement for achieving multisite harmonization using structural brain MRI features

When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.

Unraveling schizophrenia replicable functional connectivity disruption patterns across sites

Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal-based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site-effect correction methods in removing the site-related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site-related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo-connectivity within sensory areas (Net1), hypo-connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper-connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p < .05). Finally, the three subnet-specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out-of-sample public data set (Z = -1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet-specific composite FC measures might be replicable neuroimaging markers for schizophrenia.

White matter microstructure shows sex differences in late childhood: Evidence from 6797 children

Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.

Microstructural alterations measured by diffusion tensor imaging following transcatheter aortic valve replacement and their association with cerebral ischemic injury and cognitive function - a prospective study

PURPOSE

We assessed diffusion tensor imaging (DTI) metric changes of the corpus callosum and cingulum correlated to postprocedural ischemic lesion load (ILL) and cognitive performance in transcatheter aortic valve replacement (TAVR).

METHODS

TAVR subjects had DTI post-TAVR (≤ 8 days) and at 6 months (78 participants, males 56%, age 78.8 years ± 6.3) and four neurocognitive tests (pre-TAVR, post-TAVR, 6 months, 1 year). DTI metrics (fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD)) were calculated for 7 regions: corpus callosum (genu, body, splenium) and cingulum (cingulate gyrus, parahippocampal cingulum bilaterally). DTI metrics post-TAVR and at 6 months were compared with Student's t-test (p < 0.0071) and ANOVA covarying for sex, ILL (p < 0.05) with post hoc analysis of ILL groups (p < 0.0167). Repeated-measures linear mixed-effect model (p < 0.05) was performed to investigate the effect of time and ILL on cognition.

RESULTS

At 6 months, significant decrease of the following DTI metrics was detected: AD (genu, body, splenium, right parahippocampal cingulum: p ≤ 0.0046); MD (body, both cingulate gyri: p ≤ 0.0050); RD (left cingulate gyrus: p = 0.0021); FA (splenium: p < 0.0001). ANOVA confirmed significant effect of female sex on AD + MD reduction (body, right cingulate gyrus) and AD reduction (left cingulate gyrus) (p ≤ 0.0254). Significant negative effect of ILL on some DTI metric changes was found (AD + MD-body: p ≤ 0.0050; MD-left cingulate gyrus: p = 0.0087). Cognitive performance remained stable with significant negative correlation of ILL and retrograde memory and visual scores (p ≤ 0.0483).

CONCLUSION

Significant effect of TAVR on cerebral microstructural integrity was found with reduced diffusivities opposite to the trends reported in various neurodegenerative conditions/ageing, notably in women and lower ILL, and with preserved/improved cognition.

TRIAL REGISTRATION NUMBER

NCT02826200 at ClinicalTrials.gov; date of registration: 07. July 2016.

Hypothalamic microstructure and function are related to body mass, but not mental or cognitive abilities across the adult lifespan

Physical, mental, and cognitive resources are essential for healthy aging. Aging impacts on the structural integrity of various brain regions, including the hippocampus. Even though recent rodent studies hint towards a critical role of the hypothalamus, there is limited evidence on functional consequences of age-related changes of this region in humans. Given its central role in metabolic regulation and affective processing and its connections to the hippocampus, it is plausible that hypothalamic integrity and connectivity are associated with functional age-related decline. We used data of n = 369 participants (18-88 years) from the Cambridge Centre for Ageing and Neuroscience repository to determine functional impacts of potential changes in hypothalamic microstructure across the lifespan. First, we identified age-related changes in microstructure as a function of physical, mental, and cognitive health and compared those findings to changes in hippocampal microstructure. Second, we investigated the relationship of hypothalamic microstructure and resting-state functional connectivity and related those changes to age as well as physical health. Our results showed that hypothalamic microstructure is not affected by depressive symptoms (mental health), cognitive performance (cognitive health), and comparatively stable across the lifespan, but affected by body mass (physical health). Furthermore, body mass changes connectivity to limbic regions including the hippocampus, amygdala, and nucleus accumbens, suggesting functional alterations in the metabolic and reward systems. Our results demonstrate that hypothalamic structure and function are affected by body mass, focused on neural density and dispersion, but not inflammation. Still, observed effect sizes were small, encouraging detailed investigations of individual hypothalamic subunits.

FiberNeat: Unsupervised White Matter Tract Filtering

Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised white matter tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. We performed quantitative comparisons with expertly delineated tracts. We ran FiberNeat on 131 participants' data from the ADNI3 dataset. We show that applying FiberNeat as a filtering step after bundle segmentation improves the quality of extracted tracts and helps improve tractometry.

Multisite Harmonization of Structural DTI Networks in Children: An A-CAP Study

The analysis of large, multisite neuroimaging datasets provides a promising means for robust characterization of brain networks that can reduce false positives and improve reproducibility. However, the use of different MRI scanners introduces variability to the data. Managing those sources of variability is increasingly important for the generation of accurate group-level inferences. ComBat is one of the most promising tools for multisite (multiscanner) harmonization of structural neuroimaging data, but no study has examined its application to graph theory metrics derived from the structural brain connectome. The present work evaluates the use of ComBat for multisite harmonization in the context of structural network analysis of diffusion-weighted scans from the Advancing Concussion Assessment in Pediatrics (A-CAP) study. Scans were acquired on six different scanners from 484 children aged 8.00–16.99 years [Mean = 12.37 ± 2.34 years; 289 (59.7%) Male] ~10 days following mild traumatic brain injury (n = 313) or orthopedic injury (n = 171). Whole brain deterministic diffusion tensor tractography was conducted and used to construct a 90 x 90 weighted (average fractional anisotropy) adjacency matrix for each scan. ComBat harmonization was applied separately at one of two different stages during data processing, either on the (i) weighted adjacency matrices (matrix harmonization) or (ii) global network metrics derived using unharmonized weighted adjacency matrices (parameter harmonization). Global network metrics based on unharmonized adjacency matrices and each harmonization approach were derived. Robust scanner effects were found for unharmonized metrics. Some scanner effects remained significant for matrix harmonized metrics, but effect sizes were less robust. Parameter harmonized metrics did not differ by scanner. Intraclass correlations (ICC) indicated good to excellent within-scanner consistency between metrics calculated before and after both harmonization approaches. Age correlated with unharmonized network metrics, but was more strongly correlated with network metrics based on both harmonization approaches. Parameter harmonization successfully controlled for scanner variability while preserving network topology and connectivity weights, indicating that harmonization of global network parameters based on unharmonized adjacency matrices may provide optimal results. The current work supports the use of ComBat for removing multiscanner effects on global network topology.

Sex-Specific Causes and Consequences of White Matter Damage in a Middle-Aged Cohort

Objective

To evaluate potential sex-specific effects of multiple cardiovascular risk factors on white matter pathology in normal aging men and women, as well as potential sex-differences in the association of white matter pathology and cognitive functions.

Methods

We analyzed cross-sectional data of 581 participants (median age: 53 years, 54% women) of the population-based cohort of the BiDirect Study who completed clinical examinations, five neuropsychological tests, and an 3T MRI examination. White matter pathology was determined by the extent of white matter hyperintensities (WMH) on FLAIR images as well as the magnitude of global fractional anisotropy (FA) based on diffusion tensor imaging. Main effects, interaction as well as sex-stratified generalized linear regression models were used to evaluate the moderating effect of sex on the association of hypertension, diabetes mellitus, smoking, and obesity with WMH and FA, respectively. Associations of imaging markers with cognitive test results were determined with linear regression models.

Results

Hypertension showed stronger associations with more extensive WMH and less FA in women compared to men. Current smoking was associated with more severe WMH in women only. Adjusted for age and education, WMH were not significantly associated with cognitive tests, but higher FA was associated with better performance in motor function in both sexes and with executive functions in men, even after adjustment for cardiovascular risk factors.

Conclusion

We observed a stronger association of hypertension and smoking with white matter damage in women, suggesting a higher susceptibility for vascular pathology in women. However, there was no association of WMH with cognition, and FA was associated with executive function tests only in men, suggesting a higher cognitive reserve in women.

Potential Diffusion Tensor Imaging Biomarkers for Elucidating Intra-Individual Age-Related Changes in Cognitive Control and Processing Speed

Cognitive aging, especially cognitive control, and processing speed aging have been well-documented in the literature. Most of the evidence was reported based on cross-sectional data, in which inter-individual age effects were shown. However, there have been some studies pointing out the possibility of overlooking intra-individual changes in cognitive aging. To systematically examine whether age-related differences and age-related changes might yield distinctive patterns, this study directly compared cognitive control function and processing speed between different cohorts versus follow-up changes across the adult lifespan. Moreover, considering that cognitive aging has been attributed to brain disconnection in white matter (WM) integrity, this study focused on WM integrity via acquiring diffusion-weighted imaging data with an MRI instrument that are further fitted to a diffusion tensor model (i.e., DTI) to detect water diffusion directionality (i.e., fractional anisotropy, FA; mean diffusivity, MD; radial diffusivity, RD; axial diffusivity, AxD). Following data preprocessing, 114 participants remained for further analyses in which they completed the two follow-up sessions (with a range of 1-2 years) containing a series of neuropsychology instruments and computerized cognitive control tasks. The results show that many significant correlations between age and cognitive control functions originally shown on cross-sectional data no longer exist on the longitudinal data. The current longitudinal data show that MD, RD, and AxD (especially in the association fibers of anterior thalamic radiation) are more strongly correlated to follow-up aging processes, suggesting that axonal/myelin damage is a more robust phenomenon for observing intra-individual aging processes. Moreover, processing speed appears to be the most prominent cognitive function to reflect DTI-related age (cross-sectional) and aging (longitudinal) effects. Finally, converging the results from regression analyses and mediation models, MD, RD, and AxD appear to be the representative DTI measures to reveal age-related changes in processing speed. To conclude, the current results provide new insights to which indicator of WM integrity and which type of cognitive changes are most representative (i.e., potentially to be neuroimaging biomarkers) to reflect intra-individual cognitive aging processes.

Intellectual Structure and Emerging Trends of White Matter Hyperintensity Studies: A Bibliometric Analysis From 2012 to 2021

White matter hyperintensities (WMHs), which have a significant effect on human health, have received increasing attention since their number of publications has increased in the past 10 years. We aimed to explore the intellectual structure, hotspots, and emerging trends of publications on WMHs using bibliometric analysis from 2012 to 2021. Publications on WMHs from 2012 to 2021 were retrieved from the Web of Science Core Collection. CiteSpace 5.8.R3, VOSviewer 1.6.17, and an online bibliometric analysis platform (Bibliometric. com) were used to quantitatively analyze the trends of publications from multiple perspectives. A total of 29,707 publications on WMHs were obtained, and the number of annual publications generally increased from 2012 to 2021. Neurology had the most publications on WMHs. The top country and institution were the United States and Harvard University, respectively. Massimo Filippi and Stephen M. Smith were the most productive and co-cited authors, respectively. Thematic concentrations primarily included cerebral small vessel disease, diffusion magnetic resonance imaging (dMRI), schizophrenia, Alzheimer’s disease, multiple sclerosis, microglia, and oligodendrocyte. The hotspots were clustered into five groups: white matter and diffusion tensor imaging, inflammation and demyelination, small vessel disease and cognitive impairment, MRI and multiple sclerosis, and Alzheimer’s disease. Emerging trends mainly include deep learning, machine learning, perivascular space, convolutional neural network, neurovascular unit, and neurite orientation dispersion and density imaging. This study presents an overview of publications on WMHs and provides insights into the intellectual structure of WMH studies. Our study provides information to help researchers and clinicians quickly and comprehensively understand the hotspots and emerging trends within WMH studies as well as providing direction for future basic and clinical studies on WMHs.

Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years

BACKGROUND

The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk.

OBJECTIVE

Predicted age difference (PAD) was proposed to predict CDR change.

METHODS

All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction.

RESULTS

PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up.

CONCLUSION

Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

In vivo Analysis of Normal Optic Nerve in an Elderly Population Using Diffusion Magnetic Resonance Imaging Tractography

Purpose: To quantitatively investigate the microstructural properties of the optic nerve (ON) in vivo using diffusion magnetic resonance imaging (dMRI) tractography in an elderly population and to determine the differences between the ON diffusion properties stratified by basic demographics. Methods: We measured fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the intraorbital ON in cognitively normal controls selected from the Alzheimer's Disease Neuroimaging Initiative 3 database (n =104; mean age = 73. 8 ± 8.1 years) using dMRI probabilistic tractography and evaluated the correlation between diffusion parameters and demographic factors. Diffusion parameters were measured in 20 equidistant nodes along the tract, and the data from proximal 70% (14 nodes) of the intraorbital ON were averaged. Results: The mean FA of the intraorbital ON was 0.392 ± 0.063, and the mean MD was 1.163 ± 0.165 μm²/s. The mean RD was 0.882 ± 0.152 μm²/s, and the mean AD was 1.693 ± 0.183 μm²/s. The multiple linear regression model showed a negative correlation between FA and age. FA in females was significantly higher than males, whereas RD in female was significantly lower. Conclusions: We measured the diffusion properties of the intraorbital ON using dMRI tractography in an elderly cognitively normal population. The diffusion properties detected by dMRI tractography may substantially reflect the microstructure of the ON.

Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study

A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.

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.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Associations Between Amyloid and Tau Pathology, and Connectome Alterations, in Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

The roles of amyloid-β and tau in the degenerative process of Alzheimer's disease (AD) remain uncertain. [18F]AV-45 and [18F]AV-1451 PET quantify amyloid-β and tau pathology, respectively, while diffusion tractography enables detection of their microstructural consequences.

OBJECTIVE

Examine the impact of amyloid-β and tau pathology on the structural connectome and cognition, in mild cognitive impairment (MCI) and AD.

METHODS

Combined [18F]AV-45 and [18F]AV-1451 PET, diffusion tractography, and cognitive assessment in 28 controls, 32 MCI, and 26 AD patients.

RESULTS

Hippocampal connectivity was reduced to the thalami, right lateral orbitofrontal, and right amygdala in MCI; alongside the insula, posterior cingulate, right entorhinal, and numerous cortical regions in AD (all p < 0.05). Hippocampal strength inversely correlated with [18F]AV-1451 SUVr in MCI (r = -0.55, p = 0.049) and AD (r = -0.57, p = 0.046), while reductions in hippocampal connectivity to ipsilateral brain regions correlated with increased [18F]AV-45 SUVr in those same regions in MCI (r = -0.33, p = 0.003) and AD (r = -0.31, p = 0.006). Cognitive scores correlated with connectivity of the right temporal pole in MCI (r = -0.60, p = 0.035) and left hippocampus in AD (r = 0.69, p = 0.024). Clinical Dementia Rating Scale scores correlated with [18F]AV-1451 SUVr in multiple areas reflecting Braak stages I-IV, including the right (r = 0.65, p = 0.004) entorhinal cortex in MCI; and Braak stages III-VI, including the right (r = 0.062, p = 0.009) parahippocampal gyrus in AD.

CONCLUSION

Reductions in hippocampal connectivity predominate in the AD connectome, correlating with hippocampal tau in MCI and AD, and with amyloid-β in the target regions of those connections. Cognitive scores correlate with microstructural changes and reflect the accumulation of tau pathology.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

PURPOSE

Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document.

METHODS

The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses.

RESULTS

Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets.

CONCLUSIONS

The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data

•

RVI for MDD and AD was derived based on large meta-analytical findings.

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RVI-MDD and AD were significantly elevated in UKBB subjects with respective illnesses.

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There was no elevation of RVI-MDD in subjects with AD or RVI-AD in subjects with MDD.

•

RVI captures neuroanatomic deviation patterns.

•

RVI is a useful biomarker for assessing similarity to neuropsychiatric illnesses.

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10⁻⁸), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10⁻⁵) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

A White Matter Connection of Schizophrenia and Alzheimer's Disease

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer's disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter "regional vulnerability index" (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen's d = 0.44, P = 1·10-5, N = 173 patients/230 control) and replication (Cohen's d = 0.78, P = 9·10-7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P < .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients.

Plasma β-Amyloid Levels Associated With Structural Integrity Based on Diffusion Tensor Imaging in Subjective Cognitive Decline: The SILCODE Study

Background: Accumulating evidence has demonstrated that plasma β-amyloid (Aβ) levels are useful biomarkers to reflect brain amyloidosis and gray matter structure, but little is known about their correlation with subclinical white matter (WM) integrity in individuals at risk of Alzheimer's disease (AD). Here, we investigated the microstructural changes in WM between subjects with low and high plasma Aβ levels among individuals with subjective cognitive decline (SCD).

Methods: This study included 142 cognitively normal individuals with SCD who underwent a battery of neuropsychological tests, plasma Aβ measurements, and diffusion tensor imaging (DTI) based on the Sino Longitudinal Study on Cognitive Decline (SILCODE). Using tract-based spatial statistics (TBSS), we compared fractional anisotropy (FA), and mean diffusivity (MD) in WM between subjects with low (N = 71) and high (N = 71) plasma Aβ levels (cut-off: 761.45 pg/ml for Aβ40 and 10.74 pg/ml for Aβ42).

Results: We observed significantly decreased FA and increased MD in the high Aβ40 group compared to the low Aβ40 group in various regions, including the body, the genu, and the splenium of the corpus callosum; the superior longitudinal fasciculus; the corona radiata; the thalamic radiation; the external and internal capsules; the inferior fronto-occipital fasciculus; and the sagittal stratum [p < 0.05, familywise error (FWE) corrected]. Average FA values were associated with poor performance on executive and memory assessments. No significant differences were found in either MD or FA between the low and high Aβ42 groups.

Conclusion: Our results suggest that a correlation exists between WM integrity and plasma Aβ40 levels in individuals with SCD.

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.

Apparent propagator anisotropy from single-shell diffusion MRI acquisitions

PURPOSE

The apparent propagator anisotropy (APA) is a new diffusion MRI metric that, while drawing on the benefits of the ensemble averaged propagator anisotropy (PA) compared to the fractional anisotropy (FA), can be estimated from single-shell data.

THEORY AND METHODS

Computation of the full PA requires acquisition of large datasets with many diffusion directions and different b-values, and results in extremely long processing times. This has hindered adoption of the PA by the community, despite evidence that it provides meaningful information beyond the FA. Calculation of the complete propagator can be avoided under the hypothesis that a similar sensitivity/specificity may be achieved from apparent measurements at a given shell. Assuming that diffusion anisotropy (DiA) is nondependent on the b-value, a closed-form expression using information from one single shell (ie, b-value) is reported.

RESULTS

Publicly available databases with healthy and diseased subjects are used to compare the APA against other anisotropy measures. The structural information provided by the APA correlates with that provided by the PA for healthy subjects, while it also reveals statistically relevant differences in white matter regions for two pathologies, with a higher reliability than the FA. Additionally, APA has a computational complexity similar to the FA, with processing-times several orders of magnitude below the PA.

CONCLUSIONS

The APA can extract more relevant white matter information than the FA, without any additional demands on data acquisition. This makes APA an attractive option for adoption into existing diffusion MRI analysis pipelines.