Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.

Topological alteration of the brain structural network in Parkinson's disease with apathy

BACKGROUND

Apathy is a common neuropsychiatric manifestations in Parkinson's disease (PD), but neural network mechanisms still remain elusive. We aim to investigate the topological alteration of the brain structural network in PD with apathy.

METHOD

In the present study, a total of 47 apathetic PD (aPD) patients, 37 non-apathetic PD (naPD) patients, and 40 healthy controls (HCs) were enrolled. Diffusion tensor imaging (DTI) in conjunction with graph-theoretic approaches were used to explore the alterations of topological properties of the WM structural network arising from apathy in PD. One-way analysis of covariance and post hoc analyses were performed to explore differences among the three groups. Correlations were ascertained to examine relationships between the Starkstein Apathy Scale (AS) scores and significantly different network metrics among the three groups.

RESULTS

Both aPD and naPD patients remained small-world topology. However, compared with the naPD patients, aPD patients showed increased clustering coefficient (Cp) at the global level. At the regional level, aPD exhibited decreased nodal properties, mainly in the right dorsolateral prefrontal cortex (DLPFC), the right caudate nucleus (CAU), the right hippocampus, and the right superior parietal gyrus. Further, AS scores were negatively correlated with degree centrality of the right DLPFC (r = -0.254, p = 0.020) and the right CAU ( r = -0.357, p = 0.001) in the pooled patients with PD.

CONCLUSIONS

The findings suggested that apathy in PD presented relatively optimized global topological properties of the brain structural network and disrupted topological organization of the regional network, particularly involving the fronto-striatal-limbic circuits. The altered topological properties of abnormal brain regions might be used to understand the physiopathologic mechanism of the neural network in aPD patients.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

The mean kurtosis (MK) is more sensitive diagnostic biomarker than fractional anisotropy (FA) for Parkinson's disease: A diagnostic performance study and meta-analysis

BACKGROUND

The mean kurtosis (MK) and fractional anisotropy (FA) in patients of Parkinson's disease (PD) are usually measured by diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI), separately.

METHODS

In this study we perform a meta-analysis to discuss which noninvasive biomarker is more advantageous for PD, MK, or FA. Databases including Medline via PubMed, the Cochrane Central Register of Controlled Trials, Embase via OVID and China National Knowledge Infrastructure. Databases are searched up to December 31st, 2019. Four brain regions are identified for analysis based on data extracted from articles.

RESULTS

The articles contain 5 trials with 274 total PD patients and 189 healthy controls (HCs). The results show not only significantly higher MK values of putamen, caudate, globus pallidus in PD compared to that of HCs (weighted mean difference [WMD] = 0.06, 95% CI = 0.02-0.09, P = .002, WMD = 0.03, 95% CI = 0.01-0.067, P = .01, WMD = 0.18, 95% CI = 0.11-0.24, P < .00001), but also a significantly higher FA in caudate of PD compared to HCs (WMD = 0.02, 95% CI = 0.00-0.03, P = .006).

CONCLUSION

This indicates that the sharp difference detected between PD patients and HCs can be detected by DKI and DTI. By further discussing results, we found that MK could be more sensitive diagnostic biomarker than FA toward PD diagnosis.

Comparison of brain microstructure alterations on diffusion kurtosis imaging among Alzheimer’s disease, mild cognitive impairment, and cognitively normal individuals

Objective

Our study aimed to explore the differences in brain microstructure in patients with Alzheimer’s disease (AD) and with mild cognitive impairment (MCI) and in individuals with normal cognition using diffusion kurtosis imaging (DKI) to identify a potential non-invasive biomarker of AD.

Materials and methods

A total of 61 subjects were included in our study, including 20 subjects diagnosed with AD, 21 patients diagnosed with amnestic MCI, and 20 cognitively normal individuals. We acquired magnetic resonance imaging (MRI) scans, and DKI images were processed. Twelve regions of interest were drawn, and various parameters were measured and analyzed using SPSS version 11.0 software.

Results

Comparative analysis showed that differences in brain regions in terms of mean diffusion (MD) and mean kurtosis (MK) between groups were the most marked. Precuneus MD, temporal MK, precuneus MK, and hippocampal MK were significantly correlated with neuropsychological test scores. Hippocampal MK showed the strongest correlation with the medial temporal lobe atrophy score (r = −0.510), and precuneus MD had the strongest correlation with the Koedam score (r = 0.463). The receiver operating curve analysis revealed that hippocampal MK exhibited better diagnostic efficacy than precuneus MD for comparisons between any group pair.

Conclusion

DKI is capable of detecting differences in brain microstructure between patients with AD, patients with MCI, and cognitively normal individuals. Moreover, it compensates for the deficiencies of conventional MRI in detecting pathological changes in microstructure before the appearance of macroscopic atrophy. Hippocampus MK was the most sensitive single parameter map for differentiating patients with AD, patients with MCI, and cognitively normal individuals.

Aberrant brain gray matter and functional networks topology in end stage renal disease patients undergoing maintenance hemodialysis with cognitive impairment

Purpose

To characterize the topological properties of gray matter (GM) and functional networks in end-stage renal disease (ESRD) patients undergoing maintenance hemodialysis to provide insights into the underlying mechanisms of cognitive impairment.

Materials and methods

In total, 45 patients and 37 healthy controls were prospectively enrolled in this study. All subjects completed resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) examinations and a Montreal cognitive assessment scale (MoCA) test. Differences in the properties of GM and functional networks were analyzed, and the relationship between brain properties and MoCA scores was assessed. Cognitive function was predicted based on functional networks by applying the least squares support vector regression machine (LSSVRM) and the whale optimization algorithm (WOA).

Results

We observed disrupted topological organizations of both functional and GM networks in ESRD patients, as indicated by significantly decreased global measures. Specifically, ESRD patients had impaired nodal efficiency and degree centrality, predominantly within the default mode network, limbic system, frontal lobe, temporal lobe, and occipital lobe. Interestingly, the involved regions were distributed laterally. Furthermore, the MoCA scores significantly correlated with decreased standardized clustering coefficient (γ), standardized characteristic path length (λ), and nodal efficiency of the right insula and the right superior temporal gyrus. Finally, optimized LSSVRM could predict the cognitive scores of ESRD patients with great accuracy.

Conclusion

Disruption of brain networks may account for the progression of cognitive dysfunction in ESRD patients. Implementation of prediction models based on neuroimaging metrics may provide more objective information to promote early diagnosis and intervention.

Topologic Efficiency Abnormalities of the Connectome in Asymptomatic Patients with Leukoaraiosis

Leukoaraiosis (LA) is commonly found in aging healthy people but its pathophysiological mechanism is not entirely known. Furthermore, there is still a lack of effective pathological biomarkers that can be used to identify the early stage of LA. Our aim was to investigate the white matter structural network in asymptomatic patients with the early stage of LA. Tractography data of 35 asymptomatic patients and 20 matched healthy controls (HCs) based on diffusion kurtosis imaging (DKI) were analysed by using graph theory approaches and tract-based spatial statistics (TBSS). Diffusion parameters measured within the ALAs and HCs were compared. Decreased clustering coefficient and local efficiency values of the overall topological white matter network were observed in the ALAs compared with those of the HCs. Participants in the asymptomatic group also had lower nodal efficiency in the left triangular part of the inferior frontal gyrus, left parahippocampal gyrus, right calcarine fissure and surrounding cortex, right temporal pole of the superior temporal gyrus and left middle temporal gyrus compared to the ALAs. Moreover, similar hub distributions were found within participants in the two groups. In this study, our data demonstrated a topologic efficiency abnormalities of the structural network in asymptomatic patients with leukoaraiosis. The structural connectome provides potential connectome-based measures that may be helpful for detecting leukoaraiosis before clinical symptoms evolve.

A Comparative Analysis of White Matter Structural Networks on SCLC Patients After Chemotherapy

Previous sMRI, DTI and rs-fMRI studies in small cell lung cancer (SCLC) patients have reported that patients after chemotherapy had gray and white matter structural alterations along with functional deficits. Nonetheless, few are known regarding the potential alterations in the topological organization of the WM structural network in SCLC patients after chemotherapy. In this context, the scope of the present study is to evaluate the WM structural network of 20 SCLC patients after chemotherapy and to 14 healthy controls, by applying a combination of DTI with graph theory. The results revealed that both SCLC and healthy controls groups demonstrated small world properties. The SCLC patients had decreased values in the clustering coefficient, local efficiency and degree metrics as well as increased shortest path length when compared to the healthy controls. Moreover, the two groups reported different topological reorganization of hub distribution. Lastly, the SCLC patients exhibited significantly decreased structural connectivity in comparison to the healthy group. These results underline that the topological organization of the WM structural network in SCLC patients was disrupted and hence constitute new vital information regarding the effects that chemotherapy and cancer may have in the patients' brain at network level.

Imaging biomarkers for Alzheimer's disease and glaucoma: Current and future practices

Glaucoma is a leading cause of blindness worldwide. Although intraocular pressure is the main risk factor for glaucoma, several intraocular pressure independent factors have been associated with the risk of developing the disease and its progression. The diagnosis of glaucoma relies on clinical features of the optic nerve, visual field test, and optical coherence tomography. However, the multidisciplinary aspect of the disease suggests that other biomarkers may be useful for the diagnosis, thus underling the importance of novel imaging techniques supporting clinicians. This review analyzes the common pathogenic mechanisms between glaucoma and Alzheimer's disease and the possible novel approaches for diagnosis and follow up.

Frontotemporal Pathology in Motor Neuron Disease Phenotypes: Insights From Neuroimaging

Frontotemporal involvement has been extensively investigated in amyotrophic lateral sclerosis (ALS) but remains relatively poorly characterized in other motor neuron disease (MND) phenotypes such as primary lateral sclerosis (PLS), progressive muscular atrophy (PMA), spinal muscular atrophy (SMA), spinal bulbar muscular atrophy (SBMA), post poliomyelitis syndrome (PPS), and hereditary spastic paraplegia (HSP). This review focuses on insights from structural, metabolic, and functional neuroimaging studies that have advanced our understanding of extra-motor disease burden in these phenotypes. The imaging literature is limited in the majority of these conditions and frontotemporal involvement has been primarily evaluated by neuropsychology and post mortem studies. Existing imaging studies reveal that frontotemporal degeneration can be readily detected in ALS and PLS, varying degree of frontotemporal pathology may be captured in PMA, SBMA, and HSP, SMA exhibits cerebral involvement without regional predilection, and there is limited evidence for cerebral changes in PPS. Our review confirms the heterogeneity extra-motor pathology across the spectrum of MNDs and highlights the role of neuroimaging in characterizing anatomical patterns of disease burden in vivo. Despite the contribution of neuroimaging to MND research, sample size limitations, inclusion bias, attrition rates in longitudinal studies, and methodological constraints need to be carefully considered. Frontotemporal involvement is a quintessential clinical facet of MND which has important implications for screening practices, individualized management strategies, participation in clinical trials, caregiver burden, and resource allocation. The academic relevance of imaging frontotemporal pathology in MND spans from the identification of genetic variants, through the ascertainment of presymptomatic changes to the design of future epidemiology studies.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker.

METHODS

Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (D_s), fast diffusion coefficient (D_f), perfusion fraction (fp) and D_f*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis.

RESULTS

Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the D_f of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B.

CONCLUSION

IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.

Altered structural and functional connectivity in CSF1R-related leukoencephalopathy

CSF1R-related leukoencephalopathy is a rare white-matter encephalopathy characterized by motor and neuropsychiatric symptoms due to colony-stimulating factor 1 receptor (CSF1R) gene mutation. Few studies have investigated the intrinsic brain alternations of patients with CSF1R-related leukoencephalopathy. We aim to evaluate the structural and functional changes in those patients. Seven patients with CSF1R-related leukoencephalopathy and 15 age-matched healthy controls (HCs) underwent multimodal magnetic resonance imaging (MRI), including high-resolution T1-weighted imaging, T2-weighted fluid attenuated inversion recovery imaging, diffusion-weighted imaging, diffusion kurtosis imaging (DKI) and resting-state functional MRI. First, to detect structural alterations, the gray matter volumes were compared using voxel-based morphometry analyses. Second, DKI parametric maps were used to evaluate the white matter (WM) connectivity changes. Finally, we constructed a seed-based resting-state functional connectivity matrix based on 90 regions of interest and examined the functional network changes of CSF1R-related leukoencephalopathy. Unlike the HCs, patients with CSF1R-related leukoencephalopathy predominantly had morphological atrophy in the bilateral thalamus and left hippocampus. In addition, the abnormal diffusivity was mainly distributed in the splenium of the corpus callosum, periventricular regions, centrum semiovale, subcortical U-fibers and midline cortex structures. Moreover, the patients had significantly reduced functional connectivity between the bilateral caudate nucleus and their contralateral hippocampus. Therefore, in addition to hyperintensity on the T2-weighted images, CSF1R-related leukoencephalopathy also showed abnormal structural and functional alterations, including subcortical atrophy and reduced functional connectivity, as well as altered diffuse parameters in the WM and subcortical regions. These findings expand our understanding of the potential pathophysiologic mechanism behind this hereditary disease.

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Peptidotriazolamers Inhibit Aβ(1-42) Oligomerization and Cross a Blood-Brain-Barrier Model

In peptidotriazolamers every second peptide bond is replaced by a 1H-1,2,3-triazole. Such foldamers are expected to bridge the gap in molecular weight between small-molecule drugs and protein-based drugs. Amyloid β (Aβ) aggregates play an important role in Alzheimer's disease. We studied the impact of amide bond replacements by 1,4-disubstituted 1H-1,2,3-triazoles on the inhibitory activity of the aggregation "hot spots" K¹⁶ LVFF²⁰ and G³⁹ VVIA⁴² in Aβ(1-42). We found that peptidotriazolamers act as modulators of the Aβ(1-42) oligomerization. Some peptidotriazolamers are able to interfere with the formation of toxic early Aβ oligomers, depending on the position of the triazoles, which is also supported by computational studies. Preliminary in vitro results demonstrate that a highly active peptidotriazolamer is also able to cross the blood-brain-barrier.

Microstructural and Cerebral Blood Flow Abnormalities in Subjective Cognitive Decline Plus: Diffusional Kurtosis Imaging and Three-Dimensional Arterial Spin Labeling Study

Objective: To explore microstructural and cerebral blood flow (CBF) abnormalities in individuals with subjective cognitive decline plus (SCD plus) using diffusional kurtosis imaging (DKI) and three-dimensional (3D) arterial spin labeling (ASL). Methods: Twenty-seven patients with SCD plus, 31 patients with amnestic mild cognitive impairment (aMCI), and 33 elderly controls (ECs) were recruited and underwent DKI and 3D ASL using a GE 3.0-T MRI. Mean kurtosis (MK), fractional anisotropy (FA), mean diffusivity (MD), and CBF values were acquired from 24 regions of interest (ROIs) in the brain, including the bilateral hippocampal (Hip) subregions (head, body, and tail), posterior cingulate cortex (PCC), precuneus, dorsal thalamus subregions (anterior nucleus, ventrolateral nucleus, and medial nucleus), lenticular nucleus, caput nuclei caudati, white matter (WM) of the frontal lobe, and WM of the occipital lobe. Pearson's correlation analysis was performed to assess the relationships among the DKI-derived parameters, CBF values, and key neuropsychological tests for SCD plus. Results: Compared with ECs, participants with SCD plus showed a significant decline in MK and CBF values, mainly in the Hip head and PCC, and participants with aMCI exhibited more significant abnormalities in the MK and CBF values than individuals with ECs and SCD plus in multiple regions. Combined MK values showed better discrimination between patients with SCD plus and ECs than that obtained using CBF levels, with areas under the receiver operating characteristic (ROC) curve (AUC) of 0.874 and 0.837, respectively. Similarly, the AUC in discriminating SCD plus from aMCI patients obtained using combined MK values was 0.823, which was also higher than the combined AUC of 0.779 obtained using CBF values. Moreover, MK levels in the left Hip (h) and left PCC positively correlated with the auditory verbal learning test-delayed recall (AVLT-DR) score in participants with SCD plus. By contrast, only the CBF value in the left Hip head positively correlated with the AVLT-DR score. Conclusions: Our results provide new evidence of microstructural and CBF changes in patients with SCD plus. MK may be used as an early potential neuroimaging biomarker and may be a more sensitive DKI parameter than CBF at the very early stage of Alzheimer's disease (AD).

Differentiating high-grade glioma recurrence from pseudoprogression: Comparing diffusion kurtosis imaging and diffusion tensor imaging

PURPOSE

To compare the diagnostic value of DKI and DTI in differentiation of high-grade glioma recurrence and pseudoprogression (PsP).

METHOD

Forty patients with high-grade gliomas who exhibited new enhancing lesions (24 high-grade glioma recurrence and 16 PsP) within 6 months after surgery followed by completion of chemoradiation therapy. All patients underwent repeat surgery or biopsy after routine MRI and DKI (including DTI). They were histologically classified into high-grade glioma recurrence and PsP groups. DKI (mean kurtosis [MK], axial kurtosis [Ka], and radial kurtosis [Kr]) and DTI (mean diffusivity [MD] and fractional anisotropy [FA]) parameters in the enhancing lesions and in the perilesional edema were measured. Inter-group differences between high-grade glioma recurrence and PsP were compared using the Mann-Whitney U test The receiver operating characteristic (ROC) curve was used to assess differential diagnostic efficacy of each parameter, and Z-scores were used to compare the value between DKI and DTI.

RESULTS

Relative MK (rMK) was significantly higher and relative MD (rMD) was significantly lower in the enhancing lesions of high-grade glioma recurrence compared to PsP (P <  0.001, P = 0.006, respectively). The AUC was 0.914 for rMK and 0.760 for rMD, and this difference was significant (P = 0.030). In the perilesional edema, rMK values were significantly higher and rMD values were significantly lower in high-grade glioma recurrence compared to PsP (P <  0.001, P =  0.005).

CONCLUSIONS

DKI had superior performance in differentiating high-grade glioma recurrence from PsP, and rMK appeared to be the best independent predictor.

White matter alterations in adult with autism spectrum disorder evaluated using diffusion kurtosis imaging

PURPOSE

Autism spectrum disorder (ASD) is related to impairment in various white matter (WM) pathways. Utility of the recently developed two-compartment model of diffusion kurtosis imaging (DKI) to analyse axial diffusivity of WM is restricted by several limitations. The present study aims to validate the utility of model-free DKI in the evaluation of WM alterations in ASD and analyse the potential relationship between DKI-evident WM alterations and personality scales.

METHODS

Overall, 15 participants with ASD and 15 neurotypical (NT) controls were scanned on a 3 T magnetic resonance (MR) scanner, and scores for autism quotient (AQ), systemising quotient (SQ) and empathising quotient (EQ) were obtained for both groups. Multishell diffusion-weighted MR data were acquired using two b-values (1000 and 2000 s/mm²). Differences in mean kurtosis (MK), radial kurtosis (RK) and axial kurtosis (AK) between the groups were evaluated using tract-based spatial statistics (TBSS). Finally, the relationships between the kurtosis indices and personality quotients were examined.

RESULTS

The ASD group demonstrated significantly lower AK in the body and splenium of corpus callosum than the NT group; however, no other significant differences were identified. Negative correlations were found between AK and AQ or SQ, predominantly in WM areas related to social-emotional processing such as uncinate fasciculus, inferior fronto-occipital fasciculus, and inferior and superior longitudinal fasciculi.

CONCLUSIONS

Model-free DKI and its indices may represent a novel, objective method for detecting the disease severity and WM alterations in patients with ASD.

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

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FA^DTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FA^TDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FA^DTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FA^TDF detected robust widespread associations with clinical measures, while FA^DTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.