DTI correlates of distinct cognitive impairments in Parkinson's disease

Positive Associations between the Personality Trait of Openness and Verbal Learning and Memory in Individuals with Parkinson's Disease: A Pilot Study

OBJECTIVE

Individuals with idiopathic Parkinson's disease (PD) often experience difficulties with verbal learning and memory, even in the absence of dementia. Higher levels of the personality trait of openness predict better learning and memory in other older adult populations, but openness's contributions in PD are unknown. Lower openness and alterations in openness's neural substrates in PD suggest that openness may have strong associations with memory in PD.

METHOD

We used the Big Five Inventory-2 (BFI-2) personality self-rating questionnaire and the Rey Auditory Verbal Learning Test (RAVLT) in a cross-sectional sample of 33 persons with PD (PwPD; 17 men), 26 healthy older adults (OA; 14 men), and 37 healthy younger adults (YA; 19 men). Correlation analysis examined relations between openness (BFI-2 open-mindedness) and verbal learning and memory (RAVLT performances). Correlation and regression analysis controlled for psychosocial and cognitive factors and examined possible moderators and mediators.

RESULTS

Significant, positive correlations between openness and RAVLT scores occurred in PwPD but not in OA or YA. Among PwPD, openness independently predicted most RAVLT scores in regression models. Its associations were not explained by PD duration, disease severity, disease stage, or sex. PwPD low in openness performed worse than OA. Among OA, older age predicted significantly more positive association between openness and memory.

CONCLUSIONS

Openness is positively associated with verbal memory in PwPD, as well as in healthy older adults (depending on age), with implications for the relevance of personality factors in cognition.

Imaging biomarkers of cortical neurodegeneration underlying cognitive impairment in Parkinson's disease

PURPOSE

Imaging biomarkers bear great promise for improving the diagnosis and prognosis of cognitive impairment in Parkinson's disease (PD). We compared the ability of three commonly used neuroimaging modalities to detect cortical changes in PD patients with mild cognitive impairment (PD-MCI) and dementia (PDD).

METHODS

53 cognitively normal PD patients (PD-CN), 32 PD-MCI, and 35 PDD underwent concurrent structural MRI (sMRI), diffusion-weighted MRI (dMRI), and [18F]FDG PET. We extracted grey matter volumes (sMRI), mean diffusivity (MD, dMRI), and standardized uptake value ratios ([18F]FDG PET) for 52 cortical regions included in a neuroanatomical atlas. We assessed group differences using ANCOVA models and further applied a cross-validated machine learning approach to identify the modality-specific brain regions that are most indicative of dementia status and assessed their diagnostic accuracy for group separation using receiver operating characteristic analyses.

RESULTS

In sMRI, atrophy of temporal and posterior-parietal areas allowed separating PDD from PD-CN (AUC = 0.77 ± 0.07), but diagnostic accuracy was poor for separating PD-MCI from PD-CN (0.57 ± 0.10). dMRI showed most pronounced diffusivity changes in the medial temporal lobe, which provided excellent diagnostic performance for PDD (AUC = 0.87 ± 0.06), and a more modest but still significant performance for PD-MCI (AUC = 0.71 ± 0.09). Finally, [18F]FDG PET revealed pronounced hypometabolism in posterior-occipital regions, which provided the highest diagnostic accuracies for both PDD (AUC = 0.89 ± 0.05) and PD-MCI (AUC = 0.78 ± 0.05). In statistical comparisons, both [18F]FDG PET (p < 0.001) and dMRI (p < 0.031) outperformed sMRI for detecting PDD and PD-MCI.

CONCLUSION

Among the tested modalities, [18F]FDG PET was most accurate for detecting cortical changes associated with cognitive impairment in PD, especially at early stages. Diffusion measurements may represent a promising MRI-based alternative.

New method for diffusion-weighted images denoising based on patch-matching with higher-order singular value decomposition

BackgroundDiffusion-weighted imaging (DWI) is an important technique to study brain microstructure. However, diffusion-weighted (DW) images suffer from severe low signal-to-noise ratio (SNR) problem, affecting subsequent diffusion analysis.ObjectiveThe goal of this paper is to develop advanced DWI denoising technique to effectively reduce noise while improving the accuracy and reliability of subsequent diffusion model fitting and diffusion analysis, thereby facilitating the research and analysis of brain science.MethodsWe propose a new method for denoising DW images based on patch-matching with higher-order singular value decomposition (HOSVD) by combined with the variance-stabilizing transformation technique. It starts with introducing a novel non-local mean algorithm as a prefiltering stage, and then denoises the noisy data using a local HOSVD algorithm based on the HOSVD bases learned from prefiltered images.ResultsExperiments are performed on simulation, HCP and in vivo brain DWI datasets. Results show that the proposed method significantly reduces spatially invariant and variant noise, improving the most reliable diffusion analysis compared with the different denoising methods.ConclusionsThe proposed method achieves state-of-the-art performance which can improve image quality and enable accurate diffusion analysis.

Integrated genetic analysis and single cell-RNA sequencing for brain image-derived phenotypes and Parkinson's disease

BACKGROUND

Previous studies have reported Parkinson's disease (PD) patients usually have changes in brain image-derived phenotypes (IDPs). However, the role of genetic factors in their association and biological mechanism remains unclear. We aimed to unveil genetic and biological links between brain IDPs and PD.

METHODS

Using genome-wide association study (GWAS) summary statistics and single-cell RNA sequencing (scRNA-seq) data, we performed a comprehensive analysis between 624 brain IDPs and PD. The genetic correlations and causality were examined by linkage disequilibrium score regression (LDSC), two-sample bidirectional Mendelian randomization (MR) and meta-analysis. Potential shared genes were identified using MAGMA and PLACO. Finally, pathway enrichment using FUMA and Metascape, and scRNA-seq analysis were performed to determine biological mechanisms and gene expression atlas across various cell types in brain tissue.

RESULTS

LDSC revealed that 50 brain IDPs were genetically correlated with PD (P < 0.05), in which 5 IDPs, exhibited putative causality on PD through MR (P < 0.05). For instance, we identified that the increased volume of the right thalamus (IVW: OR = 2.08, 95 % CI: 1.33 to 3.25, PFDR = 0.03) was positively correlated with the risk of PD, which was also supported by replicated MR (IVW: OR = 1.63, 95 % CI: 1.17-2.26, PFDR = 0.02) in FinnGen and meta-analysis (OR = 1.78, 95 % CI: 1.36-2.31, PFDR = 5.00 × 10-4). Additionally, we identified 56 unique pleiotropic genes, such as FAM13A, with notable enrichment in neuronal cells. Biological mechanism analysis revealed these genes were enriched in brain tissues and a variety of pathways such as negative regulation of neuron apoptotic processes.

CONCLUSION

We indicated the shared genetic architecture and biological mechanisms between brain IDPs and PD. These findings might provide insights on the therapeutic intervention and early prediction of PD at the brain imaging level.

Executive dysfunction in Parkinson's disease: From neurochemistry to circuits, genetics and neuroimaging

Cognitive decline is one of the most significant non-motor symptoms of Parkinson's disease (PD), with executive dysfunction (EDF) being the most prominent characteristic of PD-associated cognitive deficits. Currently, lack of uniformity in the conceptualization and assessment scales for executive functions impedes the early and accurate diagnosis of EDF in PD. The neurobiological mechanisms of EDF in PD remain poorly understood. Moreover, the treatment of cognitive impairment in PD has progressed slowly and with limited efficacy. Thus, this review explores the characteristics and potential mechanisms of EDF in PD from multiple perspectives, including the concept of executive function, commonly used neuropsychological tests, neurobiochemistry, genetics, electroencephalographic activity and neuroimaging. The available evidence indicates that degeneration of the frontal-striatal circuit, along with mutations in the Catechol-O-methyltransferase (COMT) gene and Leucine-rich repeat kinase 2 (LRRK2) gene, may contribute to EDF in patients with PD. The increase in theta and delta waves, along with the decrease in alpha waves, offers potential biomarkers for the early identification and monitoring of EDF, as well as the development of dementia in patients with PD. The PD cognition-related pattern (PDCP) pattern may serve as a tool for monitoring and assessing cognitive function progression in these patients and is anticipated to become a biomarker for cognitive disorders associated with PD. The aim is to provide new insights for the early and precise diagnosis and treatment of EDF in PD.

A systematic review of diffusion tensor imaging and tractography in dementia with Lewy bodies and Parkinson's disease dementia

We reviewed studies using diffusion tensor imaging (DTI) and tractography to characterise white matter changes in Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD). The search included MEDLINE and EMBASE, and we used a narrative strategy to synthesise the evidence. Data was extracted from 57 studies, of which the majority were considered 'good quality'. Subjects with DLB and PDD had widespread white matter changes compared to healthy controls and Parkinson's disease without cognitive impairment, with a relative sparing of the hippocampus. Compared to subjects with Alzheimer's disease (AD), DLB had greater changes in thalamic connectivity and in the nigroputaminal tract, while AD had greater changes in the parahippocampal white matter and fornix. Cognition was associated with widespread white matter changes, visual hallucinations with thalamic and cholinergic connectivity, and parkinsonism with changes in structures involved in motor control. DTI and tractography may therefore be well suited for discriminating DLB and PDD from other types of dementia, and for studying the aetiology of common symptoms.

Microstructural Degeneration of the Corpus Callosum in Parkinson's Disease with Unilateral Onset: A Free-Water Imaging Study

Background: Motor symptom laterality is an important clinical feature of PD that not only manifests as lateral limb dysfunction but also affects the nonmotor symptoms and the prognosis in PD patients. Previous studies suggested that the compensatory mechanisms in the dominant hemisphere of the brain may be an underlying explanation. The corpus callosum (CC) is the largest fiber connecting the two hemispheres of the brain. Considering the CC as the pointcut may help elucidate the mechanisms underlying how motor symptom laterality affects nonmotor symptoms and prognosis in PD patients. Purpose: To explore microstructural degeneration of the CC in PD patients with unilateral motor symptom onset and evaluate its relationship with motor and nonmotor performance. Methods: In this study, 201 right-handed PD patients with unilateral motor symptom onset (91 patients with left-onset [LPD] and 110 with right-onset [RPD]) and 100 right-handed healthy controls (HC) were included. A bitensor model of diffusion tensor imaging was applied to analyze free water (FW) as well as fractional anisotropy (FAT) and mean diffusivity (MDT) of the tissue compartment after correcting FW. These provide noninvasive in vivo measures of white matter integrity and pathological processes including atrophy, edema, and neuroinflammation. The CC was divided into halves along the median sagittal line, and each half was manually divided into five functional segments. A total of 10 subregions were obtained and numbered in sequence. The laterality index was calculated to quantify the asymmetry of the CC and its segments. A general linear model was used to compare among groups, and partial correlation analysis was performed to explore the relationships between the diffusion parameters of CC subregions and clinical manifestations. Results: Compared with HC, FW and FAT of the bilateral CC were decreased in the LPD group, whereas MDT in the right hemisphere was increased. In the LPD group, FAT of all CC subregions except for subregions 1, 3, and 6 was significantly lower than HC, and MDT in the anterior and posterior segments of the CC (CC subregions 1, 5-7, and 10) was significantly higher than HC. In the RPD group, FAT of subregion 7 was significantly decreased and MDT was increased than HC. Laterality index analysis of the CC indicated significant interhemispheric FAT asymmetry in the anterior and middle in the RPD group, with a more significant reduction in the right CC. Moreover, degeneration of the CC and its subregions was related to motor and nonmotor symptoms in PD. Conclusions: More extensive CC damage was observed in the LPD group than in the RPD group. Additional, asymmetrical damage was observed in the anterior and middle segments of the CC in the RPD group, suggesting that differences in callosal degeneration patterns may be a potential mechanism underlying how asymmetrical motor symptoms affect the nonmotor symptoms and prognosis in PD.

White Matter Microstructure Alteration in Patients with Drug-Induced Parkinsonism: A Diffusion Tensor Imaging Study with Tract-Based Spatial Statistics

INTRODUCTION

This research aimed to investigate the pathophysiological mechanism of how drug-induced parkinsonism (DIP) affects the integrity of the white matter (WM) fiber microstructure as measured by magnetic resonance diffusion tensor image (DTI) fractional anisotropy (FA) and mean diffusivity (MD).

METHODS

We recruited 17 participants diagnosed with DIP, 20 Parkinson's disease (PD) patients, and 16 normal controls (NCs) with a similar age, gender, and years of education. Subsequently, all participants underwent DTI magnetic resonance imaging scanning. To analyze the data, we utilized the software packages Functional MRI of the Brain Centre (FMRIB) Diffusion Toolbox (FDT), developed by the FMRIB laboratory at Oxford University, and tract-based spatial statistics (TBSS).

RESULTS

The Argentina Hyposmia Rating Scale (AHRS) scores of patients in DIP group were markedly higher than those in PD patients group. Compared with the NC group, the FA values in the genu and body of the corpus callosum (CC), anterior limb of the right internal capsule, bilateral anterior corona radiata, bilateral superior corona radiata, right external capsule, and right superior fronto-occipital fasciculus (could be a part of the anterior internal capsule) were significantly decreased in the DIP group; however, no significant cluster was found in MD.

CONCLUSIONS

The present study provides novel insights into the alterations in WM microstructure among DIP patients, suggesting that these methodologies have the potential to aid in the early diagnosis and treatment of DIP.

Mitochondrial Dysfunction in Parkinson's Disease: A Contribution to Cognitive Impairment?

Among the non-motor symptoms associated with Parkinson's disease (PD), cognitive impairment is one of the most common and disabling. It can occur either early or late during the disease, and it is heterogeneous in terms of its clinical manifestations, such as Subjective Cognitive Dysfunction (SCD), Mild Cognitive Impairment (MCI), and Parkinson's Disease Dementia (PDD). The aim of the present review is to delve deeper into the molecular mechanisms underlying cognitive decline in PD. This is extremely important to delineate the guidelines for the differential diagnosis and prognosis of the dysfunction, to identify the molecular and neuronal mechanisms involved, and to plan therapeutic strategies that can halt cognitive impairment progression. Specifically, the present review will discuss the pathogenetic mechanisms involved in the progression of cognitive impairment in PD, with attention to mitochondria and their contribution to synaptic dysfunction and neuronal deterioration in the brain regions responsible for non-motor manifestations of the disease.

A worldwide study of white matter microstructural alterations in people living with Parkinson's disease

The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20-89 years; 33% female) and 885 controls (age: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen's d effect sizes reached d = -1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study

BACKGROUND AND PURPOSE

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia.

MATERIALS AND METHODS

Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed.

RESULTS

Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment.

CONCLUSIONS

The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Microstructural Changes in the Corpus Callosum in Neurodegenerative Diseases

The corpus callosum, the largest white matter structure in the brain, plays a crucial role in interhemispheric communication and cognitive function. This review examines the microstructural changes observed in the corpus callosum across various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis (ALS). New neuroimaging studies, mainly those that use diffusion tensor imaging (DTI) and advanced tractography methods, were put together to show how changes have happened in the organization of white matter and the connections between them. Some of the most common ways the corpus callosum breaks down are discussed, including less fractional anisotropy, higher mean diffusivity, and atrophy in certain regions. The relationship between these microstructural changes and cognitive decline, motor dysfunction, and disease progression is explored. Additionally, we consider the potential of corpus callosum imaging as a biomarker for early disease detection and monitoring. Studies show that people with these disorders have lower fractional anisotropy and higher mean diffusivity in the corpus callosum, often in ways that are specific to the disease. These changes often happen before gray matter atrophy and are linked to symptoms, which suggests that the corpus callosum could be used as an early sign of neurodegeneration. The review also highlights the implications of these findings for understanding disease mechanisms and developing therapeutic strategies. Future directions, including the application of advanced imaging techniques and longitudinal studies, are discussed to elucidate the role of corpus callosum degeneration in neurodegenerative processes. This review underscores the importance of the corpus callosum in understanding the pathophysiology of neurodegenerative diseases and its potential as a target for therapeutic interventions.

Advanced Cognitive Patterns in Multiple System Atrophy Compared to Parkinson's Disease: An Individual Diffusion Tensor Imaging Study

RATIONALE AND OBJECTIVES

Although both Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, they have divergent clinical courses and prognoses. The degeneration of white matter has a considerable impact on cognitive performance, yet it is uncertain how PD and MSA affect its functioning in a similar or different manner.

METHODS

In this study, a total of 116 individuals (37 PD with mild cognitive impairment (PD-MCI), 37 MSA (parkinsonian variant) with mild cognitive impairment (MSA-MCI), and 42 healthy controls) underwent diffusion tensor imaging (DTI) and cognitive assessment. Utilizing probabilistic fiber tracking, association fibers, projection fibers, and thalamic fibers were reconstructed. Subsequently, regression, support vector machine, and SHAP (Shapley Addictive exPlanations) analyzes were conducted to evaluate the association between microstructural diffusion metrics and multiple cognitive domains, thus determining the white matter predictors of MCI.

RESULTS

MSA-MCI patients exhibited distinct white matter impairment extending to the middle cerebellar peduncle, corticospinal tract, and cingulum bundle. Furthermore, the fractional anisotropy (FA) and mean diffusivity (MD)values of the right anterior thalamic radiation were significantly associated with global efficiency (FA: B = 0.69, P < 0.001, VIF = 1.31; MD: B = -0.53, P = 0.02, VIF = 2.50). The diffusion metrics of white matter between PD-MCI and MSA-MCI proved to be an effective predictor of the MCI, with an accuracy of 0.73 (P < 0.01), and the most predictive factor being the MD of the anterior thalamic radiation.

CONCLUSIONS

Our results demonstrated that MSA-MCI had a more noticeable deterioration in white matter, which potentially linked to various cognitive domain connections. Diffusion MRI could be a useful tool in comprehending the neurological basis of cognitive impairment in Parkinsonian disorders.

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures

AIMS

Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism.

METHODS

Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups.

RESULTS

All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients.

CONCLUSION

Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

Advanced brain aging in Parkinson's disease with cognitive impairment

Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.

Mendelian Randomization Analysis to Assess Whether Magnetic Resonance Imaging Signs of Cerebral Small Vessel Disease Can Cause Cognitive Decline and Dementia

OBJECTIVE

Cognitive decline and dementia have been linked to cerebral small vessel disease, so we explored using Mendelian randomization whether cerebral small vessel disease visible as 10 neuroimaging signs may cause cognitive decline and dementia.

METHODS

We analyzed publicly available data from genome-wide association studies using two-sample Mendelian randomization involving inverse variance weighting, weighted median, MR-Egger, and MR-PRESSO approaches.

RESULTS

Mendelian randomization suggested that cognitive decline can be caused by lacunar stroke (inverse variance weighting, β = -0.012, 95% CI -0.024 to -0.001, P = 0.033). Furthermore, an elevated burden of white matter hyperintensities was associated with an increased risk of Dementia due to Parkinson's disease (inverse variance weighting, OR 2.035, 95% CI 1.105 to 3.745, P = 0.023). Notably, no significant associations were observed between neuroimaging markers of Cerebral Small Vessel Disease and other types of dementia.

CONCLUSION

This Mendelian randomization study provides evidence that lacunar stroke and white matter lesions can cause cognitive decline, and that white matter hyperintensity may increase risk of dementia due to Parkinson's disease. These results underscore the need for further investigations into the neurocognitive effects of cerebral small vessel disease.

Mendelian randomization analysis implicates bidirectional associations between brain imaging-derived phenotypes and ischemic stroke

Brian imaging-derived phenotypes (IDPs) have been suggested to be associated with ischemic stroke, but the causality between them remains unclear. In this bidirectional two-sample Mendelian randomization (MR) study, we explored the potential causal relationship between 461 imaging-derived phenotypes (n = 33,224, UK Biobank) and ischemic stroke (n = 34,217 cases/406,111 controls, Multiancestry Genome-Wide Association Study of Stroke). Forward MR analyses identified five IDPs associated with ischemic stroke, including mean diffusivity (MD) in the right superior fronto-occipital fasciculus (1.22 [95% CI, 1.11-1.34]), MD in the left superior fronto-occipital fasciculus (1.30 [1.17-1.44]), MD in the anterior limb of the right internal capsule (1.36 [1.22-1.51]), MD in the right anterior thalamic radiation (1.17 [1.09-1.26]), and MD in the right superior thalamic radiation (1.23 [1.11-1.35]). In the reverse MR analyses, ischemic stroke was identified to be associated with three IDPs, including high isotropic or free water volume fraction in the body of corpus callosum (beta, 0.189 [95% confidence interval, 0.107-0.271]), orientation dispersion index in the pontine crossing tract (0.175 [0.093-0.257]), and volume of the third ventricle (0.219 [0.138-0.301]). This bidirectional two-sample MR study suggested five predictors and three diagnostic markers for ischemic stroke at the brain-imaging level. Further studies are warranted to replicate our findings and clarify underlying mechanisms.

The right hemisphere's capacity for language: evidence from primary progressive aphasia

The role of the right hemisphere (RH) in core language processes is still a matter of intense debate. Most of the relevant evidence has come from studies of gray matter, with relatively little research on RH white matter (WM) connectivity. Using Diffusion Tensor Imaging-based tractography, the current work examined the role of the two hemispheres in language processing in 33 individuals with Primary Progressive Aphasia (PPA), aiming to better characterize the contribution of the RH to language processing in the context of left hemisphere (LH) damage. The findings confirm the impact of PPA on the integrity of the WM language tracts in the LH. Additionally, an examination of the relationship between tract integrity and language behaviors provides robust evidence of the involvement of the WM language tracts of both hemispheres in language processing in PPA. Importantly, this study provides novel evidence of a unique contribution of the RH to language processing (i.e. a contribution independent from that of the language-dominant LH). Finally, we provide evidence that the RH contribution is specific to language processing rather than being domain general. These findings allow us to better characterize the role of RH in language processing, particularly in the context of LH damage.

Neurofilaments - Small proteins of physiological significance and predictive power for future neurodegeneration and cognitive decline across the life span

Neurofilaments (NFs) are not only important for axonal integrity and nerve conduction in large myelinated axons but they are also thought to be crucial for receptor and synaptic functioning. Therefore, NFs may play a critical role in cognitive functions, as cognitive processes are known to depend on synaptic integrity and are modulated by dopaminergic signaling. Here, we present a theory-driven interdisciplinary approach that NFs may link inflammation, neurodegeneration, and cognitive functions. We base our hypothesis on a wealth of evidence suggesting a causal link between inflammation and neurodegeneration and between these two and cognitive decline (see Fig. 1), also taking dopaminergic signaling into account. We conclude that NFs may not only serve as biomarkers for inflammatory, neurodegenerative, and cognitive processes but also represent a potential mechanical hinge between them, moreover, they may even have predictive power regarding future cognitive decline. In addition, we advocate the use of both NFs and MRI parameters, as their synthesis offers the opportunity to individualize medical treatment by providing a comprehensive view of underlying disease activity in neurological diseases. Since our society will become significantly older in the upcoming years and decades, maintaining cognitive functions and healthy aging will play an important role. Thanks to technological advances in recent decades, NFs could serve as a rapid, noninvasive, and relatively inexpensive early warning system to identify individuals at increased risk for cognitive decline and could facilitate the management of cognitive dysfunctions across the lifespan.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease Based on DTI Fiber Tracts

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinson's Progression Markers Initiative.

Frontostriatal and limbic contributions to cognitive decline in Parkinson's disease

BACKGROUND AND PURPOSE

The circuitry underlying heterogenous cognitive profiles in Parkinson's disease (PD) remains unclear. The purpose of this study is to investigate whether structural changes in frontostriatal and limbic pathways contribute to different cognitive trajectories in PD.

METHODS

We obtained clinical and multimodal MRI data from 120 control and 122 PD subjects without dementia or severe motor disability. T1/T2-weighted images estimated volume, and diffusion imaging evaluated fractional anisotropy (FA) of frontostriatal (striatum and frontostriatal white matter [FSWM]) and limbic (hippocampus and fornix) structures. Montreal Cognitive Assessment (MoCA) gauged total and domain-specific (attention/executive and memory) cognitive function. Linear mixed-effects models were used to compare MRI and cognitive progression over 4.5 years between controls and PD and evaluate associations between baseline MRI and cognitive changes in PD.

RESULTS

At baseline, control and PD groups were comparable, except PD participants had smaller striatal volume (p < 0.001). Longitudinally, PD showed faster decline in hippocampal volume, FSWM FA, and fornix FA (ps < .016), but not striatal volume (p = .218). Total and domain-specific MoCA scores declined faster in PD (ps < .030). In PD, lower baseline hippocampal volume (p = .005) and fornix FA (p = .032), but not striatal volume (p = .662) or FSWM FA (p = .143), were associated with faster total MoCA decline. Baseline frontostriatal metrics of striatal volume and FSWM FA were associated with faster attention/executive decline (p < .038), whereas lower baseline hippocampal volume was associated with faster memory decline (p = .005).

CONCLUSION

In PD, frontostriatal structural metrics are associated with attention/executive tasks, whereas limbic changes correlated with faster global cognitive decline, particularly in memory tasks.

Structural connectivity and brain network analyses in Parkinson's disease: A cross-sectional and longitudinal study

Introduction

Parkinson's disease (PD) is an idiopathic disease of the central nervous system characterized by both motor and non-motor symptoms. It is the second most common neurodegenerative disease. Magnetic resonance imaging (MRI) can reveal underlying brain changes associated with PD.

Objective

In this study, structural connectivity and white matter networks were analyzed by diffusion MRI and graph theory in a cohort of patients with PD and a cohort of healthy controls (HC) obtained from the Parkinson's Progression Markers Initiative (PPMI) database in a cross-sectional analysis. Furthermore, we investigated longitudinal changes in the PD cohort over 36 months.

Result

Compared with the control group, participants with PD showed lower structural connectivity in several brain areas, including the corpus callosum, fornix, and uncinate fasciculus, which were also confirmed by a large effect-size. Additionally, altered connectivity between baseline and after 36 months was found in different network paths inside the white matter with a medium effect-size. Network analysis showed trends toward lower network density in PD compared with HC at baseline and after 36 months, though not significant after correction. Significant differences were observed in nodal degree and strength in several nodes.

Conclusion

In conclusion, altered structural and network metrics in several brain regions, such as corpus callosum, fornix, and cingulum were found in PD, compared to HC. We also report altered connectivity in the PD group after 36 months, reflecting the impact of both PD pathology and aging processes. These results indicate that structural and network metrics might yield insight into network reorganization that occurs in PD.

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm

In order to study the therapeutic neuroprotective effect of deep brain stimulation (DBS) in Parkinson's disease (PD), based on the deep learning algorithm, this study combines with magnetic resonance imaging (MRI) image analysis technology to study the clinical efficacy of DBS in the surgical treatment of PD and the neuroprotective and neurological recovery effects after surgery. Establish a deep learning algorithm model based on MRI image analysis technology, comparison of UPDRS motor status assessment and the improvement of daily life ability before and after DBS surgery, evaluate the accuracy rate and the detection speed of the model. The models constructed in this study have an accuracy rate of more than 90% in the PD detection test, and the detection speed of the algorithm model under the condition of big data is between 60 and 200 ms. DBS significantly improve a series of clinical symptoms in patients with PD. The deep learning algorithm model based on MRI image analysis technology in this paper has a certain effect. DBS operation can improve the symptoms of PD, and has the effect of neuroprotection and neurological recovery.

Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms

The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.

Potential Link Between Cognition and Motor Reserve in Patients With Parkinson's Disease

Objective

To investigate whether there is a link between cognitive function and motor reserve (i.e., individual capacity to cope with nigrostriatal dopamine depletion) in patients with newly diagnosed Parkinson's disease (PD).

Methods

A total of 163 patients with drug-naïve PD who underwent 18F-FP-CIT PET, brain MRI, and a detailed neuropsychological test were enrolled. We estimated individual motor reserve based on initial motor deficits and striatal dopamine depletion using a residual model. We performed correlation analyses between motor reserve estimates and cognitive composite scores. Diffusion connectometry analysis was performed to map the white matter fiber tracts, of which fractional anisotropy (FA) values were well correlated with motor reserve estimates. Additionally, Cox regression analysis was used to assess the effect of initial motor reserve on the risk of dementia conversion.

Results

The motor reserve estimate was positively correlated with the composite score of the verbal memory function domain (γ = 0.246) and with the years of education (γ = 0.251). Connectometry analysis showed that FA values in the left fornix were positively correlated with the motor reserve estimate, while no fiber tracts were negatively correlated with the motor reserve estimate. Cox regression analysis demonstrated that higher motor reserve estimates tended to be associated with a lower risk of dementia conversion (hazard ratio, 0.781; 95% confidence interval, 0.576-1.058).

Conclusion

The present study demonstrated that the motor reserve estimate was well correlated with verbal memory function and with white matter integrity in the left fornix, suggesting a possible link between cognition and motor reserve in patients with PD.

Association between white matter alterations on diffusion tensor imaging and incidence of frailty in older adults with cardiometabolic diseases

Diffusion tensor imaging (DTI) can be used for the early detection of abnormal changes in the integrity of cerebral white matter tracts, and we have previously reported that these changes are associated with indices of early atherosclerotic lesions. Although these changes have been demonstrated to be associated with the incidence of frailty in older adults, no studies have investigated this relationship in patients at high risk for vascular disease. In this longitudinal study, we followed outpatients with cardiometabolic diseases for a maximum of 6 years (median, 3 years) and evaluated the association of baseline DTI data of seven white matter tracts with the incidence of frailty. The modified version of the Cardiovascular Health Study criteria and the Kihon Checklist were used as indices of frailty; fractional anisotropy (FA) and mean diffusivity (MD) were used as indices of white matter changes. Patients who developed frailty based on both indices had low FA and high MD in many of the tracts tested, with the most significant difference found in the MD of the anterior thalamic radiation (ATR). Cox proportional hazard model analysis revealed a significantly high risk of frailty defined by both indices in the groups with high MD values in the left ATR. Similar results were found in patients with diabetes mellitus but not in those without diabetes mellitus. Therefore, abnormalities in the integrity of the left ATR could be associated with the progression of frailty in older adults with cardiometabolic disease, particularly those with diabetes mellitus.

Gray and white matter alterations in different predominant side and type of motor symptom in Parkinson's disease

Background

Parkinson's disease (PD) is highly heterogeneous reflected by different affected side of body and type of motor symptom. We aim to explore clinical characteristics and underlying brain structure alterations in PD with different predominant sides and motor types.

Methods

We recruited 161 PD patients and 50 healthy controls (HC). Patients were classified into four subtypes according to their predominant side and motor type: left akinetic/rigid‐dominant (LAR), left tremor‐dominant (LTD), right akinetic/rigid‐dominant (RAR), and right tremor‐dominant (RTD). All participants assessed motor and cognitive performances, then underwent T1‐weighted and diffusion tensor imaging scanning. A general linear model was used to compare neuroimaging parameters among five groups.

Results

Among four PD subtypes, patients of LAR subtype experienced the worst motor impairment, and only this subtype showed worse cognitive performance compared with HC. Compared with HC and other subtypes, LAR subtype showed a significant reduction in cortical thickness of the right caudal‐anterior‐cingulate gyrus and fractional anisotropy of the right cingulum bundle.

Conclusions

We demonstrated that LAR subtype had the worst clinical performance, which the severer damage in the right cingulate region might be the underlying mechanism. This study underscores the importance of classifying PD subtypes based on both the side and type of motor symptom for clinical intervention and research to optimize behavioral outcomes in the future.

High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced steady-state free precession frequency profile

We propose to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, that is, mean, axial, and radial diffusivity (MD, AD, and RD), fractional anisotropy (FA), as well as the spherical coordinates (azimuth Φ and inclination ϴ) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased (e.g. in corpus callosum). The inclination ϴ was well predicted for anisotropic WM structures, while the azimuth Φ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultrahigh field strengths in clinically relevant scan times.

The way forward for cognition in Parkinson's disease

This is the concluding chapter in our volume on cognition in Parkinson's disease.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

R2* relaxometry analysis for mapping of white matter alteration in Parkinson's disease with mild cognitive impairment

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R2* relaxometry analysis combined with QSM revealed detail of WM alteration in PD-MCI.

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R2* relaxometry analysis can detect slight demyelination in PD-MCI.

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R2* value shows potential for early evaluation of cognitive decline in PD.

Background

R2* relaxometry analysis combined with quantitative susceptibility mapping (QSM), which has high sensitivity to iron deposition, can distinguish microstructural changes of the white matter (WM) and iron deposition, thereby providing a sensitive and biologically specific measure of the WM owing to the changes in myelin and its surrounding environment. This study aimed to explore the microstructural WM alterations associated with cognitive impairment in patients with Parkinson’s disease (PD) using R2* relaxometry analysis combined with QSM.

Materials and methods

We enrolled 24 patients with PD and mild cognitive impairment (PD-MCI), 22 patients with PD and normal cognition (PD-CN), and 19 age- and sex-matched healthy controls (HC). All participants underwent Montreal Cognitive Assessment (MoCA) and brain magnetic resonance imaging, including structural three-dimensional T1-weighted images and multiple spoiled gradient echo sequence (mGRE). The R2* and susceptibility maps were estimated from the multiple magnitude images of mGRE. The susceptibility maps were used for verifying iron deposition in the WM. The voxel-based R2* of the entire WM and its correlation with cognitive performance were analyzed.

Results

In the voxel-based group comparisons, the R2* in the PD-MCI group was lower in some WM regions, including the corpus callosum, than R2* in the PD-CN and HC groups. The mean susceptibility values in almost all brain regions were negative and close-to-zero values, indicating no detectable paramagnetic iron deposition in the WM of all subjects. There was a significant positive correlation between R2* and MoCA in some regions of the WM, mainly the corpus callosum and left hemisphere.

Conclusion

R2* relaxometry analysis for WM microstructural changes provided further biologic insights on demyelination and changes in the surrounding environment, supported by the QSM results demonstrating no iron existence. This analysis highlighted the potential for the early evaluation of cognitive decline in patients with PD.

Deep brain stimulation in Parkinson's disease: analysis of brain fractional anisotropy differences in operated patients

INTRODUCTION

Deep brain stimulation (DBS) of the subthalamic nucleus is currently an evidence-based therapeutic option for motor symptoms in patients with Parkinson's disease (PD), although other non-motor symptoms can be affected by stimulation.

AIM

Our objective is to evaluate the global changes in the connectivity of the large-scale structural network in PD patients that have obtained a benefit from subthalamic DBS.

SUBJECTS AND METHODS

Retrospective study of 31 subjects: 7 PD patients with subthalamic DBS (group A), 12 age and gender-matched non-operated PD (B) and 12 healthy controls (C). All subjects had undergone a 1.5 T brain MRI with DTI. DICOM images were processed with the FSL5.0 software and TBSS tool.

RESULTS

The study group comprised 23 men and 8 women. No statistically significant differences in age, gender, scores on the HandY scale and mean follow-up between group A and B were found, and in age and gender between groups A and C. Statistical analysis revealed differences in the fractional anisotropy of the different groups in certain areas: bilateral corticospinal tract, anterior thalamic radiations, bilateral fronto-occipital fascicle, both superior longitudinal fascicles, and left inferior longitudinal fascicle.

CONCLUSIONS

In our series, PD patients treated with bilateral subthalamic DBS showed a significantly higher fractional anisotropy in widespread areas of the cerebral white matter; suggesting that neuromodulation produces connectivity changes in different neural networks.

White matter microstructure in Parkinson's disease with and without elevated rapid eye movement sleep muscle tone

People with Parkinson's disease who have elevated muscle activity during rapid eye movement sleep (REM sleep without atonia) typically have a worse motor and cognitive impairment compared with those with normal muscle atonia during rapid eye movement sleep. This study used tract-based spatial statistics to compare diffusion MRI measures of fractional anisotropy, radial, mean and axial diffusivity (measures of axonal microstructure based on the directionality of water diffusion) in white matter tracts between people with Parkinson's disease with and without rapid eye movement sleep without atonia and controls and their relationship to measures of motor and cognitive function. Thirty-eight individuals with mild-to-moderate Parkinson's disease and 21 matched control subjects underwent ultra-high field MRI (7 T), quantitative motor assessments of gait and bradykinesia and neuropsychological testing. The Parkinson's disease cohort was separated post hoc into those with and without elevated chin or leg muscle activity during rapid eye movement sleep based on polysomnography findings. Fractional anisotropy was significantly higher, and diffusivity significantly lower, in regions of the corpus callosum, projection and association white matter pathways in the Parkinson's group with normal rapid eye movement sleep muscle tone compared with controls, and in a subset of pathways relative to the Parkinson's disease group with rapid eye movement sleep without atonia. The Parkinson's disease group with elevated rapid eye movement sleep muscle tone showed significant impairments in the gait and upper arm speed compared with controls and significantly worse scores in specific cognitive domains (executive function, visuospatial memory) compared with the Parkinson's disease group with normal rapid eye movement sleep muscle tone. Regression analyses showed that gait speed and step length in the Parkinson's disease cohort were predicted by measures of fractional anisotropy of the anterior corona radiata, whereas elbow flexion velocity was predicted by fractional anisotropy of the superior corona radiata. Visuospatial memory task performance was predicted by the radial diffusivity of the posterior corona radiata. These findings show that people with mild-to-moderate severity of Parkinson's disease who have normal muscle tone during rapid eye movement sleep demonstrate compensatory-like adaptations in axonal microstructure that are associated with preserved motor and cognitive function, but these adaptations are reduced or absent in those with increased rapid eye movement sleep motor tone.

Changed Cerebral White Matter Structural Network Topological Characters and Its Correlation with Cognitive Behavioral Abnormalities in Narcolepsy Type 1

OBJECTIVE

In the current study we investigated topological abnormalities of the cerebral white matter networks in narcolepsy type 1 (NT1) patients and its relationship with their cognitive abnormalities using diffusion tensor imaging (DTI) technology.

METHODS

DTI and the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) were applied to 30 NT1 patients and 30 age-matched healthy controls. DTI studies were also carried using the 3T MRI system. Next, DTI data was used to establish a cerebral white matter network for all subjects and graph theory was applied to analyze the topological characteristics of the white matter structural network. Topographical parameters (such as local efficiency (Eloc), global efficiency (Eglob) and small-world (σ)) between NT1 patients and controls were then compared. The correlation between MoCA-BJ scores and topological parameters was also analyzed.

RESULTS

MoCA-BJ scores in NT1 patients were lower than those in the healthy controls. Compared with healthy controls, the global efficiency of the white matter network and attributes of the small world network were significantly reduced in NT1 patients. Finally, the global efficiency of the white matter structural network was related to the MoCA-BJ score of NT1 patients.

CONCLUSION

The abnormal topological characteristics of the white matter structural network in NT1 patients may be associated with their cognitive impairment.

White matter changes in drug-naïve Parkinson's disease patients with impulse control & probable REM sleep behavior disorders

BACKGROUND

According to epidemiological studies, Parkinson's disease (PD) patients with probable REM sleep behavior disorder (pRBD) are more prone to develop impulse control disorders (ICDs), which is shown to be present in drug-naïve PD patients, and vice versa.

OBJECTIVES

To investigate white-matter integrity differences, with and without comorbid pRBD and ICDs.

METHODS

149 de-novo PD patients and 30 age- and gender-matched controls from the Parkinson's Progression Markers Initiative were studied. PD subjects were categorized into four groups with and without these comorbidities. We investigated the white matter integrity differences between these groups.

RESULTS

PDs with only ICDs manifested greater fractional anisotropy (FA) and lower mean diffusivity (MD) in ipsilateral cerebellar connections when compared to controls and to Parkinson's with both comorbid disorders. In contrast, significantly lower FA and higher MD in the ipsilateral fornix-stria-terminalis was observed in PDs with only pRBD compared to controls and to PDs without either comorbid disorder. Also, PDs with only pRBD manifested greater FA in contralateral putamen when compared to controls.

CONCLUSIONS

Our results suggest the presence of an underlying neural network in PDs with ICDs, particularly involving cerebellar connections, which makes the subjects susceptible to pRBD. Lower white-matter integrity in the fornix of PDs with only pRBD suggests a neuropathological pathway specific to sleep behavior disorder, independent of impulse control disorders. Greater white-matter integrity observed in PDs without comorbid ICDs, regardless of their comorbid pRBD status, might reflect compensatory mechanisms. Targeted therapies for this particular neuropathology may help prevent these comorbidities.

A biocompatible and injectable hydrogel to boost the efficacy of stem cells in neurodegenerative diseases treatment

AIMS

Stem cell therapies emerged as treatment modalities with potential to cure neurodegenerative diseases (NDs). However, despite high expectations, their clinical use is still limited. Critical issues in treatment outcomes may be related to stem cells formulation and administration route. We develop a hydrogel as a cell carrier, consisting of compounds (phospholipids and hyaluronic acid-HA) naturally present in the central nervous system (CNS). The HA-based hydrogel physically crosslinked with liposomes is designed for direct injection into the CNS to significantly increase the bone marrow mesenchymal stem cells (BMSCs) bioavailability.

MATERIALS AND METHODS

Hydrogel compatibility is confirmed in vitro with BMSCs and in vivo through its intracerebroventricular injection in rats. To assess its efficacy, the main cause of chronic neurologic disability in young adults is selected, namely multiple sclerosis (MS). The efficacy of the developed formulation containing a lower number of cells than previously reported is demonstrated using an experimental autoimmune encephalomyelitis (EAE) rat model.

KEY FINDINGS

The distribution of the engineered hydrogel into corpus callosum can be ideal for NDs treatment, since damage of this white matter structure is responsible for important neuronal deficits. Moreover, the BMSCs-laden hydrogel significantly decreases disease severity and maximum clinical score and eliminated the relapse.

SIGNIFICANCE

The engineering of advanced therapies using this natural carrier can result in efficacious treatments for MS and related debilitating conditions.

Subclinical Atherosclerosis, Vascular Risk Factors, and White Matter Alterations in Diffusion Tensor Imaging Findings of Older Adults With Cardiometabolic Diseases

White matter abnormalities may reflect cerebral microvessel disease. Diffusion tensor imaging (DTI) can help detect early changes in white matter integrity in each tract. However, studies investigating the relationship between subclinical atherosclerosis markers and white matter alterations in DTI findings are limited. This study aimed to examine associations between cardiovascular risk factors and indices of subclinical atherosclerosis-ankle brachial index (ABI), brachial-ankle pulse wave velocity (baPWV), and carotid artery intima-media thickness (IMT)-and altered white matter integrity in older patients. A total of 224 patients (aged ≥65 years) with cardiometabolic disease who underwent magnetic resonance imaging (MRI) and either plethysmography or cervical ultrasound at the start of the 3-year observational study period were included in this study. We measured fractional anisotropy (FA) and mean diffusivity (MD), which are indices of white matter integrity in seven white matter tracts. In a univariate analysis, lower ABI and higher baPWV values were associated with FA or MD abnormalities in several tracts, whereas IMT was scarcely associated with such change. In addition, high blood pressure and glycoalbumin/glycohemoglobin ratio (GA/HbA1c) and low body mass index (BMI) and triglyceride (TG) levels were associated with FA or MD abnormalities. In a multivariate analysis adjusted for age, sex, BMI, diastolic blood pressure, TG, and GA/HbA1c, the associations between ABI and FA or MD remained in all of either side of the following tracts: anterior thalamic radiation, forceps minor, inferior frontooccipital fasciculus (p < 0.001 for all) and superior longitudinal fasciculus (SLF; p < 0.05), whereas most of those between baPWV and FA or MD disappeared except for SLF (p < 0.05). These results indicate that low ABI could be an indicator of white matter abnormalities.

Neural correlates of self-awareness of cognitive deficits in non-demented patients with Parkinson's disease

BACKGROUND AND PURPOSE

The aim was to investigate the neural correlates of impaired self-awareness of cognitive deficits (IACd) in non-demented patients with Parkinson's disease (PD).

METHODS

This cross-sectional study enrolled 153 drug-naïve and non-demented PD patients who underwent brain magnetic resonance imaging, dopamine transporter (DAT) positron emission tomography, detailed neuropsychological testing, and the Cognitive Complaints Interview at baseline. Based on the presence of mild cognitive impairment and subjective cognitive complaints, patients were grouped into those with IACd (PD-IACd+, n = 33) and those with normal recognition of cognitive function (n = 82) or underestimation of cognitive function (n = 38). Cortical thickness, white matter (WM) integrity, DAT availability and cognitive function were compared between the groups.

RESULTS

The prevalence of IACd was 21.6% in drug-naïve patients with PD. The PD-IACd+ group had a lower z-score in the Stroop color reading test than the other groups. Patients in the PD-IACd+ group had WM disintegrity, especially in the genu of the corpus callosum and anterior limb of the internal capsule, compared to those without IACd, whilst cortical thickness or striatal DAT availability was comparable regardless of the presence of IACd. Amongst patients with mild cognitive impairment, those with IACd had more severe WM disintegrity than those without IACd.

CONCLUSION

Structural connectivity between and from the frontal lobes is closely associated with self-awareness of cognitive deficits in PD. Evaluating frontal structural connectivity from the early stages of PD will be important in assessing the actual cognitive and daily life performance of patients with PD.

HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease

Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.

Altered microstructural properties of superficial white matter in patients with Parkinson's disease

Parkinson's disease (PD), a chronic neurodegenerative disease, is characterized by sensorimotor and cognitive deficits. Previous diffusion tensor imaging (DTI) studies found abnormal DTI metrics in white matter bundles, such as the corpus callosum, cingulate, and frontal-parietal bundles, in PD patients. These studies mainly focused on alterations in microstructural features of long-range bundles within the deep white matter (DWM) that connects pairs of distant cortical regions. However, less is known about the DTI metrics of the superficial white matter (SWM) that connects local cortical regions in PD patients. To determine whether the DTI metrics of the SWM were different between the PD patients and the healthy controls, we recruited DTI data from 34 PD patients and 29 gender- and age-matched healthy controls. Using a probabilistic tractographic approach, we first defined a population-based SWM mask across all the subjects. Using a tract-based spatial statistical (TBSS) analytic approach, we then identified the SWM bundles showing abnormal DTI metrics in the PD patients. We found that the PD patients showed significantly lower DTI metrics in the SWM bundles connecting the sensorimotor cortex, cingulate cortex, posterior parietal cortex (PPC), and parieto-occipital cortex than the healthy controls. We also found that the clinical measures in the PD patients was significantly negatively correlated with the fractional anisotropy in the SWM (FA_SWM) that connects core regions in the default mode network (DMN). The FA_SWM in the bundles that connected the PPC was significantly positively correlated with cognitive performance in the PD patients. Our findings suggest that SWM may serve as the brain structural basis underlying the sensorimotor deficits and cognitive degeneration in PD patients.

Diffusion tensor imaging reveals neuronal microstructural changes in myalgic encephalomyelitis/chronic fatigue syndrome

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients suffer from a variety of physical and neurological complaints indicating the central nervous system plays a role in ME/CFS pathophysiology. Diffusion tensor imaging (DTI) has been used to study microstructural changes in neurodegenerative diseases. In this study, we evaluated DTI parameters to investigate microstructural abnormalities in ME/CFS patients. We estimated DTI parameters in 25 ME/CFS patients who met Fukuda criteria (ME/CFS_Fukuda ), 18 ME/CFS patients who met International Consensus Criteria (ICC) (ME/CFS_ICC ) only and 26 healthy control (HC) subjects. In addition to voxel-based DTI-parameter group comparisons, we performed voxel-based DTI-parameter interaction-with-group regressions with clinical and autonomic measures to test for abnormal regressions. Group comparisons between ME/CFS_ICC and HC detected significant clusters (a) with decreased axial diffusivity (p = .001) and mean diffusivity (p = .01) in the descending cortico-cerebellar tract in the midbrain and pons and (b) with increased transverse diffusivity in the medulla. The mode of anisotropy was significantly decreased (p = .001) in a cluster in the superior longitudinal fasciculus region. Voxel-based group comparisons between ME/CFS_Fukuda and HC did not detect significant clusters. For ME/CFS_ICC and HC, DTI parameter interaction-with-group regressions were abnormal for the clinical measures of information processing score, SF36 physical, sleep disturbance score and respiration rate in both grey and white matter regions. Our study demonstrated that DTI parameters are sensitive to microstructural changes in ME/CFS_ICC and could potentially act as an imaging biomarker of abnormal pathophysiology in ME/CFS. The study also shows that strict case definitions are essential in investigation of the pathophysiology of ME/CFS.

Hippocampal correlates of episodic memory in Parkinson's disease: A systematic review of magnetic resonance imaging studies

The present review asks whether magnetic resonance imaging (MRI) studies are able to define neural correlates of episodic memory within the hippocampus in Parkinson's disease (PD). Systematic searches were performed in PubMed, Web of Science, Medline, CINAHL, and EMBASE using search terms related to structural and functional MRI (fMRI), the hippocampus, episodic memory, and PD. Risk of bias was assessed for each study using the Newtown-Ottawa Scale. Thirty-nine studies met inclusion criteria; eight fMRI, seven diffusion MRI (dMRI), and 24 structural MRI (14 exploring whole hippocampus and 10 exploring hippocampal subfields). Critical analysis of the literature revealed mixed evidence from functional and dMRI, but stronger evidence from sMRI of the hippocampus as a biomarker for episodic memory impairment in PD. Hippocampal subfield studies most often implicated CA1, CA3/4, and subiculum volume in episodic memory and cognitive decline in PD. Despite differences in imaging methodology, study design, and sample characteristics, MRI studies have helped elucidate an important neural correlate of episodic memory impairment in PD with both clinical and theoretical implications. Natural progression of this work encourages future research on hippocampal subfield function as a potential biomarker of, or therapeutic target for, episodic memory dysfunction in PD.

Role of Diffusion Tensor Imaging in Parkinson's Disease Diagnosis

Background: Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease that affects the dopamine-containing neurons. In this study, the role of the Diffusion Tensor imaging (DTI) method was investigated in the detection of PD. Objectives: The purpose of this study was to investigate the microstructural damage of the brain's white matter in PD using a non-invasive DTI technique. Methods: Twenty patients with PD were studied with comprehensive clinical assessments and DTI data. Also, 10 normal subjects were investigated. Fractional anisotropic (FA) and mean diffusivity (MD) values were calculated by drawing region of interest (ROI) on eight distinctive areas of the brain. Results: The level of FA and MD in substantia nigra (SN) was significantly different between the PD and healthy control (HC) groups. Also, differences were found in DTI parameters between PD and HC groups in some regions, such as genu, anterior limb of internal capsule (ALIC), splenium, and putamen. Conclusions: To summarize, DTI as a non-invasive method can be useful in the detection of Parkinson's disease.

Hybrid diffusion imaging reveals altered white matter tract integrity and associations with symptoms and cognitive dysfunction in chronic traumatic brain injury

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Hybrid Diffusion Imaging (HYDI) detects white matter associations in patients with cTBI.

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The advanced diffusion model NODDI was more sensitive in detecting between-group differences than classic DTI.

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DTI appeared to be just as sensitive as NODDI for detecting white matter correlations with self-reported symptoms.

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This study highlights the advantages of acquiring both DTI and NODDI to fully characterize white matter microstructure in cTBI.

The detection and association of in vivo biomarkers in white matter (WM) pathology after acute and chronic mild traumatic brain injury (mTBI) are needed to improve care and develop therapies. In this study, we used the diffusion MRI method of hybrid diffusion imaging (HYDI) to detect white matter alterations in patients with chronic TBI (cTBI). 40 patients with cTBI presenting symptoms at least three months post injury, and 17 healthy controls underwent magnetic resonance HYDI. cTBI patients were assessed with a battery of neuropsychological tests. A voxel-wise statistical analysis within the white matter skeleton was performed to study between group differences in the diffusion models. In addition, a partial correlation analysis controlling for age, sex, and time after injury was performed within the cTBI cohort, to test for associations between diffusion metrics and clinical outcomes. The advanced diffusion modeling technique of neurite orientation dispersion and density imaging (NODDI) showed large clusters of between-group differences resulting in lower values in the cTBI across the brain, where the single compartment diffusion tensor model failed to show any significant results. However, the diffusion tensor model appeared to be just as sensitive in detecting self-reported symptoms in the cTBI population using a within-group correlation. To the best of our knowledge this study provides the first application of HYDI in evaluation of cTBI using combined DTI and NODDI, significantly enhancing our understanding of the effects of concussion on white matter microstructure and emphasizing the utility of full characterization of complex diffusion to diagnose, monitor, and treat brain injury.

Support vector machine based classification of smokers and nonsmokers using diffusion tensor imaging

Despite significant progress in treatments for smoking cessation, smoking continues to be a significant public health concern, especially in young adulthood. Thus, developing a predictive model that can classify and characterize the brain-based biomarkers predicting smoking status would be imperative to improving treatment development. In this study, we applied a support vector machine-based classification method to discriminate 70 young male smokers and 70 matched nonsmokers using their diffusion tensor imaging (DTI) data. The classification procedure achieved an average accuracy of 88.6% and an average area under the curve of 0.95. The most discriminative features that contributed to the classification were primarily located in the sagittal stratum (SS), external capsule (EC), superior longitudinal fasciculus (SLF), anterior corona radiata (ACR) and inferior front-occipital fasciculus (IFOF). The following regression analysis showed a significant negatively correlation between the average RD values of the left ACR (r = -0.247, p = 0.039) and FTND. The average MD values in the right EC (r = -0.254, p = 0.034) and RD values in the right IFOF (r = -0.240, p = 0.046) were inversely associated with pack-years. Our findings indicate that the discriminative white matter (WM) features as brain biomarkers provide great predictive power for smoking status and suggest that machine learning techniques can reveal underlying smoking-related neurobiology.

Executive function network's white matter alterations relate to Parkinson's disease motor phenotype

Parkinson's disease (PD) patients with postural instability and gait disorder phenotype (PIGD) are at high risk of cognitive deficits compared to those with tremor dominant phenotype (TD). Alterations of white matter (WM) integrity can occur in patients with normal cognitive functions (PD-N). However, the alterations of WM integrity related to cognitive functions in PD-N, especially in these two motor phenotypes, remain unclear. Diffusion tensor imaging (DTI) is a non-invasive neuroimaging method to evaluate WM properties and by applying DTI tractography, one can identify WM tracts connecting functional regions. Here, we 1) compared the executive function (EF) in PIGD phenotype with normal cognitive functions (PIGD-N) and TD phenotype with normal cognitive functions (TD-N) phenotypes; 2) used DTI tractography to evaluated differences in WM alterations between these two phenotypes within a task-based functional network; and 3) examined the WM integrity alterations related to EF in a whole brain network for PD-N patients regardless of phenotypes. Thirty-four idiopathic PD-N patients were classified into two groups based on phenotypes: TD-N and PIGD-N, using an algorithm based on UPDRS part III. Neuropsychological tests were used to evaluate patients' EF, including the Trail making test part A and B, the Stroop color naming, the Stroop word naming, the Stroop color-word interference task, as well as the FAS verbal fluency task and the animal category fluency tasks. DTI measures were calculated among WM regions associated with the verbal fluency network defined from previous task fMRI studies and compared between PIGD-N and TD-N groups. In addition, the relationship of DTI measures and verbal fluency scores were evaluated for our full cohort of PD-N patients within the whole brain network. These values were also correlated with the scores of the FAS verbal fluency task. Only the FAS verbal fluency test showed significant group differences, having lower scores in PIGD-N when compared to TD-N phenotype (p < 0.05). Compared to the TD-N, PIGD-N group exhibited significantly higher MD and RD in the tracts connecting the left superior temporal gyrus and left insula, and those connecting the right pars opercularis and right insula. Moreover, compared to TD-N, PIGD-N group had significantly higher RD in the tracts connecting right pars opercularis and right pars triangularis, and the tracts connecting right inferior temporal gyrus and right middle temporal gyrus. For the entire PD-N cohort, FAS verbal fluency scores positively correlated with MD in the superior longitudinal fasciculus (SLF). This study confirmed that PIGD-N phenotype has more deficits in verbal fluency task than TD-N phenotype. Additionally, our findings suggest: (1) PIGD-N shows more microstructural changes related to FAS verbal fluency task when compared to TD-N phenotype; (2) SLF plays an important role in FAS verbal fluency task in PD-N patients regardless of motor phenotypes.