Alteration of brain structural connectivity in progression of Parkinson's disease: A connectome-wide network analysis

Micron-resolution fiber mapping in histology independent of sample preparation

Detailed knowledge of the brain's nerve fiber network is crucial for understanding its function in health and disease. However, mapping fibers with high resolution remains prohibitive in most histological sections because state-of-the-art techniques are incompatible with their preparation. Here, we present a micron-resolution light-scattering-based technique that reveals intricate fiber networks independent of sample preparation for extended fields of view. We uncover fiber structures in both label-free and stained, paraffin-embedded and deparaffinized, newly-prepared and archived, animal and human brain tissues - including whole-brain sections from the BigBrain atlas. We identify altered microstructures in demyelination and hippocampal neurodegeneration, and show key advantages over diffusion magnetic resonance imaging, polarization microscopy, and structure tensor analysis. We also reveal structures in non-brain tissues - including muscle, bone, and blood vessels. Our cost-effective, versatile technique enables studies of intricate fiber networks in any type of histological tissue section, offering a new dimension to neuroscientific and biomedical research.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

MRI Assessment of Intrinsic Neural Timescale and Gray Matter Volume in Parkinson's Disease

BACKGROUND

Numerous studies have indicated altered temporal features of the brain function in Parkinson's disease (PD), and the autocorrelation magnitude of intrinsic neural signals, called intrinsic neural timescales, were often applied to estimate how long neural information stored in local brain areas. However, it is unclear whether PD patients at different disease stages exhibit abnormal timescales accompanied with abnormal gray matter volume (GMV).

PURPOSE

To assess the intrinsic timescale and GMV in PD.

STUDY TYPE

Prospective.

POPULATION

74 idiopathic PD patients (44 early stage (PD-ES) and 30 late stage (PD-LS), as determined by the Hoehn and Yahr (HY) severity classification scale), and 73 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

3.0 T MRI scanner; magnetization prepared rapid acquisition gradient echo and echo planar imaging sequences.

ASSESSMENT

The timescales were estimated by using the autocorrelation magnitude of neural signals. Voxel-based morphometry was performed to calculate GMV in the whole brain. Severity of motor symptoms and cognitive impairments were assessed using the unified PD rating scale, the HY scale, the Montreal cognitive assessment, and the mini-mental state examination.

STATISTICAL TEST

Analysis of variance; two-sample t-test; Spearman rank correlation analysis; Mann-Whitney U test; Kruskal-Wallis' H test. A P value <0.05 was considered statistically significant.

RESULTS

The PD group had significantly abnormal intrinsic timescales in the sensorimotor, visual, and cognitive-related areas, which correlated with the symptom severity (ρ = -0.265, P = 0.022) and GMV (ρ = 0.254, P = 0.029). Compared to the HC group, the PD-ES group had significantly longer timescales in anterior cortical regions, whereas the PD-LS group had significantly shorter timescales in posterior cortical regions.

CONCLUSION

This study suggested that PD patients have abnormal timescales in multisystem and distinct patterns of timescales and GMV in cerebral cortex at different disease stages. This may provide new insights for the neural substrate of PD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Morphometric similarity differences in drug-naive Parkinson's disease correlate with transcriptomic signatures

BACKGROUND

Differences in cortical morphology have been reported in individuals with Parkinson's disease (PD). However, the pathophysiological mechanism of transcriptomic vulnerability in local brain regions remains unclear.

OBJECTIVE

This study aimed to characterize the morphometric changes of brain regions in early drug-naive PD patients and uncover the brain-wide gene expression correlates.

METHODS

The morphometric similarity (MS) network analysis was used to quantify the interregional structural similarity from multiple magnetic resonance imaging anatomical indices measured in each brain region of 170 early drug-naive PD patients and 123 controls. Then, we applied partial least squares regression to determine the relationship between regional changes in MS and spatial transcriptional signatures from the Allen Human Brain Atlas dataset, and identified the specific genes related to MS differences in PD. We further investigated the biological processes by which the PD-related genes were enriched and the cellular characterization of these genes.

RESULTS

Our results showed that MS was mainly decreased in cingulate, frontal, and temporal cortical areas and increased in parietal and occipital cortical areas in early drug-naive PD patients. In addition, genes whose expression patterns were associated with regional MS changes in PD were involved in astrocytes, excitatory, and inhibitory neurons and were functionally enriched in neuron-specific biological processes related to trans-synaptic signaling and nervous system development.

CONCLUSIONS

These findings advance our understanding of the microscale genetic and cellular mechanisms driving macroscale morphological abnormalities in early drug-naive PD patients and provide potential targets for future therapeutic trials.

Functional connectivity-based classification of rapid eye movement sleep behavior disorder

BACKGROUND

Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose.

METHODS

We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs.

RESULTS

Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients.

CONCLUSIONS

Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

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.

The interplay between structural and functional connectivity in early stage Parkinson's disease patients

The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.

Top-down and bottom-up propagation of disease in the neuronal ceroid lipofuscinoses

Background

The Neuronal Ceroid Lipofuscinoses (NCLs) may be considered distinct neurodegenerative disorders with separate underlying molecular causes resulting from monogenetic mutations. An alternative hypothesis is to consider the NCLs as related diseases that share lipofuscin pathobiology as the common core feature, but otherwise distinguished by different a) initial anatomic location, and b) disease propagation.

Methods

We have tested this hypothesis by comparing known differences in symptomatology and pathology of the CLN1 phenotype caused by complete loss of PPT1 function (i.e., the classical infantile form) and of the classical juvenile CLN3 phenotype. These two forms of NCL represent early onset and rapidly progressing vs. late onset and slowly progressing disease modalities respectively.

Results

Despite displaying similar pathological endpoints, the clinical phenotypes and the evidence of imaging and postmortem studies reveal strikingly different time courses and distributions of disease propagation. Data from CLN1 disease are indicative of disease propagation from the body, with early effects within the spinal cord and subsequently within the brainstem, the cerebral hemispheres, cerebellum and retina. In contrast, the retina appears to be the most vulnerable organ in CLN3, and the site where pathology is first present. Pathology subsequently is present in the occipital connectome of the CLN3 brain, followed by a top-down propagation in which cerebral and cerebellar atrophy in early adolescence is followed by involvement of the peripheral nerves in later adolescence/early twenties, with the extrapyramidal system also affected during this time course.

Discussion

The propagation of disease in these two NCLs therefore has much in common with the “Brain-first” vs. “Body-first” models of alpha-synuclein propagation in Parkinson's disease. CLN1 disease represents a “Body-first” or bottom-up disease propagation and CLN3 disease having a “Brain-first” and top-down propagation. It is noteworthy that the varied phenotypes of CLN1 disease, whether it starts in infancy (infantile form) or later in childhood (juvenile form), still fit with our proposed hypothesis of a bottom-up disease propagation in CLN1. Likewise, in protracted CLN3 disease, where both cognitive and motor declines are delayed, the initial manifestations of disease are also seen in the outer retinal layers, i.e., identical to classical Juvenile NCL disease.

Topological patterns of motor networks in Parkinson’s disease with different sides of onset: A resting-state-informed structural connectome study

Parkinson’s disease (PD) has a characteristically unilateral pattern of symptoms at onset and in the early stages; this lateralization is considered a diagnostically important diagnosis feature. We aimed to compare the graph-theoretical properties of whole-brain networks generated by using resting-state functional MRI (rs-fMRI), diffusion tensor imaging (DTI), and the resting-state-informed structural connectome (rsSC) in patients with left-onset PD (LPD), right-onset PD (RPD), and healthy controls (HCs). We recruited 26 patients with PD (13 with LPD and 13 with RPD) as well as 13 age- and sex-matched HCs. Rs-fMRI and DTI were performed in all subjects. Graph-theoretical analysis was used to calculate the local and global efficiency of a whole-brain network generated by rs-fMRI, DTI, and rsSC. Two-sample t-tests and Pearson correlation analysis were conducted. Significantly decreased global and local efficiency were revealed specifically in LPD patients compared with HCs when the rsSC network was used; no significant intergroup difference was found by using rs-fMRI or DTI alone. For rsSC network analysis, multiple network metrics were found to be abnormal in LPD. The degree centrality of the left precuneus was significantly correlated with the Unified Parkinson’s Disease Rating Scale (UPDRS) score and disease duration (p = 0.030, r = 0.599; p = 0.037, r = 0.582). The topological properties of motor-related brain networks can differentiate LPD and RPD. Nodal metrics may serve as important structural features for PD diagnosis and monitoring of disease progression. Collectively, these findings may provide neurobiological insights into the lateralization of PD onset.

MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation

Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis. Traditional neuroimaging analysis pipelines are implemented based on registration methods, which involve time-consuming optimization steps. Recent related deep learning methods speed up the segmentation pipeline but are limited to distinguishing fuzzy boundaries, especially encountering the multi-grained whole-brain segmentation task, where there exists high variability in size and shape among various anatomical regions. In this article, we propose a deep learning-based network, termed Multi-branch Residual Fusion Network, for the whole brain segmentation, which is capable of segmenting the whole brain into 136 parcels in seconds, outperforming the existing state-of-the-art networks. To tackle the multi-grained regions, the multi-branch cross-attention module (MCAM) is proposed to relate and aggregate the dependencies among multi-grained contextual information. Moreover, we propose a residual error fusion module (REFM) to improve the network's representations fuzzy boundaries. Evaluations of two datasets demonstrate the reliability and generalization ability of our method for the whole brain segmentation, indicating that our method represents a rapid and efficient segmentation tool for neuroimage analysis.

Characterizing mild cognitive impairment in prodromal Parkinson's disease: A community-based study in China

Objective

The International Parkinson and Movement Disorder Society (MDS) has published research criteria for prodromal Parkinson's disease (pPD), which includes cognitive impairment as a prodromal marker. However, the clinical features of mild cognitive impairment (MCI) in pPD remain unknown. Our study aimed to evaluate the frequency and clinical features of mild cognitive impairment of pPD in the elderly in China.

Methods

The cross‐sectional community‐based study recruited 2688 participants aged ≥50 years. Subjects were diagnosed with pPD according to the MDS criteria. Overall, 39 pPD and 22 healthy controls underwent comprehensive clinical and neuropsychological assessment. MCI was also diagnosed by the MDS criteria. Next, we investigated the relationship between clinical factors and cognition.

Results

Among the 2,663 dementia‐free and Parkinson disease (PD)‐free participants, 55 met the criteria for pPD (2.1%) and 23 pPD met the criteria for MCI. Memory, attention/working memory, and executive function were the most frequent impaired domains, and amnestic MCI multidomain phenotype was the most frequent MCI subtype (69.57%) in pPD. Additionally, correlation analysis revealed that the global cognitive performance was negatively related to UPDRS‐III score (r = −0.456, p = 0.004).

Conclusion

MCI, specifically impairment in memory, attention/working memory, and executive domain, is present at the prodromal stage of PD. In addition, cognitive performance is correlated with motor symptoms in pPD. Our results reflect that cognitive profile, combined with motor symptoms, can help clinicians to identify individuals with pPD early, as those would be the optimal candidates for neuroprotective therapy.