Diffusion Tensor Imaging With Tract-Based Spatial Statistics Reveals White Matter Abnormalities in Patients With Vascular Cognitive Impairment

Cognitive impairment mediates the white matter injury load and gait disorders in subcortical ischemic vascular disease

Gait disorders are common in patients with subcortical ischemic vascular disease (SIVD). We aim to explore the impact of white matter (WM) damage on gait disorders in SIVD. 21 SIVD patients and 20 normal controls (NC) were included in the study. Montreal Cognitive Assessment (MoCA) was used to evaluate general cognition, while Speed-Accuracy Trade-Off (SAT) was used to assess executive function. Gait velocity, cadence, and stride length were measured. Diffusion Tensor Imaging (DTI) data were analyzed using Tract-Based Spatial Statistics (TBSS) and Peak Width of Skeletonized Mean Diffusivity (PSMD). The relationships among WM damage, gait disorders, and cognitive function were examined through mediation analysis. SIVD scored lower than NC in MoCA and SAT tests (P < 0.001). Gait velocity and stride length were decreased in SIVD. SIVD had lower PSMD (P < 0.001). PSMD correlated with gait parameters, which were totally mediated by MoCA and partially mediated by SAT. The fractional anisotropy (FA) and mean diffusivity (MD) of the genu of the corpus callosum (GCC) and body of CC (BCC) were correlated with gait parameters. The FA of the bilateral anterior corona radiata (ACR) was positively correlated with gait parameters, while the MD of the bilateral superior corona radiata (SCR), bilateral superior longitudinal fasciculus (SLF), and left external capsule (EC) were negatively correlated with them (P < 0.05). Gait impairments in SIVD were associated with cognitive deficits. Cognitive impairment mediated the WM damage and gait disorders. The microstructural alterations of CC, SLF, EC, and CR may be related to changes in gait.

Microstructural Abnormalities in the Contralateral Normal-appearing White Matter of Glioblastoma Patients Evaluated with Advanced Diffusion Imaging

PURPOSE

Glioblastoma patients develop recurrence in the opposite hemisphere far from the primary tumor site even after complete resection. This is one of the main reasons for short disease survival. Our aim in this study is to detect microstructural changes in the contralateral hemisphere of glioblastoma patients using different diffusion models with the fully automated tract-based spatial statistics (TBSS) method.

METHODS

Fourteen right-sided and eleven left-sided glioblastoma patients without any treatment and eighteen age- and gender-matched controls were included in the study. Multi-shell diffusion weighted images were created with a 3T MRI device. After various preprocessing steps, images of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), axial kurtosis (AK), mean kurtosis (MK), radial kurtosis (RK), intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic water fraction (ISO) were obtained. TBSS was used to compare diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging parameters of right- and left-sided glioblastoma patients with the control group for the contralateral hemisphere.

RESULTS

Both right-sided and left-sided glioblastoma patients have shown an increase in MD and ODI in the contralateral hemisphere. While right-sided glioblastoma patients showed an increase in RD, AD, and ISO in a more limited area in the contralateral hemisphere, left-sided glioblastoma patients showed an increase in MK and AK. FA, ICVF, and RK did not show any difference in both groups.

CONCLUSION

There are microstructural changes in the contralateral hemisphere in glioblastoma patients, and these changes differ between right-sided and left-sided glioblastoma patients.

Altered Brain Structure in Hemifacial Spasm Patients: A Multimodal Brain Structure Study

Objective

Hemifacial spasm (HFS) is a clinical neurosurgical disease, which brain structural alterations caused by HFS remain a topic of debate. We evaluated changes in brain microstructure associated with HFS and observed their relevance to clinical characteristics.

Methods

We enrolled 72 participants. T1-weighted structural and diffusion tensor images were collected from all participants using 3.0T magnetic resonance equipment. Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to identify changes in gray matter volume (GMV) and disruptions in white matter (WM) integrity. The severity of the spasms was graded using the Cohn scale.

Results

VBM analysis revealed that the GMV was significantly reduced in the left Thalamus and increased GMV in the right Cerebellum IV-V of the HFS group. TBSS analysis showed that FA in the left superior longitudinal fasciculus (SLF) of the HFS group was significantly increased. GMV in the thalamus showed a negative correlation with disease duration and Cohn grade, while FA in the left SLF had a positive correlation with both the disease duration and Cohn grade.

Conclusion

We identified regions with altered GMV in HFS patients. Additionally, we determined that FA in the left SLF might serve as a significant neural indicator of HFS.

Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study

BACKGROUND

Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear.

PURPOSE

To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event.

STUDY TYPE

Retrospective.

POPULATION

Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years).

FIELD STRENGTH/SEQUENCE

3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event.

ASSESSMENT

Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure.

STATISTICAL TESTS

Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected).

RESULTS

TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR.

DATA CONCLUSION

In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

An Update on Parkinson's Disease and its Neurodegenerative Counterparts

INTRODUCTION

Neurodegenerative disorders are a group of diseases that cause nerve cell degeneration in the brain, resulting in a variety of symptoms and are not treatable with drugs. Parkinson's disease (PD), prion disease, motor neuron disease (MND), Huntington's disease (HD), spinal cerebral dyskinesia (SCA), spinal muscle atrophy (SMA), multiple system atrophy, Alzheimer's disease (AD), spinocerebellar ataxia (SCA) (ALS), pantothenate kinase-related neurodegeneration, and TDP-43 protein disorder are examples of neurodegenerative diseases. Dementia is caused by the loss of brain and spinal cord nerve cells in neurodegenerative diseases.

BACKGROUND

Even though environmental and genetic predispositions have also been involved in the process, redox metal abuse plays a crucial role in neurodegeneration since the preponderance of symptoms originates from abnormal metal metabolism.

METHOD

Hence, this review investigates several neurodegenerative diseases that may occur symptoms similar to Parkinson's disease to understand the differences and similarities between Parkinson's disease and other neurodegenerative disorders based on reviewing previously published papers.

RESULTS

Based on the findings, the aggregation of alpha-synuclein occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Other neurodegenerative diseases occur with different protein aggregation or mutations.

CONCLUSION

We can conclude that Parkinson's disease, Multiple system atrophy, and Dementia with Lewy bodies are closely related. Therefore, researchers must distinguish among the three diseases to avoid misdiagnosis of Multiple System Atrophy and Dementia with Lewy bodies with Parkinson's disease symptoms.

Multimodal magnetic resonance imaging on brain structure and function changes in vascular cognitive impairment without dementia

Vascular cognitive impairment not dementia (VCIND) is one of the three subtypes of vascular cognitive impairment (VCI), with cognitive dysfunction and symptoms ranging between normal cognitive function and vascular dementia. The specific mechanisms underlying VCIND are still not fully understood, and there is a lack of specific diagnostic markers in clinical practice. With the rapid development of magnetic resonance imaging (MRI) technology, structural MRI (sMRI) and functional MRI (fMRI) have become effective methods for exploring the neurobiological mechanisms of VCIND and have made continuous progress. This article provides a comprehensive overview of the research progress in VCIND using multimodal MRI, including sMRI, diffusion tensor imaging, resting-state fMRI, and magnetic resonance spectroscopy. By integrating findings from these multiple modalities, this study presents a novel perspective on the neuropathological mechanisms underlying VCIND. It not only highlights the importance of multimodal MRI in unraveling the complex nature of VCIND but also lays the foundation for future research examining the relationship between brain structure, function, and cognitive impairment in VCIND. These new perspectives and strategies ultimately hold the potential to contribute to the development of more effective diagnostic tools and therapeutic interventions for VCIND.

Divergent brain regional atrophy and associated fiber disruption in amnestic and non-amnestic MCI

BACKGROUND

Understanding the pathological characteristics of various mild cognitive impairment (MCI) subtypes is crucial for the differential diagnosis of dementia. The purpose of this study was to feature divergent symptom-deficit profiles in amnestic MCI (aMCI) and non-amnestic MCI (naMCI).

METHODS

T1 and DTI MRI data from a total of 158 older adults with 50 normal controls, 56 aMCI, and 52 naMCI were included. The voxel-wise gray matter volumes and the number of seed-based white matter fiber bundles were compared among these three groups. Furthermore, correlation and mediation analyses between the neuroimaging indices and cognitive measures were performed.

RESULTS

The aMCI with specific memory abnormalities was characterized by volumetric atrophy of the left hippocampus but not by damage in the linked white matter fiber bundles. Conversely, naMCI was characterized by both the altered volume of the right inferior frontal gyrus and the significant damage to fiber bundles traversing the region in all three directions, not only affecting fibers around the atrophied area but also distant fibers. Mediation analyses of gray matter-white matter-cognition showed that gray matter atrophy affects the number of fiber bundles and further affects attention and executive function. Meanwhile, fiber bundle damage also affects gray matter volume, which further affects visual processing and language.

CONCLUSIONS

The divergent structural damage patterns of the MCI subtypes and cognitive dysfunctions highlight the importance of detailed differential diagnoses in the early stages of pathological neurodegenerative diseases to deepen the understanding of dementia subtypes and inform targeted early clinical interventions.

Quantification of Diffusion Magnetic Resonance Imaging for Prognostic Prediction of Neonatal Hypoxic-Ischemic Encephalopathy

Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurological sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional magnetic resonance imaging (MRI). DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measurements in the corpus callosum, thalamus, basal ganglia, corticospinal tract, and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication.

Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices

BACKGROUND

Cognitive dysfunction is the most common non-motor symptom in Parkinson's disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups.

METHODS

We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman's rank correlation coefficient (LDHs) and Kendall's coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values.

RESULTS

The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features.

CONCLUSIONS

More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

Lung function and risk of incident dementia: A prospective cohort study of 431,834 individuals

BACKGROUND

Whether lung function prospectively affects cognitive brain health independent of their overlapping factors remains largely unknown. This study aimed to investigate the longitudinal association between decreased lung function and cognitive brain health and to explore underlying biological and brain structural mechanisms.

METHODS

This population-based cohort included 43,1834 non-demented participants with spirometry from the UK Biobank. Cox proportional hazard models were fitted to estimate the risk of incident dementia for individuals with low lung function. Mediation models were regressed to explore the underlying mechanisms driven by inflammatory markers, oxygen-carrying indices, metabolites, and brain structures.

FINDINGS

During a follow-up of 3,736,181 person-years (mean follow-up 8.65 years), 5,622 participants (1.30 %) developed all-cause dementia, which consisted of 2,511 Alzheimer's dementia (AD) and 1,308 Vascular Dementia (VD) cases. Per unit decrease in lung function measure was each associated with increased risk for all-cause dementia (forced expiratory volume in 1 s [liter]: hazard ratio [HR, 95 %CI], 1.24 [1.14-1.34], P = 1.10 × 10^-07; forced vital capacity [liter]: 1.16 [1.08-1.24], P = 2.04 × 10^-05; peak expiratory flow [liter/min]: 1.0013 [1.0010-1.0017], P = 2.73 × 10^-13). Low lung function generated similar hazard estimates for AD and VD risks. As underlying biological mechanisms, systematic inflammatory markers, oxygen-carrying indices, and specific metabolites mediated the effects of lung function on dementia risks. Besides, brain grey and white matter patterns mostly affected in dementia were substantially changed with lung function.

INTERPRETATION

Life-course risk for incident dementia was modulated by individual lung function. Maintaining optimal lung function is useful for healthy aging and dementia prevention.

Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment

Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.

Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

Aim

Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls.

Methods

Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers.

Results

Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09.

Conclusion

These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.

Association between Changes in White Matter Microstructure and Cognitive Impairment in White Matter Lesions

This study investigated the characteristics of cognitive impairment in patients with white matter lesions (WMLs) caused by cerebral small vessel disease and the corresponding changes in WM microstructures. Diffusion tensor imaging (DTI) data of 50 patients with WMLs and 37 healthy controls were collected. Patients were divided into vascular cognitive impairment non-dementia and vascular dementia groups. Tract-based spatial statistics showed that patients with WMLs had significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values throughout the WM areas but predominately in the forceps minor, forceps major (FMA), bilateral corticospinal tract, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, inferior longitudinal fasciculus (ILF), and anterior thalamic radiation, compared to the control group. These fiber bundles were selected as regions of interest. There were significant differences in the FA, MD, AD, and RD values (p < 0.05) between groups. The DTI metrics of all fiber bundles significantly correlated with the Montreal Cognitive Assessment (p < 0.05), with the exception of the AD values of the FMA and ILF. Patients with WMLs showed changes in diffusion parameters in the main WM fiber bundles. Quantifiable changes in WM microstructure are the main pathological basis of cognitive impairment, and may serve as a biomarker of WMLs.

Altered Microstructural Changes Detected by Diffusion Kurtosis Imaging in Patients With Cognitive Impairment After Acute Cerebral Infarction

Objective

To detect the microstructural changes in patients with cognitive impairment after acute cerebral infarction using diffusion kurtosis imaging (DKI).

Materials and Methods

A total of 70 patients with acute cerebral infarction were divided into two groups: 35 patients with cognitive impairment (VCI group), and 35 patients without cognitive impairment (N-VCI group), according to mini-mental state examination (MMSE) score. Healthy individuals (n = 36) were selected as the normal control (NORM) group. DKI parameters from 28 different brain regions of interest (ROIs) were selected, measured, and compared.

Results

VCI group patients had significantly higher mean diffusion (MD) and significantly lower mean kurtosis (MK) values in most ROIs than those in the N-VCI and NORM groups. DKI parameters in some ROIs correlated significantly with MMSE score. The splenium of corpus callosum MD was most correlated with MMSE score, the correlation coefficient was −0.652, and this parameter had good ability to distinguish patients with VCI from healthy controls; at the optimal cut-off MD value (0.9915), sensitivity was 91.4%, specificity 100%, and the area under the curve value 0.964.

Conclusions

Pathological changes in some brain regions may underlie cognitive impairment after acute cerebral infarction, especially the splenium of corpus callosum. These preliminary results suggest that, in patients with VCI, DKI may be useful for assessing microstructural tissue damage.

Reduced Inter-Voxel Whiter Matter Integrity in Subjective Cognitive Decline: Diffusion Tensor Imaging With Tract-Based Spatial Statistics Analysis

Subjective cognitive decline (SCD), a self-reported worsening in cognition concurrent with normal performance on standardized neuropsychological tests, has gained much attention due to its high risks in the development of mild cognitive impairments or Alzheimer’s disease. The existing cross-sectional diffusion tensor imaging (DTI) studies in SCD have shown extremely controversial findings. Furthermore, all of these studies investigated diffusion properties within the voxel, such as fractional anisotropy, mean diffusivity, or axial diffusivity (DA). However, it remains unclear whether individuals with SCD demonstrate alterations of diffusion profile between voxels and their neighbors, as indexed by local diffusion homogeneity (LDH). We selected 30 healthy controls (HCs) and 23 SCD subjects to acquire their whole-brain DTI. Diffusion images were compared using the tract-based spatial statistics method. Diffusion indices with significant between-group tract clusters were extracted from each individual for further region-of-interest (ROI)-based comparisons. Our results showed that subjects with SCD demonstrated reduced LDH in the left superior frontal gyrus (SFG) and DA in the right anterior cingulate cortex compared with the HC group. In contrast, the SCD group showed higher LDH values in the left lingual gyrus (LG) compared with the HC group. Notably, LDH in the left SFG was significantly and negatively correlated with LDH in the left LG. In conclusion, white matter (WM) integrity in the left SFG, right ACC, and left LG is altered in SCD, suggesting that individuals with SCD exhibit detectable changes in WM tracts before they demonstrate objective cognitive deficits.

Reduced White Matter Integrity in Patients With End-Stage and Non-end-Stage Chronic Kidney Disease: A Tract-Based Spatial Statistics Study

Background and Purpose: Reduced white matter (WM) integrity has been implicated in chronic kidney disease (CKD), especially in end-stage renal disease (ESRD). However, whether the differences in WM abnormalities exist in ESRD and non-end-stage CKD (NES-CKD) remains unclear. Hence, this study aimed to investigate the WM microstructural changes between the two stages using diffusion tensor imaging (DTI) and explore the related influencing factors.

Methods: Diffusion tensor imaging’ images were prospectively acquired from 18 patients with ESRD, 22 patients with NES-CKD, and 19 healthy controls (HCs). Tract-based spatial statistics (TBSS) was performed to assess the voxel-wise differences in WM abnormalities among the three groups. The relationships between DTI parameters and biochemical data were also analyzed.

Results: Compared with NES-CKDs, FA value was significantly decreased, and AD value increased in ESRDs mainly in brain regions of bilateral anterior thalamic radiation (ATR), the genu and body of corpus callosum (CC), bilateral anterior corona radiata, superior corona radiata, and superior longitudinal fasciculus. Besides, extensive and symmetrical deep WM damages were observed in patients with ESRD, accompanied by increased MD and RD values. Multiple regression analysis revealed that uric acid and serum phosphorus level can be used as independent predictors of WM microstructural abnormalities in clusters with statistical differences in DTI parameters between ESRD and NES-CKD groups.

Conclusion: In the progression of CKD, patients with ESRD have more severe WM microstructural abnormalities than NES-CKDs, and this progressive deterioration may be related to uric acid and phosphate levels.

Association between diffusion tensor imaging findings and domain-specific cognitive impairment in cerebral small vessel disease: a protocol for systematic review and meta-analysis

INTRODUCTION

Cognitive impairment is the main clinical manifestation of cerebral small vessel disease (CSVD). However, the mechanism and structural damage in different domains of cognitive disorders are poorly understood. There is an urgent need to quantify the relation between diffusion tensor imaging (DTI) data and impaired cognitive testing in CSVD, which may help to find biomarkers for early diagnosis or treatment evaluation. We aim to summarise the understanding of association between DTI findings and domain-specific cognitive impairment.

METHODS AND ANALYSIS

PubMed, EMBASE, Web of science, Cochrane library, Chinese National Knowledge Infrastructure Databases, Wanfang, SinoMed and VIP will be searched, from 1 January 1994 to 1 August 2021. The ClinicalTrials.gov and Chictr.org.cn records will also be searched to identify further potential studies. The included studies should report fractional anisotropy and/or and mean diffusivity/apparent diffusion coefficient data for one or more individual regions of interest in DTI analysis. Meanwhile, cognitive testing scores are also needed. This systematic review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The quality of cohort or case-control studies will be evaluated by the Newcastle-Ottawa Scale, and the cross-section studies will be evaluated by Agency for Healthcare Research and Quality scale. Meta-analysis, subgroup and sensitivity analyses, and publication bias will be all performed with Stata.

ETHICS AND DISSEMINATION

Patients and the public will not be involved in this study. The existing data from published studies will be used. The findings from this research will be relevant information regarding the association of DTI metrics with cognitive disorder, which will be published in a peer-reviewed journal. If we need to amend this protocol, we will give the date of each amendment, describe the change and give the rationale. Changes will not be incorporated into the protocol.

PROSPERO REGISTRATION NUMBER

CRD42021226133.

The Contribution of White Matter Diffusion and Cortical Perfusion Pathology to Vascular Cognitive Impairment: A Multimode Imaging-Based Machine Learning Study

Widespread impairments in white matter and cerebrovascular integrity have been consistently implicated in the pathophysiology of patients with small vessel disease (SVD). However, the neural circuit mechanisms that underlie the developing progress of clinical cognitive symptoms remain largely elusive. Here, we conducted cross-modal MRI scanning including diffusion tensor imaging and arterial spin labeling in a cohort of 113 patients with SVD, which included 74 patients with vascular mild cognitive impairment (vMCI) and 39 patients without vMCI symptoms, and hence developed multimode imaging-based machine learning models to identify markers that discriminated SVD subtypes. Diffusion and perfusion features, respectively, extracted from individual white matter and gray matter regions were used to train three sets of classifiers in a nested 10-fold fashion: diffusion-based, perfusion-based, and combined diffusion-perfusion-based classifiers. We found that the diffusion-perfusion combined classifier achieved the highest accuracy of 72.57% with leave-one-out cross-validation, with the diffusion features largely spanning the capsular lateral pathway of the cholinergic tracts, and the perfusion features mainly distributed in the frontal-subcortical-limbic areas. Furthermore, diffusion-based features within vMCI group were associated with performance on executive function tests. We demonstrated the superior accuracy of using diffusion-perfusion combined multimode imaging features for classifying vMCI subtype out of a cohort of patients with SVD. Disruption of white matter integrity might play a critical role in the progression of cognitive impairment in patients with SVD, while malregulation of coritcal perfusion needs further study.

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

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

White Matter Structural Network Analysis to Differentiate Alzheimer's Disease and Subcortical Ischemic Vascular Dementia

To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (Eg), and local efficiency (Eloc)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.

Loss of Integrity of Corpus Callosum White Matter Hyperintensity Penumbra Predicts Cognitive Decline in Patients With Subcortical Vascular Mild Cognitive Impairment

Loss of white matter (WM) integrity contributes to subcortical vascular mild cognitive impairment (svMCI). Diffusion tensor imaging (DTI) has revealed damage beyond the area of WM hyperintensity (WMH) including in normal-appearing WM (NAWM); however, the functional significance of this observation is unclear. To answer this question, in this study we investigated the relationship between microstructural changes in the WMH penumbra (WMH-P) and cognitive function in patients with svMCI by regional tract-based analysis. A total of 111 patients with svMCI and 72 patients with subcortical ischemic vascular disease (SIVD) without cognitive impairment (controls) underwent DTI and neuropsychological assessment. WMH burden was determined before computing mean values of fractional anisotropy (FA) and mean diffusivity (MD) within WMHs and WMH-Ps. Pearson’s partial correlations were used to assess the relationship between measurements showing significant intergroup differences and composite Z-scores representing global cognitive function. Multiple linear regression analysis was carried out to determine the best model for predicting composite Z-scores. We found that WMH burden in the genu, body, and splenium of the corpus callosum (GCC, BCC, and SCC respectively); bilateral anterior, superior, and posterior corona radiata; left sagittal stratum was significantly higher in the svMCI group than in the control group (p < 0.05). The WMH burden of the GCC, BCC, SCC, and bilateral anterior corona radiata was negatively correlated with composite Z-scores. Among diffusion parameters showing significant differences across the 10 WM regions, mean FA values of WMH and WMH-P of the BCC were correlated with composite Z-scores in svMCI patients. The results of the multiple linear regression analysis showed that the FA of WMH-P of the BCC and WMH burden of the SCC and GCC were independent predictors of composite Z-score, with the FA of WMH-P of the BCC making the largest contribution. These findings indicate that disruption of the CC microstructure—especially the WMH-P of the BCC—may contribute to the cognitive deficits associated with SIVD.

Alterations and Associations Between Magnetic Susceptibility of the Basal Ganglia and Diffusion Properties in Alzheimer's Disease

This study adopted diffusion tensor imaging to detect alterations in the diffusion parameters of the white matter fiber in Alzheimer’s disease (AD) and used quantitative susceptibility mapping to detect changes in magnetic susceptibility. However, whether the changes of susceptibility values due to excessive iron in the basal ganglia have correlations with the alterations of the diffusion properties of the white matter in patients with AD are still unknown. We aim to investigate the correlations among magnetic susceptibility values of the basal ganglia, diffusion indexes of the white matter, and cognitive function in patients with AD. Thirty patients with AD and nineteen healthy controls (HCs) were recruited. Diffusion indexes of the whole brain were detected using tract-based spatial statistics. The caudate nucleus, putamen, and globus pallidus were selected as regions of interest, and their magnetic susceptibility values were measured. Compared with HCs, patients with AD showed that there were significantly increased axial diffusivity (AxD) in the internal capsule, superior corona radiata (SCR), and right anterior corona radiata (ACR); increased radial diffusivity (RD) in the right anterior limb of the internal capsule, ACR, and genu of the corpus callosum (GCC); and decreased fractional anisotropy (FA) in the right ACR and GCC. The alterations of RD values, FA values, and susceptibility values of the right caudate nucleus in patients with AD were correlated with cognitive scores. Besides, AxD values in the right internal capsule, ACR, and SCR were positively correlated with the magnetic susceptibility values of the right caudate nucleus in patients with AD. Our findings revealed that the magnetic susceptibility of the caudate nucleus may be an MRI-based biomarker of the cognitive dysfunction of AD and abnormal excessive iron distribution in the basal ganglia had adverse effects on the diffusion properties of the white matter.

Altered white matter integrity in patients with monocular blindness: A diffusion tensor imaging and tract-based spatial statistics study

BACKGROUND

Visual deprivation can lead to abnormal and plastic changes in the brain's visual system and other systems. Although the secondary changes of gray matter in patients have been well studied, the study of white matter is rare. In fact, subtle changes in white matter may be revealed by diffusion tensor imaging, and tract-based spatial statistics can be used to analyze DTI image data.

PURPOSE

In the present study, diffusion tensor imaging (DTI) and tract-based spatial statistics (TBSS) were used to investigate abnormal structural changes in the white matter (WM) of patients with monocular blindness (MB).

METHODS

We recruited 16 healthy controls (HC) (fourteen males and two females) and 16 patients (fifteen males and one female) with right-eye blindness (without differences in left-eye vision). All patients were of similar age. Data acquisition was performed using magnetic resonance imaging (MRI) and DTI. Voxel-based whole brain comparisons of fractional anisotropy (FA) and radial diffusivity (RD) of WM fibers in patients and HC were performed using the TBSS method. The mean FA and RD values for altered brain regions in MB patients were analyzed via the receiver operating characteristic (ROC) curve. Correlation analysis was performed to investigate the relationships between the average FA (RD) value of the whole brain and anxiety score, depression score, and visual function questionnaire score in MB patients.

RESULTS

In MB patients, the mean FA of the whole brain was decreased versus HC. Moreover, the FA values of the corpus callosum, the corona radiata, the posterior thalamic radiation, and the right retrolenticular part of internal capsule were significantly decreased. In addition, the average RD value of the whole brain in MB patients was higher than that observed in HC. The mean FA and RD values of brain regions were analyzed using the ROC curve, and the results showed that the area under the ROC curve was more accurate. Furthermore, the average FA and RD values of the whole brain were significantly correlated with anxiety score, depression score, and visual function-related quality of life score.

CONCLUSION

DTI and TBSS may be useful in examining abnormal spontaneous alterations in the WM of MB patients. The observed changes in FA and RD values may imply the larvaceous neurological mechanism involved in MB.

Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Brain white matter abnormalities and correlation with severity in amyotrophic lateral sclerosis: An atlas-based diffusion tensor imaging study

OBJECTIVES

To assess microstructural alterations in white matter (WM) in amyotrophic lateral sclerosis (ALS) using diffusion tensor imaging (DTI).

METHODS

DTI data were collected from 34 subjects (18 patients with ALS and 16 healthy controls). The atlas-based region of interest (ROI) analysis was conducted to assess WM microstructure in ALS by combining intra-voxel metrics, which included fractional anisotropy (FA) and mean diffusivity (MD), and an inter-voxel metric, i.e., local diffusion homogeneity (LDH). Correlation analysis of diffusion values and clinical factors was also performed.

RESULTS

ALS group showed a significant FA reduction in bilateral corticospinal tract (CST) as well as right uncinate fasciculus (RUF). The areas with higher MD were situated in right corticospinal tract (RCST), left cingulum hippocampus (LCH), RUF, and right superior longitudinal fasciculus (RSLF). Additionally, ALS patients showed decreased LDH in bilateral anterior thalamic radiation (ATR), bilateral CST and left inferior frontal-occipital fasciculus (LIFOF). Significant correlations were observed between ALSFRS-R (revised ALS Functional Rating Scale) scores or progression rate and FA in bilateral CST, as well as between disease duration and LDH in right CST. Receiver operating characteristic (ROC) analysis revealed the feasibility of employing diffusion metrics along the CST to distinguish two groups (AUC = 0.792-0.868, p < .005 for all).

CONCLUSIONS

WM microstructural alteration is a common pathology in ALS, which can be detected by both intra- and inter-voxel diffusion metrics. The extent of abnormalities in several WM tracts such as ATR and LIFOF may be better assessed through the inter-voxel diffusion measurement.

Treatment of intracranial hemorrhage with neuroendoscopy guided by body surface projection

BACKGROUND

We aimed to study the feasibility of body surface projection in neuroendoscopic treatment of intracranial hemorrhage (ICH), and to evaluate the prognosis of muscle strength using diffusion tensor imaging (DTI) technique.

METHODS

We utilized 3D-SLICER software and adopted hematoma body surface projection orientation to eliminate ICH by using neuroendoscope for 69 cases of spontaneous intracerebral hemorrhage. The standard of correct location was determined by the direct view of hematoma at the first operation. Evacuation rate by comparing computed tomography (CT) before and after the surgery and Glasgow coma scale (GCS) was computed. DTI was used for pyramidal tract imaging 3 weeks after the operation, while the prognosis of muscle strength was assessed after 6 months. The control group included 69 patients with basal ganglia hemorrhage who received conservative treatment during the same period.

RESULTS

The hematoma evacuation rate was 90.75% in average. The average GCS score rose by 4 points one week after the surgery. The shape of pyramidal tract affected the prognosis of body muscle strength, and the simple disruption type was the worst. There was no difference in mortality between the surgery group (10.1%) and the conservative group (4.3%). The muscle strength improvement value and modulate RANK score (MRS) in the surgery group were better than the control group.

CONCLUSION

It is convenient and feasible to use the surface projection to determine the target of operation, and the clearance rate of hematoma is high. Pyramidal tract imaging can predict the prognosis of muscle strength.