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Frontiers in Endocrinology 2021Type 2 diabetes mellitus (T2DM) is often accompanied by cognitive decline and depressive symptoms. Numerous diffusion tensor imaging (DTI) studies revealed... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Type 2 diabetes mellitus (T2DM) is often accompanied by cognitive decline and depressive symptoms. Numerous diffusion tensor imaging (DTI) studies revealed microstructural white matter (WM) abnormalities in T2DM but the findings were inconsistent. The present study aimed to conduct a coordinate-based meta-analysis (CBMA) to identify statistical consensus of DTI studies in T2DM.
METHODS
We performed a systematic search on relevant studies that reported fractional anisotropy (FA) differences between T2DM patients and healthy controls (HC). The anisotropic effect size seed-based d mapping (AES-SDM) approach was used to explore WM alterations in T2DM. A meta-regression was then used to analyze potential influences of sample characteristics on regional FA changes.
RESULTS
A total of eight studies that comprised 245 patients and 200 HC, along with 52 coordinates were extracted. The meta-analysis identified FA reductions in three clusters including the left inferior network, the corpus callosum (CC), and the left olfactory cortex. Besides, FA in the CC was negatively correlated with body mass index (BMI) in the patients group.
CONCLUSIONS
T2DM could lead to subtle WM microstructural alterations, which might be associated with cognitive deficits or emotional distress symptoms. This provides a better understanding of the pathophysiology of neurodegeneration and complications in T2DM.
SYSTEMATIC REVIEW REGISTRATION
Registered at PROSPERO (http://www.crd.york.ac.uk/PROSPERO), registration number: CRD42020218737.
Topics: Adolescent; Adult; Anisotropy; Body Mass Index; Cognition; Corpus Callosum; Diabetes Mellitus, Type 2; Diffusion Tensor Imaging; Female; Humans; Male; Middle Aged; White Matter; Young Adult
PubMed: 34012420
DOI: 10.3389/fendo.2021.658198 -
Frontiers in Aging Neuroscience 2020Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's...
Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's disease (PD); however, the results were inconsistent. Therefore, a meta-analytical approach was used in this study to find the most prominent and replicable WM abnormalities of PD. Online databases were systematically searched for all TBSS studies comparing fractional anisotropy (FA) between patients with PD and controls. Subsequently, we performed the meta-analysis using a coordinate-based meta-analytic software called seed-based d mapping. Meanwhile, meta-regression was performed to explore the potential correlation between the alteration of FA and the clinical characteristics of PD. Out of a total of 1,701 studies that were identified, 23 studies were included. Thirty datasets, including 915 patients (543 men) with PD and 836 healthy controls (449 men), were included in the current study. FA reduction was identified in the body of the corpus callosum (CC; 245 voxels; = -1.739; < 0.001) and the left inferior fronto-occipital fasciculus (IFOF) 118 voxels; = -1.182; < 0.001). Both CC and IFOF maintained significance in the sensitivity analysis. No increase in FA was identified, but the percentage of male patients with PD was positively associated with the value of FA in the body of the CC. Although some limitations exist, DTI is regarded as a valid way to identify the pathophysiology of PD. It could be more beneficial to integrate DTI parameters with other MRI techniques to explore brain degeneration in PD.
PubMed: 33584244
DOI: 10.3389/fnagi.2020.610962 -
NeuroImage Apr 2021Recent years have seen an increased understanding of the importance of myelination in healthy brain function and neuropsychiatric diseases. Non-invasive microstructural... (Meta-Analysis)
Meta-Analysis
Recent years have seen an increased understanding of the importance of myelination in healthy brain function and neuropsychiatric diseases. Non-invasive microstructural magnetic resonance imaging (MRI) holds the potential to expand and translate these insights to basic and clinical human research, but the sensitivity and specificity of different MR markers to myelination is a subject of debate. To consolidate current knowledge on the topic, we perform a systematic review and meta-analysis of studies that validate microstructural imaging by combining it with myelin histology. We find meta-analytic evidence for correlations between various myelin histology metrics and markers from different MRI modalities, including fractional anisotropy, radial diffusivity, macromolecular pool, magnetization transfer ratio, susceptibility and longitudinal relaxation rate, but not mean diffusivity. Meta-analytic correlation effect sizes range widely, between R = 0.26 and R = 0.82. However, formal comparisons between MRI-based myelin markers are limited by methodological variability, inconsistent reporting and potential for publication bias, thus preventing the establishment of a single most sensitive strategy to measure myelin with MRI. To facilitate further progress, we provide a detailed characterisation of the evaluated studies as an online resource. We also share a set of 12 recommendations for future studies validating putative MR-based myelin markers and deploying them in vivo in humans.
Topics: Brain; Humans; Magnetic Resonance Imaging; Myelin Proteins; Myelin Sheath; Qualitative Research; Reproducibility of Results
PubMed: 33524576
DOI: 10.1016/j.neuroimage.2021.117744 -
Clinical and Translational Imaging 2020Diffusion tensor magnetic resonance imaging (DTI) characterises tissue microstructure and provides proxy measures of myelination, axon diameter, fibre density and... (Review)
Review
PURPOSE
Diffusion tensor magnetic resonance imaging (DTI) characterises tissue microstructure and provides proxy measures of myelination, axon diameter, fibre density and organisation. This may be valuable in the assessment of the roots of the brachial plexus in health and disease. Therefore, there is a need to define the normal DTI values.
METHODS
The literature was systematically searched for studies of asymptomatic adults who underwent DTI of the brachial plexus. Participant characteristics, scanning protocols, and measurements of the fractional anisotropy (FA) and mean diffusivity (MD) of each spinal root were extracted by two independent review authors. Generalised linear modelling was used to estimate the effect of experimental conditions on the FA and MD. Meta-analysis of root-level estimates was performed using Cohen's method with random effects.
RESULTS
Nine articles, describing 316 adults (1:1 male:female) of mean age 35 years (SD 6) were included. Increments of ten diffusion sensitising gradient directions reduced the mean FA by 0.01 (95% CI 0.01, 0.03). Each year of life reduced the mean MD by 0.03 × 10 mm/s (95% CI 0.01, 0.04). At 3-T, the pooled mean FA of the roots was 0.36 (95% CI 0.34, 0.38; 98%). The pooled mean MD of the roots was 1.51 × 10 mm/s (95% CI 1.45, 1.56; 99%).
CONCLUSIONS
The FA and MD of the roots of the brachial plexus vary according to experimental conditions and participant factors. We provide summary estimates of the normative values in different conditions which may be valuable to researchers and clinicians alike.
PubMed: 33282795
DOI: 10.1007/s40336-020-00393-x -
Brain and Behavior Feb 2021To identify the most prominent and replicable fractional anisotropy (FA) alterations of white matter associated with obsessive-compulsive disorder (OCD) in tract-based... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To identify the most prominent and replicable fractional anisotropy (FA) alterations of white matter associated with obsessive-compulsive disorder (OCD) in tract-based spatial statistics (TBSS) studies.
METHODS
We reviewed previous TBSS studies (n = 20) in OCD and performed a meta-analysis (n = 16) of FA differences.
RESULTS
No between-group differences in FA were detected in the pooled meta-analysis. However, reduced FA was identified in the genu and anterior body of corpus callosum (CC) in adult OCD. FA reductions in the anterior body of CC were associated with a later age of onset in adult patients with OCD. For pediatric OCD, decreased FA in earlier adolescence and increased FA in later adolescence were seemingly related to an altered trajectory of brain maturation.
CONCLUSIONS
Absent in the pooled sample but robust in adults, disrupted microstructural organization in the anterior part of CC indicates a bias of deficits toward connections in interhemispheric connections of rostral neocortical regions, which could lead to deficits of interhemispheric communication and thus contribute to cognitive and emotional deficits in adult OCD. The correlation between FA in the anterior body of CC and older illness onset suggests that patients with later adult onset of illness may represent a biologically distinct subgroup. For pediatric OCD, alterations in neurodevelopmental maturation may contribute to inconsistent patterns of FA alteration relative to controls during adolescence. While most studies of OCD have emphasized alterations of within hemisphere fronto-striatal circuits, these results indicate that between hemisphere connectivity of this circuitry may also represent important pathophysiology of the illness.
Topics: Adolescent; Adult; Anisotropy; Brain; Child; Diffusion Tensor Imaging; Humans; Obsessive-Compulsive Disorder; White Matter
PubMed: 33270358
DOI: 10.1002/brb3.1975 -
Frontiers in Neurology 2020[This corrects the article DOI: 10.3389/fneur.2020.531993.].
[This corrects the article DOI: 10.3389/fneur.2020.531993.].
PubMed: 33193071
DOI: 10.3389/fneur.2020.612069 -
Frontiers in Neurology 2020Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional... (Review)
Review
Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.
PubMed: 33101169
DOI: 10.3389/fneur.2020.531993 -
NeuroImage. Clinical 2020Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to... (Review)
Review
Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer's Disease. This paper aims to systematically review studies that examined the effect of Alzheimer's risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer's Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer's disease.
Topics: Alzheimer Disease; Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; White Matter
PubMed: 32758801
DOI: 10.1016/j.nicl.2020.102359 -
Neuroinformatics Jan 2021Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of...
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .
Topics: Aged; Aged, 80 and over; Alzheimer Disease; Brain; Diffusion Magnetic Resonance Imaging; Female; Humans; Image Interpretation, Computer-Assisted; Machine Learning; Male
PubMed: 32524428
DOI: 10.1007/s12021-020-09469-5 -
Frontiers in Neurology 2020Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be...
Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.
PubMed: 32477235
DOI: 10.3389/fneur.2020.00314