Characterization of white matter changes along fibers by automated fiber quantification in the early stages of Alzheimer's disease

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Brain white matter microstructural alterations in patients with systemic lupus erythematosus: an automated fiber quantification study

This study aimed to identify damaged segments of brain white matter fiber tracts in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ), and analyze their relationship with cognitive impairment. Clinical and imaging data for 39 female patients with SLE and for 44 female healthy controls (HCs) were collected. AFQ was used to track whole-brain white matter tracts in each participant, and each tract was segmented into 100 equally spaced nodes. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated at each node. Correlations were also explored between DTI metrics in the damaged segments of white matter fiber tracts and neuropsychological test scores of patients with SLE. Compared with HCs, SLE patients exhibited significantly lower FA values, and significantly higher MD, AD, RD values in many white matter tracts (all P < 0.05, false discovery rate-corrected). FA values in nodes 97-100 of the left inferior fronto-occipital fasciculus (IFOF) positively correlated with the mini-mental state examination score. AFQ enables precise and accurate identification of damage to white matter fiber tracts in brains of patients with SLE. FA values in the left IFOF correlate with cognitive impairment in SLE.

Cortical thickness and white matter microstructure predict freezing of gait development in Parkinson's disease

The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.

White matter microstructure alterations in type 2 diabetes mellitus and its correlation with cerebral small vessel disease and cognitive performance

Microstructural abnormalities of white matter fiber tracts are considered as one of the etiology of diabetes-induced neurological disorders. We explored the cerebral white matter microstructure alteration accurately, and to analyze its correlation between cerebral small vessel disease (CSVD) burden and cognitive performance in type 2 diabetes mellitus (T2DM). The clinical-laboratory data, cognitive scores [including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), California verbal learning test (CVLT), and symbol digit modalities test (SDMT)], CSVD burden scores of the T2DM group (n = 34) and healthy control (HC) group (n = 21) were collected prospectively. Automatic fiber quantification (AFQ) was applied to generate bundle profiles along primary white matter fiber tracts. Diffusion tensor images (DTI) metrics and 100 nodes of white matter fiber tracts between groups were compared. Multiple regression analysis was used to analyze the relationship between DTI metrics and cognitive scores and CSVD burden scores. For fiber-wise and node-wise, DTI metrics in some commissural and association fibers were increased in T2DM. Some white matter fiber tracts DTI metrics were independent predictors of cognitive scores and CSVD burden scores. White matter fiber tracts damage in patients with T2DM may be characterized in specific location, especially commissural and association fibers. Aberrational specific white matter fiber tracts are associated with visuospatial function and CSVD burden.

White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics

The neuropathological mechanism of mild cognitive impairment (MCI) remains unclarified. Diffusion tensor imaging (DTI) studies revealed white matter (WM) microarchitecture alterations in MCI, but consistent findings and conclusions have not yet been drawn. The present coordinate-based meta-analysis (CBMA) of tract-based spatial statistics (TBSS) studies aimed to identify the most prominent and robust WM abnormalities in patients with MCI. A systematic search of relevant studies was conducted through January 2022 to identify TBSS studies comparing fractional anisotropy (FA) between MCI patients and healthy controls (HC). We used the seed-based d mapping (SDM) software to achieve the CBMA and analyze regional FA alterations in MCI. Meta-regression analysis was subsequently applied to explore the potential associations between clinical variables and FA changes. MCI patients demonstrated significantly decreased FA in widely distributed areas in the corpus callosum (CC), including the genu, body, and splenium of the CC, as well as one cluster in the left striatum. FA in the body of the CC and in three clusters in the splenium of the CC was negatively associated with the mean age. Additionally, FA in the genu of the CC and in three clusters in the splenium of the CC had negative correlations with the MMSE scores. Disrupted integrities of the CC and left striatum might play vital roles in the process of cognitive decline. These findings enhanced our understanding of the neural mechanism underlying WM neurodegeneration in MCI and provided perspectives for the early detection and intervention of dementia.Registration number: CRD42022235716.

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

Microstructure abnormalities of the diffusion quantities in children with attention-deficit/hyperactivity disorder: an AFQ and TBSS study

Objective

To explore the specific alterations of white matter microstructure in children with attention-deficit/hyperactivity disorder (ADHD) by automated fiber quantification (AFQ) and tract-based spatial statistics (TBSS), and to analyze the correlation between white matter abnormality and impairment of executive function.

Methods

In this prospective study, a total of twenty-seven patients diagnosed with ADHD (20 males, 7 females; mean age of 8.89 ± 1.67 years) and twenty-two healthy control (HC) individuals (11 males, 11 females, mean age of 9.82 ± 2.13 years) were included. All participants were scanned with diffusion kurtosis imaging (DKI) and assessed for executive functions. AFQ and TBSS analysis methods were used to investigate the white matter fiber impairment of ADHD patients, respectively. Axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD) and fractional anisotropy (FA) of 17 fiber properties were calculated using the AFQ. The mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), mean diffusivity (MDDKI), axial diffusivity (ADDKI), radial diffusivity (RDDKI) and fractional anisotropy (FADKI) of DKI and AD, RD, MD, and FA of diffusion tensor imaging (DTI) assessed the integrity of the white matter based on TBSS. Partial correlation analyses were conducted to evaluate the correlation between white matter abnormalities and clinical test scores in ADHD while taking age, gender, and education years into account. The analyses were all family-wise error rate (FWE) corrected.

Results

ADHD patients performed worse on the Behavior Rating Inventory of Executive Function (BRIEF) test (p < 0.05). Minor variances existed in gender and age between ADHD and HC, but these variances did not yield statistically significant distinctions. There were no significant differences in TBSS for DKI and DTI parameters (p > 0.05, TFCE-corrected). Compared to HC volunteers, the mean AD value of right cingulum bundle (CB_R) fiber tract showed a significantly higher level in ADHD patients following the correction of FWE. As a result of the point-wise comparison between groups, significant alterations (FWE correction, p < 0.05) were mainly located in AD (nodes 36-38, nodes 83-97) and MD (nodes 92-95) of CB_R. There was no significant correlation between white matter diffusion parameters and clinical test scores in ADHD while taking age, gender, and education years into account.

Conclusion

The AFQ method can detect ADHD white matter abnormalities in a specific location with greater sensitivity, and the CB_R played a critical role. Our findings may be helpful in further studying the relationship between focal white matter abnormalities and ADHD.

Characteristics of white matter alterations along fibres in patients with bulimia nervosa: A combined voxelwise and tractography study

Accumulating evidence supports the hypothesis that white matter (WM) abnormalities are involved in the pathophysiology of bulimia nervosa (BN); however, findings from in vivo neuroimaging studies have been inconsistent. We aimed to investigate the possible brain WM alterations, including WM volume and microstructure, in patients with BN. We recruited 43 BN patients and 31 healthy controls (HCs). All participants underwent structural and diffusion tensor imaging. Differences in WM volume and microstructure were evaluated using voxel-based morphometry, tract-based spatial statistics, and automated fibre quantification analysis. Compared with HCs, BN patients showed significantly decreased fractional anisotropy in the middle part of the corpus callosum (nodes 31-32) and increased mean diffusivity in the right cranial nerve V (CN V) (nodes 27-33 and nodes 55-88) and vertical occipital fasciculus (VOF) (nodes 58-85). Moreover, we found decreased axial diffusivity in the right inferior fronto-occipital fasciculus (node 67) and increased radial diffusivity in the CN V (nodes 22-34 and nodes 52-89) and left VOF (nodes 60-66 and nodes 81-85). Meanwhile, WM microstructural changes were correlated with patients' clinical manifestations. We did not find any significant differences in WM volume and the main WM fibre bundle properties between BN patients and HCs. Taken together, these findings provide that BN shows significant brain WM reorganization, but primarily in microstructure (part of WM fibre bundle), which is not sufficient to cause changes in WM volume. The automated fibre quantification analysis could be more sensitive to detect the subtle pathological changes in a point or segment of the WM fibre bundle.

Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging

White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.

Myelin in Alzheimer's disease: culprit or bystander?

Alzheimer's disease (AD) is a neurodegenerative disorder with neuronal and synaptic losses due to the accumulation of toxic amyloid β (Αβ) peptide oligomers, plaques, and tangles containing tau (tubulin-associated unit) protein. While familial AD is caused by specific mutations, the sporadic disease is more common and appears to result from a complex chronic brain neuroinflammation with mitochondriopathies, inducing free radicals' accumulation. In aged brain, mutations in DNA and several unfolded proteins participate in a chronic amyloidosis response with a toxic effect on myelin sheath and axons, leading to cognitive deficits and dementia. Αβ peptides are the most frequent form of toxic amyloid oligomers. Accumulations of misfolded proteins during several years alters different metabolic mechanisms, induce chronic inflammatory and immune responses with toxic consequences on neuronal cells. Myelin composition and architecture may appear to be an early target for the toxic activity of Aβ peptides and others hydrophobic misfolded proteins. In this work, we describe the possible role of early myelin alterations in the genesis of neuronal alterations and the onset of symptomatology. We propose that some pathophysiological and clinical forms of the disease may arise from structural and metabolic disorders in the processes of myelination/demyelination of brain regions where the accumulation of non-functional toxic proteins is important. In these forms, the primacy of the deleterious role of amyloid peptides would be a matter of questioning and the initiating role of neuropathology would be primarily the fact of dysmyelination.

Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer's disease.

OBJECTIVE

We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans.

METHODS

Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans' baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores' differences from baseline to follow-up.

RESULTS

Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size.

CONCLUSIONS

PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer's disease pathology based on amyloid and tau PET.

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.

White Matter Characteristics of Damage Along Fiber Tracts in Patients with Type 2 Diabetes Mellitus

Purpose

The white matter (WM) of the brain of type 2 diabetes mellitus (T2DM) patients is susceptible to neurodegenerative processes, but the specific types and positions of microstructural lesions along the fiber tracts remain unclear.

Methods

In this study 61 T2DM patients and 61 healthy controls were recruited and underwent diffusion spectrum imaging (DSI). The results were reconstructed with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). WM microstructural abnormalities were identified using tract-based spatial statistics (TBSS). Pointwise WM tract differences were detected through automatic fiber quantification (AFQ). The relationships between WM tract abnormalities and clinical characteristics were explored with partial correlation analysis.

Results

TBSS revealed widespread WM lesions in T2DM patients with decreased fractional anisotropy and axial diffusivity and an increased orientation dispersion index (ODI). The AFQ results showed microstructural abnormalities in T2DM patients in specific portions of the right superior longitudinal fasciculus (SLF), right arcuate fasciculus (ARC), left anterior thalamic radiation (ATR), and forceps major (FMA). In the right ARC of T2DM patients, an aberrant ODI was positively correlated with fasting insulin and insulin resistance, and an abnormal intracellular volume fraction was negatively correlated with fasting blood glucose. Additionally, negative associations were found between blood pressure and microstructural abnormalities in the right ARC, left ATR, and FMA in T2DM patients.

Conclusion

Using AFQ, together with DTI and NODDI, various kinds of microstructural alterations in the right SLF, right ARC, left ATR, and FMA can be accurately identified and may be associated with insulin and glucose status and blood pressure in T2DM patients.

Alterations in White Matter Fiber Tracts Characterized by Automated Fiber-Tract Quantification and Their Correlations With Cognitive Impairment in Neuromyelitis Optica Spectrum Disorder Patients

Objectives

To investigate whether patients with neuromyelitis optica spectrum disorder (NMOSD) have tract-specific alterations in the white matter (WM) and the correlations between the alterations and cognitive impairment.

Materials and Methods

In total, 40 patients with NMOSD and 20 healthy controls (HCs) who underwent diffusion tensor imaging (DTI) scan and neuropsychological scale assessments were enrolled. Automated fiber-tract quantification (AFQ) was applied to identify and quantify 100 equally spaced nodes of 18 specific WM fiber tracts for each participant. Then the group comparisons in DTI metrics and correlations between different DTI metrics and neuropsychological scales were performed.

Results

Regardless of the entire or pointwise level in WM fiber tracts, patients with NMOSD exhibited a decreased fractional anisotropy (FA) in the left inferior fronto-occipital fasciculus (L_IFOF) and widespread increased mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD), especially for the thalamic radiation (TR), corticospinal tract (CST), IFOF, inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) [p < 0.05, false discovery rate (FDR) correction], and the pointwise analyses performed more sensitive. Furthermore, the negative correlations among MD, AD, RD, and symbol digit modalities test (SDMT) scores in the left TR (L_TR) were found in NMOSD.

Conclusion

Patients with NMOSD exhibited the specific nodes of WM fiber tract damage, which can enhance our understanding of WM microstructural abnormalities in NMOSD. In addition, the altered DTI metrics were correlated with cognitive impairment, which can be used as imaging markers for the early identification of NMOSD cognitive impairment.

Tract Specific White Matter Lesion Load Affects White Matter Microstructure and Their Relationships With Functional Connectivity and Cognitive Decline

White matter hyperintensities (WMHs) are associated with cognitive decline. Assessing the effect of WMH on WM microstructural changes and its relationships with structural and functional connectivity to multiple cognitive domains are helpful to better understand the pathophysiological processes of cognitive impairment. 65 participants (49 normal and 16 MCI subjects, age: 67.4 ± 8.3 years, 44 females) were studied at 3T. The WMHs and fifty fiber tracts were automatically segmented from the T1/T2-weighted images and diffusion-weighted images, respectively. Tract-profiles of WMH were compared with those of apparent fiber density (AFD). The relationship between AFD and tract connectivity (TC) was assessed. Functional connectivity (FC) between tract ends obtained from resting-state functional MRI was examined in relation to TC. Tract-specific relationships of WMH, TC and FC with a multi-domain neuropsychological test battery and Montreal Cognitive Assessment (MoCA) were also separately assessed by lasso linear regression. Indirect pathways of TC and FC between WMH and multiple cognitive measures were tested using the mediation analysis. Higher WMH loads in WM tracts were locally matched with the reduced AFD, which was related to decrease in TC. However, no direct relationship was found between TC and FC. Tract-specific changes on WMH, TC and FC for each cognitive performance may explain that macro- and microstructural and functional changes are associated differently with each cognitive domain in a fiber specific manner. In these identified tracts, the differences between normal and MCI for WMH and TC were increased, and the relationships of WMH, TC and FC with cognitive outcomes were more significant, compared to the results from all tracts. Indirect pathways of two-step (TC-FC) between WMH and all cognitive domains were significant (p < 0.0083 with Bonferroni correction), while the separated indirect pathways through TC and through FC were different depending on cognitive domain. Deterioration in specific cognitive domains may be affected by alterations in a set of different tracts that are differently associated with macrostructural, microstructural, and function changes. Thus, assessments of WMH and its associated changes on specific tracts help for better understanding of the interrelationships of multiple changes in cognitive impairment.

General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents

Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses.

Segmental abnormalities of superior longitudinal fasciculus microstructure in patients with schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder: An automated fiber quantification tractography study

INTRODUCTION

Superior longitudinal fasciculus (SLF) is a white matter (WM) tract that connects the frontal, parietal and temporal lobes. SLF integrity has been widely assessed in neuroimaging studies of psychiatric disorders, such as schizophrenia (SZ), bipolar disorder (BD), and attention-deficit/hyperactivity disorder (ADHD). However, prior studies have revealed inconsistent findings and comparisons across disorders have not been fully examined.

METHODS

Here, we obtained data for 113 patients (38 patients with SZ, 40 with BD, 35 with ADHD) and 94 healthy controls from the UCLA Consortium for Neuropsychiatric Phenomic LA5c dataset. We assessed the integrity of 20 major WM tracts with a novel segmentation method by automating fiber tract quantification (AFQ). The AFQ divides each tract into 100 equal parts along the direction of travel, with fractional anisotropy (FA) of each part taken as a characteristic. Differences in FA among the four groups were examined.

RESULTS

Compared to healthy controls, patients with SZ showed significantly lower FA in the second half (51-100 parts) of the SLF. No differences were found between BD and healthy controls, nor between ADHD and healthy controls. Results also demonstrated that patients with SZ showed FA reduction in the second half of the SLF relative to patients with BP. Moreover, greater FA in patients in SLF was positively correlated with the manic-hostility score of the Brief Psychiatry Rating scale.

DISCUSSION

These findings indicated that differences in focal changes in SLF might be a key neurobiological abnormality contributing to characterization of these psychiatric disorders.

DTI Connectometry Analysis Reveals White Matter Changes in Cognitively Impaired World Trade Center Responders at Midlife

BACKGROUND

More than 8% of responders who participated in the search and rescue efforts at the World Trade Center (WTC) following 9/11 developed early-onset cognitive impairment (CI). Approximately 23% were also diagnosed with chronic post-traumatic stress disorder (PTSD).

OBJECTIVE

To shed light on the pathophysiology of these WTC-related conditions, we examined diffusion connectometry to identify altered white matter tracts in WTC responders with CI and/or PTSD compared to unaffected responders.

METHODS

99 WTC responders (mean age 56 years) consisting of CI-/PTSD- (n = 27), CI+/PTSD- (n = 25), CI-/PTSD+ (n = 24), and CI+/PTSD+ (n = 23) were matched on age, sex, occupation, race, and education. Cognitive status was determined using the Montreal Cognitive Assessment and PTSD status was determined using the DSM-IV SCID. Diffusion tensor imaging was acquired on a 3T Siemens Biograph mMR scanner. Connectometry analysis was used to examine whole-brain tract-level differences in white matter integrity as reflected by fractional anisotropy (FA) values after adjusting for confounders.

RESULTS

Analyses identified that FA was negatively correlated with CI and PTSD status in the fornix, cingulum, forceps minor of the corpus callosum and the right uncinate fasciculus. Furthermore, FA was negatively correlated with PTSD status, regardless of CI status in the superior thalamic radiation and the cerebellum.

CONCLUSION

This is the first connectometry study to examine altered white matter tracts in a sample of WTC responders with CI and/or PTSD. Results from this study suggest that WTC responders with early-onset CI may be experiencing an early neurodegenerative process characterized by decreased FA in white matter tracts.

White matter integrity in patients with classic trigeminal neuralgia: a multi-node automated fiber tract quantification study

OBJECTIVE

To study the characteristics of point-by-point destruction of white matter tracts in patients using automated fiber tract quantification (AFQ).

METHODS

Thirty-four classic trigeminal neuralgia (CTN) patients and 34 healthy control (HC) subjects underwent 3.0 T diffusion tensor magnetic resonance imaging and T1-weighted imaging. The fractional anisotropy (FA) and mean diffusivity (MD) of 100 nodes of 20 fiber tracts were analyzed by AFQ, and the correlations of the FA and MD with the visual analogue scale (VAS) pain score were assessed.

RESULTS

The FA values of the left thalamic radiation (middle segment), left corticospinal tract, callosum forceps minor, and right uncinate fasciculus were significantly lower in CTN patients than in the HC group. The MD of the left thalamic tract (middle segment), left corticospinal tract, right superior longitudinal fasciculus, and left superior longitudinal fasciculus (anterior segment) were significantly higher in the CTN group. Additionally, the VAS pain score in CTN patients was positively correlated with FA and negatively correlated with MD.

CONCLUSION

Specific fiber tract nodes were damaged in CTN patients, which was related to the VAS pain score. Multi-node quantitative studies of fiber tract damage are valuable for understanding the white matter tract damage pattern in CTN patients.

Altered Connection and Diagnosis Utility of White Matter in Alzheimer's Disease: A Multi-site Automated Fiber Quantification Study

Alzheimer's disease (AD) is a typical neurodegenerative disease that is associated with cognitive decline, memory loss, and functional disconnection. Diffusion tensor imaging (DTI) has been widely used to investigate the integrity and degeneration of white matter in AD. In this study, with one of the world's largest DTI biobanks (865 individuals), we aim to explore the diagnosis utility and stability of tractbased features (extracted by automated fiber quantification (AFQ) pipeline) in AD. First, we studied the clinical association of tract-based features by detecting AD-associated alterations of diffusion properties along fiber bundles. Then, a binary classification experiment between AD and normal controls was performed using tract-based diffusion properties as features and support vector machine (SVM) as a classifier with an independent site cross-validation strategy. The average accuracy of 77.90% (the highest was 88.89%) showed that white matter properties as biomarkers had a relatively stable role in the clinical diagnosis of AD.Clinical Relevance- White matter characteristics are valid and robust biomarkers of AD, which have high accuracy and generalizability in the AD diagnosis in a large multi-site dataset.

Aberrant White Matter Microstructure in Depressed Patients with Suicidality

BACKGROUND

Depression is a common psychiatric disorder affecting 264 million people globally, and the worst outcome is suicide. While regional brain alterations in depressed suicidal brain have previously been reported, knowledge about white matter (WM) microstructure is limited.

PURPOSE

Automated fiber quantification (AFQ) acquired by magnetic resonance imaging was used to calculate diffusion properties of fiber tracks to explore the structural alteration of WM associated with suicidality in depressive patients.

STUDY TYPE

Cross-sectional.

SUBJECTS

Forty-five depressive patients without suicidality (DS- group, 60.00% females), 53 depressed patients with suicidality (DS+ group, 66.04% females), and 59 healthy controls (HC group, 67.80% females).

FIELD STRENGTH/SEQUENCE

3.0 T; single-shot echo-planar imaging sequence.

ASSESSMENT

The point-wise group difference of the fiber tracts was determined by diffusion properties including fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of 18 specific WM tracts.

STATISTICAL TESTS

Analysis of covariance (ANCOVA) and partial correlation analysis were used. A threshold of P < 0.05 was considered statistically significant.

RESULTS

The significantly different diffusion properties were found in callosum forceps, left inferior fronto-occipital fasciculus (IFOF), right anterior thalamic radiation (ATR), and left cingulum cingulate in DS- and DS+ groups. The correlation analysis results showed that MD of right ATR was significantly positively correlated with Hamilton Depression Rating Scale (HAMD) scores (r = 0.363). In addition, AD of right ATR (r = 0.372), MD of callosum forceps minor (r = 0.511), RD of left IFOF (r = 0.429), and RD of callosum forceps minor (r = 0.515) were significantly positively correlated with suicide item scores of HAMD.

DATA CONCLUSION

Our demonstration of decreased WM tract integrity including callosum forceps, IFOF, and ATR confirms the central involvement of the frontal cortex and limbic system with suicidality in depression.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 3.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer's disease

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer's disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.

Extreme capsule is a bottleneck for ventral pathway

As neuroscience literature suggests, extreme capsule is considered a whiter matter tract. Nevertheless, it is not clear whether extreme capsule itself is an association fiber pathway or only a bottleneck for other association fibers to pass. Via our review, investigating anatomical position, connectivity and cognitive role of the bundles in extreme capsule, and by analyzing data from the dissection, it can be argued that extreme capsule is probably a bottleneck for the passage of uncinated fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF), and these fasciculi are responsible for the respective roles in language processing.

An anomaly detection approach to identify chronic brain infarcts on MRI

The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how 'normal' tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.

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

Background

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

Methods

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

Results

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

Conclusions

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

Microstructural properties of major white matter tracts in constant exotropia before and after strabismus surgery

AIMS

The purpose of this study was to explore the microstructural properties of the major white matter (WM) tracts in constant exotropia (XT) before and after strabismus surgery, and further investigate the association between microstructural alterations and the ocular dominance (OD).

METHODS

We collected diffusion tensor imaging data of patients with XT before (n=19) and after (n=15) strabismus surgery and 20 healthy controls and evaluated OD and stereopsis. The probabilistic streamline tractography of the 24 major WM tracts was reconstructed by using the automated fibre quantification package. Fractional anisotropy and mean diffusivity (MD) along each tract were estimated, and their differences between the groups were examined. Furthermore, we evaluated the relationship between OD and the absolute value of altered microstructural parameters.

RESULTS

While all postoperative XT patients restored normal stereopsis, most of their OD remained aberrant (9 out of 11). Compared with that of preoperation, the MD of postoperative patients decreased significantly along left anterior thalamic radiation (ATR), left arcuate fasciculus (AF), left corticospinal tract (CST), left cingulum cingulate (CGC) and left inferior fronto-occipital fasciculus. Moreover, OD was negatively correlated with the absolute value of MD changes in left ATR, left AF, left CST and left CGC.

CONCLUSION

Microstructural alterations after surgery in the visuospatial network tracts may contribute to the stereopsis restoration. Additionally, the results of the correlation analysis may signify that the balanced binocular input may be more conducive for the restoration and improvement of binocular visual function, including stereopsis. Thus, restoring normal ocular balance after surgical correction may be necessary to achieve more substantial improvements.

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

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

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

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

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

Alzheimer-related altered white matter microstructural integrity in Down syndrome: A model for sporadic AD?

INTRODUCTION

Virtually all adults with Down syndrome (DS) develop Alzheimer's disease (AD)-associated neuropathology by the age of 40, with risk for dementia increasing from the early 50s. White matter (WM) pathology has been reported in sporadic AD, including early demyelination, microglial activation, loss of oligodendrocytes and reactive astrocytes but has not been extensively studied in the at-risk DS population.

METHODS

Fifty-six adults with DS (35 cognitively stable adults, 11 with mild cognitive impairment, 10 with dementia) underwent diffusion-weighted magnetic resonance imaging (MRI), amyloid imaging, and had assessments of cognition and functional abilities using tasks appropriate for persons with intellectual disability.

RESULTS

Early changes in late-myelinating and relative sparing of early-myelinating pathways, consistent with the retrogenesis model proposed for sporadic AD, were associated with AD-related cognitive deficits and with regional amyloid deposition.

DISCUSSION

Our findings suggest that quantification of WM changes in DS could provide a promising and clinically relevant biomarker for AD clinical onset and progression.

White Matter Differences in Networks in Elders with Mild Cognitive Impairment and Alzheimer's Disease

Background: Alzheimer's disease (AD) is associated with impairment of large-scale brain networks, disruption in structural connections, and functional disconnection between distant brain regions. Although decreased functional connectivity has been thoroughly investigated and reported by existing functional neuroimaging literature, this study investigated network-based differences due to the structural changes in white matter pathways in AD patients. We hypothesize that diffusion metrics of disrupted tracts that go through cognitive networks related with intrinsic awareness, motor movement, and executive control can be utilized as biomarkers to distinguish prodromal stage from AD stage. Methods: Diffusion MRI data of a total 154 subjects, including patients with clinical AD (n = 47) and patients with mild cognitive impairment (MCI) (n = 107) was used. To study structural changes associated with white matter fiber pathways voxel-averaged diffusion metrics and fiber density metrics were calculated. Results: Study revealed that AD patients exhibit disruptions in intrahemispheric tracts and projection fiber tracts as suggested by diffusion indices. Our whole brain analysis revealed that network differences within default mode network (DMN), sensory motor network, and frontoparietal networks are associated with disruption in inferior fronto-occipital fasciculus (IFOF), corticospinal tract, and superior longitudinal fasciculus. Global function revealed by Mini Mental State Examination correlate with those fiber pathways that form reciprocal connections within networks associated with motor movement and executive control. Conclusion: Diffusion metrics appear to be more sensitive than fiber density metrics in differentiating the structural changes in the white matter. Decreased fractional anisotropy along with increased mean diffusivity and radial diffusivity in forceps minor, corticospinal tract, and IFOF as an imaging biomarker would be ideal to distinguish AD patients from MCI patients. Difference of DMN, sensory motor network, and frontal parietal network in our study reveals that AD patients may suffer from poor motor movement and degraded executive control.

Focal white matter disruptions along the cingulum tract explain cognitive decline in amnestic mild cognitive impairment (aMCI)

White matter abnormalities of the human brain are implicated in typical aging and neurodegenerative diseases. However, our understanding of how fine-grained changes in microstructural properties along white matter tracts are associated with memory and cognitive decline in normal aging and mild cognitive impairment remains elusive. We quantified tract profiles with a newer method that can reliably measure fine-grained changes in white matter properties along the tracts using advanced multi-shell diffusion magnetic resonance imaging in 25 patients with amnestic mild cognitive impairment (aMCI) and 23 matched healthy controls (HC). While the changes in tract profiles were parallel across aMCI and HC, we found a significant focal shift in the profile at specific locations along major tracts sub-serving memory in aMCI. Particularly, our findings depict white matter alterations at specific locations on the right cingulum cingulate, the right cingulum hippocampus and anterior corpus callosum (CC) in aMCI compared to HC. Notably, focal changes in white matter tract properties along the cingulum tract predicted memory and cognitive functioning in aMCI. The results suggest that white matter disruptions at specific locations of the cingulum bundle may be a hallmark for the early prediction of Alzheimer’s disease and a predictor of cognitive decline in aMCI.

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

Aims

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

Methods

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

Results

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

Conclusions

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

Associations Between Microstructure, Amyloid, and Cognition in Amnestic Mild Cognitive Impairment and Dementia

BACKGROUND

Although amyloid-β (Aβ) and microstructural brain changes are both effective biomarkers of Alzheimer's disease, their independent or synergistic effects on cognitive decline are unclear.

OBJECTIVE

To examine associations of Aβ and brain microstructure with cognitive decline in amnestic mild cognitive impairment and dementia.

METHODS

Restriction spectrum imaging, cerebrospinal fluid Aβ, and longitudinal cognitive data were collected on 23 healthy controls and 13 individuals with mild cognitive impairment or mild to moderate Alzheimer's disease. Neurite density (ND) and isotropic free water diffusion (IF) were computed in fiber tracts and cortical regions of interest. We examined associations of Aβ with regional and whole-brain microstructure, and assessed whether microstructure mediates effects of Aβ on cognitive decline.

RESULTS

Lower ND in limbic and association fibers and higher medial temporal lobe IF predicted baseline impairment and longitudinal decline across multiple cognitive domains. ND and IF predicted cognitive outcomes after adjustment for Aβ or whole-brain microstructure. Correlations between microstructure and cognition were present for both amyloid-positive and amyloid-negative individuals. Aβ correlated with whole-brain, rather than regional, ND and IF.

CONCLUSION

Aβ correlates with widespread microstructural brain changes, whereas regional microstructure correlates with cognitive decline. Microstructural abnormalities predict cognitive decline regardless of amyloid, and may inform about neural injury leading to cognitive decline beyond that attributable to amyloid.