Network Structure and Function in Parkinson's Disease

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Parkinson's disease speech production network as determined by graph-theoretical network analysis

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network

BACKGROUND AND PURPOSE

Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored.

METHODS

We explored 2-[¹⁸ F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods.

RESULTS

The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization.

CONCLUSIONS

The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning

BACKGROUND

Metabolic brain imaging with 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes.

METHODS

We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations.

RESULTS

Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC.

CONCLUSION

Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.

Network Reconfiguration Among Cerebellar Visual, and Motor Regions Affects Movement Function in Spinocerebellar Ataxia Type 3

Background

Spinocerebellar ataxia type 3 (SCA3) is a rare movement disorder characterized with ataxia. Previous studies on movement disorders show that the whole-brain functional network tends to be more regular, and these reconfigurations correlate with genetic and clinical variables.

Methods

To test whether the brain network in patients with SCA3 follows a similar reconfiguration course to other movement disorders, we recruited 41 patients with SCA3 (mean age = 40.51 ± 12.13 years; 23 male) and 41 age and sex-matched healthy individuals (age = 40.10 ± 11.56 years; 24 male). In both groups, the whole-brain network topology of resting-state functional magnetic resonance imaging (rs-fMRI) was conducted using graph theory, and the relationships among network topologies, cytosine-adenine-guanine (CAG) repeats, clinical symptoms, and functional connectivity were explored in SCA3 patients using partial correlation analysis, controlling for age and sex.

Results

The brain networks tended to be more regular with a higher clustering coefficient, local efficiency, and modularity in patients with SCA3. Hubs in SCA3 patients were reorganized as the number of hubs increased in motor-related areas and decreased in cognitive areas. At the global level, small-worldness and normalized clustering coefficients were significantly positively correlated with clinical motor symptoms. At the nodal level, the clustering coefficient and local efficiency increased significantly in the visual (bilateral cuneus) and sensorimotor (right cerebellar lobules IV, V, VI) networks and decreased in the cognitive areas (right middle frontal gyrus). The clustering coefficient and local efficiency in the bilateral cuneus gyrus were negatively correlated with clinical motor symptoms. The functional connectivity between right caudate nucleus and bilateral calcarine gyrus were negatively correlated with disease duration, while connectivity between right posterior cingulum gyrus and left cerebellar lobule III, left inferior occipital gyrus and right cerebellar lobule IX was positively correlated.

Conclusion

Our results demonstrate that a more regular brain network occurred in SCA3 patients, with motor and visual-related regions, such as, cerebellar lobules and cuneus gyrus, both forayed neighbor nodes as “resource predators” to compensate for normal function, with motor and visual function having the higher priority comparing with other high-order functions. This study provides new information about the neurological mechanisms underlying SCA3 network topology impairments in the resting state, which give a potential guideline for future clinical treatments.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [ChiCTR1800019901].

Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease

Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as ¹⁸F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (¹⁸F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and ¹⁸F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.

Modeling Parkinson's Disease: Not Only Rodents?

Parkinson’s disease (PD) is a common chronic progressive multifactorial neurodegenerative disease. In most cases, PD develops as a sporadic idiopathic disease. However, in 10%–15% of all patients, Mendelian inheritance of the disease is observed in an autosomal dominant or autosomal recessive manner. To date, mutations in seven genes have been convincingly confirmed as causative in typical familial forms of PD, i.e., SNCA, LRRK2, VPS35, PRKN, PINK1, GBA, and DJ-1. Family and genome-wide association studies have also identified a number of candidate disease genes and a common genetic variability at 90 loci has been linked to risk for PD. The analysis of the biological function of both proven and candidate genes made it possible to conclude that mitochondrial dysfunction, lysosomal dysfunction, impaired exosomal transport, and immunological processes can play important roles in the development of the pathological process of PD. The mechanisms of initiation of the pathological process and its earliest stages remain unclear. The study of the early stages of the disease (before the first motor symptoms appear) is extremely complicated by the long preclinical period. In addition, at present, the possibility of performing complex biochemical and molecular biological studies familial forms of PD is limited. However, in this case, the analysis of the state of the central nervous system can only be assessed by indirect signs, such as the level of metabolites in the cerebrospinal fluid, peripheral blood, and other biological fluids. One of the potential solutions to this problem is the analysis of disease models, in which it is possible to conduct a detailed in-depth study of all aspects of the pathological process, starting from its earliest stages. Many modeling options are available currently. An analysis of studies published in the 2000s suggests that toxic models in rodents are used in the vast majority of cases. However, interesting and important data for understanding the pathogenesis of PD can be obtained from other in vivo models. Within the framework of this review, we will consider various models of PD that were created using various living organisms, from unicellular yeast (Saccharomyces cerevisiae) and invertebrate (Nematode and Drosophila) forms to various mammalian species.

The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration

Brain's modular connectivity gives this organ resilience and adaptability. The ageing process alters the organised modularity of the brain and these changes are further accentuated by neurodegeneration, leading to disorganisation. To understand this further, we analysed modular variability-heterogeneity of modules-and modular dissociation-detachment from segregated connectivity-in two ageing cohorts and a mixed cohort of neurodegenerative diseases. Our results revealed that the brain follows a universal pattern of high modular variability in metacognitive brain regions: the association cortices. The brain in ageing moves towards a segregated modular structure despite presenting with increased modular heterogeneity-modules in older adults are not only segregated, but their shape and size are more variable than in young adults. In the presence of neurodegeneration, the brain maintains its segregated connectivity globally but not locally, and this is particularly visible in dementia with Lewy bodies and Parkinson's disease dementia; overall, the modular brain shows patterns of differentiated pathology.

Co-registration Analysis of Fluorodopa and Fluorodeoxyglucose Positron Emission Tomography for Differentiating Multiple System Atrophy Parkinsonism Type From Parkinson's Disease

It is difficult to differentiate between Parkinson's disease and multiple system atrophy parkinsonian subtype (MSA-P) because of the overlap of their signs and symptoms. Enormous efforts have been made to develop positron emission tomography (PET) imaging to differentiate these diseases. This study aimed to investigate the co-registration analysis of ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET images to visualize the difference between Parkinson's disease and MSA-P. We enrolled 29 Parkinson's disease patients, 28 MSA-P patients, and 10 healthy controls, who underwent both ¹⁸F-fluorodopa and ¹⁸F-flurodeoxyglucose PET scans. Patients with Parkinson's disease and MSA-P exhibited reduced bilateral striatal ¹⁸F-fluorodopa uptake (p < 0.05, vs. healthy controls). Both regional specific uptake ratio analysis and statistical parametric mapping analysis of ¹⁸F-flurodeoxyglucose PET revealed hypometabolism in the bilateral putamen of MSA-P patients and hypermetabolism in the bilateral putamen of Parkinson's disease patients. There was a significant positive correlation between ¹⁸F-flurodeoxyglucose uptake and ¹⁸F-fluorodopa uptake in the contralateral posterior putamen of MSA-P patients (rs = 0.558, p = 0.002). Both ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images showed that the striatum was rabbit-shaped in the healthy control group segmentation analysis. A defective rabbit-shaped striatum was observed in the ¹⁸F-fluorodopa PET image of patients with Parkinson's disease and MSA-P. In the segmentation analysis of ¹⁸F-flurodeoxyglucose PET image, an intact rabbit-shaped striatum was observed in Parkinson's disease patients, whereas a defective rabbit-shaped striatum was observed in MSA-P patients. These findings suggest that there were significant differences in the co-registration analysis of ¹⁸F-flurodeoxyglucose and ¹⁸F-fluorodopa PET images, which could be used in the individual analysis to differentiate Parkinson's disease from MSA-P.

Modulating brain networks associated with cognitive deficits in Parkinson's disease

Parkinson's disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.

Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease

In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [¹⁸F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.

Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study

Background

With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome. This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD.

Methods

Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent ¹⁸F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions.

Results

The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson’s Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient.

Conclusions

The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients.

Clinical Trial Registration ChiCTR-DOC-16008645. http://www.chictr.org.cn/showproj.aspx?proj=13865.

Elevated caudate connectivity in cognitively normal Parkinson's disease patients

Mild cognitive impairment (MCI) is common in Parkinson’s disease patients. However, its underlying mechanism is not well understood, which has hindered new treatment discoveries specific to MCI. The aim of this study was to investigate functional connectivity changes of the caudate nucleus in cognitively impaired Parkinson’s patients. We recruited 18 Parkinson’s disease patients—10 PDNC [normal cognition Parkinson’s disease; Montreal Cognitive Assessment (MoCA) ≥ 26], 8 PDLC (low cognition Parkinson’s disease; MoCA < 26) —and 10 age-matched healthy controls. All subjects were scanned with resting-state functional magnetic resonance imaging (MRI) and perfusion MRI. We analyzed these data for graph theory metrics and Alzheimer’s disease-like pattern score, respectively. A strong positive correlation was found between the functional connectivity of the right caudate nucleus and MoCA scores in Parkinson’s patient groups, but not in healthy control subjects. Interestingly, PDNC’s functional connectivity of the right caudate was significantly higher than both PDLC and healthy controls, while PDLC and healthy controls were not significantly different from each other. We found that Alzheimer’s disease-like metabolic/perfusion pattern score correlated with MoCA scores in healthy controls, but not in Parkinson’s disease. Increased caudate connectivity may be related to a compensatory mechanism found in cognitively normal patients with Parkinson’s disease. Our findings support and complement the dual syndrome hypothesis.

Brain Functional and Structural Signatures in Parkinson's Disease

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). ¹⁸F-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia

Purpose

Positron emission tomography (PET) with ¹⁸F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD.

Methods

FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.

Results

The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).

Conclusion

The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users.

Brain metabolic alterations herald falls in patients with Parkinson's disease

Pathophysiological understanding of gait and balance disorders in Parkinson’s disease is insufficient and late recognition of fall risk limits efficacious follow‐up to prevent or delay falls. We show a distinctive reduction of glucose metabolism in the left posterior parietal cortex, with increased metabolic activity in the cerebellum, in parkinsonian patients 6–8 months before their first fall episode. Falls in Parkinson’s disease may arise from altered cortical processing of body spatial orientation, possibly predicted by abnormal cortical metabolism.

LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks

The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.

Oscillation-specific nodal alterations in early to middle stages Parkinson's disease

BACKGROUND

Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson's disease (PD) across early stage to middle stage by using graph theory-based analysis.

METHODS

Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated.

RESULTS

Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity.

CONCLUSIONS

Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.

Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity

MOTIVATION

Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example.

RESULTS

A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed.

AVAILABILITY

The resulting data and software are available at http://neuroviisas.med.uni-rostock.de/neuroviisas.shtml.

Network imaging biomarkers: insights and clinical applications in Parkinson's disease

Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.

Brain Atrophy and Reorganization of Structural Network in Parkinson's Disease With Hemiparkinsonism

Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with hemiparkinsonism, which could potentially disclose the early biomarker for PD. Thirty-one PD patients with hemiparkinsonism and 37 age- and gender- matched normal controls were included in the present study. First of all, we normalized the left hemisphere of structural images as the contralateral side to the affected limbs. Deformation-based morphometry (DBM) was conducted to evaluate the brain atrophy and/or enlargement. structural networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches (e.g., small-worldness, global, and nodal measures). Significantly decreased deformation values were observed in the temporoparietal regions like bilateral middle temporal gyri, ipsilateral precuneus and contralateral Rolandic operculum extending to supramarginal and postcentral gyri. Lower deformation values in contralateral middle temporal gyrus were negatively correlated with higher motor impairment which was dominated by akinesia/rigidity. Moreover, nodal reorganization of structural network mainly located in frontal, temporal, subcortex and cerebellum was bilaterally explored in PD patients with hemiparkinsonism. Increased nodal properties could be commonly observed in frontal lobes. Disruption of subcortex including basal ganglia and amygdala was detected by nodal local efficiency and nodal clustering coefficient. Twelve hubs, mainly from paralimbic-limbic and heteromodal networks, were disrupted and, alternatively, 14 hubs, most of which were located in frontal lobes, were additionally detected in PD patients with hemiparkinsonism. In conclusion, during hemiparkinsonism period, mild brain atrophy in the temporoparietal regions and widespread reorganization of structural network, e.g., enhanced frontal function and disruption of basal ganglia nodes, occurred in both hemispheres. With our data, we can also argue that MTG contralateral to the affected limbs (expressing clinically verified brain atrophy) might be a potential living biomarker to monitor disease progression. Therefore, the combination of DBM and structural network analyses can provide a comprehensive and sensitive evaluation for potential pathogenesis of early PD patients with hemiparkinsonism.