Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level

Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia

PURPOSE OF THE REVIEW

In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia.

RECENT FINDINGS

Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.

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.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy

OBJECTIVES

This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process.

RESULTS

The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively.

CONCLUSIONS

Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.

How should we be using biomarkers in trials of disease modification in Parkinson's disease?

The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.

Alterations in Resting-State MR Functional Connectivity of the Central Autonomic Network in Multiple System Atrophy and Relationship with Disease Severity

BACKGROUND

The central autonomic network (CAN) plays a critical role in the body's sympathetic and parasympathetic control. However, functional connectivity (FC) changes of the CAN in patients with multiple system atrophy (MSA) remain unknown.

PURPOSE

To investigate FC alterations of CAN in MSA patients.

STUDY TYPE

Prospective.

POPULATION

Eighty-two subjects (47 patients with MSA [44.7% female, 60.5 ± 6.9 years], 35 age- and sex-matched healthy controls [HC] [57.1% female, 62.5 ± 6.6 years]).

FIELD STRENGTH/SEQUENCE

3-T, resting-state functional magnetic resonance imaging (rs-fMRI) using gradient echo-planar imaging (EPI), T1-weighted three-dimensional magnetization-prepared rapid gradient echo (3D MPRAGE) structural MRI.

ASSESSMENT

FC alterations were explored by using core modulatory regions of CAN as seeds, including midcingulate cortex, insula, amygdala, and ventromedial prefrontal cortex. Bartlett factor score (BFS) derived from a factor analysis of clinical assessments on disease severity was used as a grouping factor for moderate MSA (mMSA: BFS < 0) and severe MSA (sMSA: BFS > 0).

STATISTICAL TESTS

For FC analysis, the one-way ANCOVA with cluster-level family-wise error correction (statistical significance level of P < 0.025), and post hoc t-testing with Bonferroni correction or Tamhane's T2 correction (statistical significance level of adjusted-P < 0.05) were adopted. Correlation was assessed using Pearson correlation or Spearman correlation (statistical significance level of P < 0.05).

RESULTS

Compared with HC, patients with MSA exhibited significant FC aberrances between the CAN and brain areas of sensorimotor control, limbic network, putamen, and cerebellum. For MSA patients, most FC alterations of CAN, especially concerning FC between the right anterior insula and right primary sensorimotor cortices, were found to be significantly correlated with disease severity. FC changes were found to be more significant in sMSA group than in mMSA group when compared with HCs.

DATA CONCLUSION

MSA shows widespread FC changes of CAN, suggesting that abnormal functional integration of CAN may be involved in disease pathogenesis of MSA.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

Linking the cerebellum to Parkinson disease: an update

Parkinson disease (PD) is characterized by heterogeneous motor and non-motor symptoms, resulting from neurodegeneration involving various parts of the central nervous system. Although PD pathology predominantly involves the nigral-striatal system, growing evidence suggests that pathological changes extend beyond the basal ganglia into other parts of the brain, including the cerebellum. In addition to a primary involvement in motor control, the cerebellum is now known to also have an important role in cognitive, sleep and affective processes. Over the past decade, an accumulating body of research has provided clinical, pathological, neurophysiological, structural and functional neuroimaging findings that clearly establish a link between the cerebellum and PD. This Review presents an overview and update on the involvement of the cerebellum in the clinical features and pathogenesis of PD, which could provide a novel framework for a better understanding the heterogeneity of the disease.

Elevated Levels of Serum Biomarkers Associated with Damage to the CNS Neurons and Endothelial Cells Are Linked with Changes in Brain Connectivity in Breast Cancer Patients with Vestibulo-Atactic Syndrome

Vestibulo-atactic syndrome (VAS), which represents a combination of motor and vestibular disorders, can be manifested as a clinical complication of breast cancer treatment and has a significant impact on patients' quality of life. The identification of novel potential biomarkers that might help to predict the onset of VAS and its progression could improve the management of this group of patients. In the current study, the levels of intercellular cell adhesion molecule 1 (ICAM-1), platelet/endothelial cell adhesion molecule 1 (PECAM-1), NSE (neuron-specific enolase), and the antibodies recognizing NR-2 subunit of NMDA receptor (NR-2-ab) were measured in the blood serum of BC survivor patients with vestibulo-atactic syndrome (VAS) and associated with the brain connectome data obtained via functional magnetic resonance imaging (fMRI) studies. A total of 21 patients were registered in this open, single-center trial and compared to age-matched healthy female volunteers (control group) (n = 17). BC patients with VAS demonstrated higher serum levels of ICAM-1, PECAM-1, and NSE and a lower value of NR-2-ab, with values of 654.7 ± 184.8, 115.3 ± 37.03, 49.9 ± 103.9, and 0.5 ± 0.3 pg/mL, respectively, as compared to the healthy volunteers, with 230.2 ± 44.8, 62.8 ± 15.6, 15.5 ± 6.4, and 1.4 ± 0.7 pg/mL. According to the fMRI data (employing seed-to-voxel and ROI-to-ROI methods), in BC patients with VAS, significant changes were detected in the functional connectivity in the areas involved in the regulation of postural-tonic reflexes, the coordination of movements, and the regulation of balance. In conclusion, the detected elevated levels of serum biomarkers may reveal damage to the CNS neurons and endothelial cells that is, in turn, associated with the change in the brain connectivity in this group of patients.

Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages

Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.

Automatic classification of MSA subtypes using Whole-brain gray matter function and Structure-Based radiomics approach

BACKGROUND

This study aims to develop a radiomics method based on the function and structure of whole-brain gray matter to accurately classify multiple system atrophy with predominant Parkinsonism (MSA-P) or predominant cerebellar ataxia (MSA-C).

METHODS

We enrolled 30 MSA-C and 41 MSA-P cases for the internal cohort and 11 MSA-C and 10 MSA-P cases for the external test cohort. We extracted 7,308 features, including gray matter volume (GMV), mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and resting-state functional connectivity (RSFC) from 3D-T1 and Rs-fMR data. Feature selection was conducted with t-test and least absolute shrinkage and selection operator (Lasso). Classification was performed using the support vector machine with linear and RBF kernel (SVM-linear/SVM-RBF), random forest and logistic regression. Model performance was assessed via receiver operating characteristic (ROC) curve and compared with DeLong's test.

RESULTS

Feature selection resulted in 12 features, including 1 ALFF, 1 DC and 10 RSFC. All the classifiers showed remarkable classification performance, especially the RF model which exhibited AUC values of 0.91 and 0.80 in the validation and test datasets, respectively. The brain functional activity and connectivity in the cerebellum, orbitofrontal lobe and limbic system were important features to distinguish MSA subtypes with the same disease severity and duration.

CONCLUSION

Radiomics approach has the potential to support clinical diagnostic systems and to achieve high classification accuracy for distinguishing between MSA-C and MSA-P patients at the individual level.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Transcranial Current Stimulation as a Tool of Neuromodulation of Cognitive Functions in Parkinson’s Disease

Decrease in cognitive function is one of the most common causes of poor life quality and early disability in patients with Parkinson’s disease (PD). Existing methods of treatment are aimed at both correction of motor and non-motor symptoms. Methods of adjuvant therapy (or complementary therapy) for maintaining cognitive functions in patients with PD are of interest. A promising subject of research in this regard is the method of transcranial electric current stimulation (tES). Here we reviewed the current understanding of the pathogenesis of cognitive impairment in PD and of the effects of transcranial direct current stimulation and transcranial alternating current stimulation on the cognitive function of patients with PD-MCI (Parkinson’s Disease–Mild Cognitive Impairment).

Consistent Changes in Cortico-Subthalamic Directed Connectivity Are Associated With the Induction of Parkinsonism in a Chronically Recorded Non-human Primate Model

Parkinson’s disease is a neurological disease with cardinal motor signs including bradykinesia and tremor. Although beta-band hypersynchrony in the cortico-basal ganglia network is thought to contribute to disease manifestation, the resulting effects on network connectivity are unclear. We examined local field potentials from a non-human primate across the naïve, mild, and moderate disease states (model was asymmetric, left-hemispheric dominant) and probed power spectral density as well as cortico-cortical and cortico-subthalamic connectivity using both coherence and Granger causality, which measure undirected and directed effective connectivity, respectively. Our network included the left subthalamic nucleus (L-STN), bilateral primary motor cortices (L-M1, R-M1), and bilateral premotor cortices (L-PMC, R-PMC). Results showed two distinct peaks (Peak A at 5–20 Hz, Peak B at 25–45 Hz) across all analyses. Power and coherence analyses showed widespread increases in power and connectivity in both the Peak A and Peak B bands with disease progression. For Granger causality, increases in Peak B connectivity and decreases in Peak A connectivity were associated with the disease. Induction of mild disease was associated with several changes in connectivity: (1) the cortico-subthalamic connectivity in the descending direction (L-PMC to L-STN) decreased in the Peak A range while the reciprocal, ascending connectivity (L-STN to L-PMC) increased in the Peak B range; this may play a role in generating beta-band hypersynchrony in the cortex, (2) both L-M1 to L-PMC and R-M1 to R-PMC causalities increased, which may either be compensatory or a pathologic effect of disease, and (3) a decrease in connectivity occurred from the R-PMC to R-M1. The only significant change seen between mild and moderate disease was increased right cortical connectivity, which may reflect compensation for the left-hemispheric dominant moderate disease state.

Combination of structural MRI, functional MRI and brain PET-CT provide more diagnostic and prognostic value in patients of cerebellar ataxia associated with anti-Tr/DNER: a case report

Background

Brain magnetic resonance imaging (MRI) rarely reveals structural changes in patients with suspected anti-Tr/DNER encephalitis and thus provides very limited information. Here, we combined structural MRI, functional MRI, and positron emission tomography-computed tomography (PET-CT) findings to characterize this rare disorder in a patient.

Case presentation

A 43-year-old woman presented with progressive cerebellar ataxia, memory impairment, anxiety, and depression. Anti-Tr antibodies were detected in both her serum (1:10) and cerebrospinal fluid (1:10). A diagnosis of anti-Tr-positive autoimmune cerebellar ataxia was established. The patient’s symptoms were worse, but her brain MRI was normal. Meanwhile, voxel-based morphometry analysis showed bilateral reduced cerebellar volume, especially in the posterior lobe and uvula of the cerebellum and the middle of the left temporal lobe compared with 6 sex- and age-matched healthy subjects (6 females, 43 ± 2 years; p < 0.05). Using seed-based functional connectivity analysis, decreased connectivity between the posterior cingulate cortex/precuneus and left frontal lobe compared to the control group (p < 0.05) was detected. PET-CT revealed bilateral hypometabolism in the cerebellum and relative hypermetabolism in the cerebellar vermis and bilateral frontal lobe, but no malignant changes.

Conclusions

A combination of structural MRI, functional MRI, and brain PET-CT has higher diagnostic and prognostic value than conventional MRI in patients with suspected anti-Tr/DNER encephalitis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02403-5.

Altered Cerebellar Resting-State Functional Connectivity in Early-Stage Parkinson's Disease Patients With Cognitive Impairment

Background: Cognitive impairment is one of the most prominent non-motor symptoms in Parkinson's disease (PD), due in part to known cerebellar dysfunctions. Furthermore, previous studies have reported altered cerebellar functional connectivity (FC) in PD patients. Yet whether these changes are also due to the cognitive deficits in PD remain unclear. Methods: A total of 122 non-dementia participants, including 64 patients with early PD and 58 age- and gender-matched elderly controls were stratified into four groups based on their cognitive status (normal cognition vs. cognitive impairment). Cerebellar volumetry and FC were investigated by analyzing, respectively, structural and resting-state functional MRI data among groups using quality control and quantitative measures. Correlation analysis between MRI metrics and clinical features (motor and cognitive scores) were performed. Results: Compared to healthy control subjects with no cognitive deficits, altered cerebellar FC were observed in early PD participants with both motor and cognitive deficits, but not in PD patients with normal cognition, nor elderly subjects showing signs of a cognitive impairment. Moreover, connectivity between the "motor" cerebellum and SMA was positively correlated with motor scores, while intracerebellar connectivity was positively correlated with cognitive scores in PD patients with cognitive impairment. No cerebellar volumetric difference was observed between groups. Conclusions: These findings show that altered cerebellar FC during resting state in early PD patients may be driven not solely by the motor deficits, but by cognitive deficits as well, hence highlighting the interplay between motor and cognitive functioning, and possibly reflecting compensatory mechanisms, in the early PD.

Bereitschaftspotential in Multiple System Atrophy

Objective: Multiple system atrophy (MSA) is a neurodegenerative disorder manifesting as parkinsonism, cerebellar ataxia, and autonomic dysfunction. It is categorized into MSA with predominant parkinsonism (MSA-P) and into MSA with predominant cerebellar ataxia (MSA-C). The pathophysiology of motor control circuitry involvement in MSA subtype is unclear. Bereitschaftspotential (BP) is a feasible clinical tool to measure electroencephalographic activity prior to volitional motions. We recorded BP in patients with MSA-P and MSA-C to investigate their motor cortical preparation and activation for volitional movement.

Methods: We included eight patients with MSA-P, eight patients with MSA-C, and eight age-matched healthy controls. BP was recorded during self-paced rapid wrist extension movements. The electroencephalographic epochs were time-locked to the electromyography onset of the voluntary wrist movements. The three groups were compared with respect to the mean amplitudes of early (1,500–500 ms before movement onset) and late (500–0 ms before movement onset) BP.

Results: Mean early BP amplitude was non-significantly different between the three groups. Mean late BP amplitude in the two patient groups was significantly reduced in the parietal area contralateral to the movement side compared with that in the healthy control group. In addition, the late BP of the MSA-C group but not the MSA-P group was significantly reduced at the central parietal area compared with that of the healthy control group.

Conclusions: Our findings suggest that patients with MSA exhibit motor cortical dysfunction in voluntary movement preparation and activation. The dysfunction can be practicably evaluated using late BP, which represents the cerebello-dentato-thalamo-cortical pathway.

Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism

PURPOSE OF REVIEW

Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA).

RECENT FINDINGS

Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools.

SUMMARY

These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy

OBJECTIVES

This study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson's disease (PD) from atypical parkinsonian syndromes (APS).

METHODS

Radiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest-based recursive feature elimination by cross-validation method.

RESULTS

The PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS.

CONCLUSIONS

This study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease.

KEY POINTS

• Radiomics features were extracted from T1-weighted MRI images. • Parkinson's disease and atypical parkinsonian syndromes were classified at an accuracy of 92%. • This study establishes the utility of radiomics to differentiate Parkinson's disease and atypical parkinsonian syndromes using routine T1-weighted images.

Functional Connectome in Parkinson's Disease and Parkinsonism

PURPOSE OF REVIEW

There has been an exponential growth in functional connectomics research in neurodegenerative disorders. This review summarizes the recent findings and limitations of the field in Parkinson's disease (PD) and atypical parkinsonian syndromes.

RECENT FINDINGS

Increasingly more sophisticated methods ranging from seed-based to network and whole-brain dynamic functional connectivity have been used. Results regarding the disruption in the functional connectome vary considerably based on disease severity and phenotypes, and treatment status in PD. Non-motor symptoms of PD also link to the dysfunction in heterogeneous networks. Studies in atypical parkinsonian syndromes are relatively scarce. An important clinical goal of functional connectomics in neurodegenerative disorders is to establish the presence of pathology, track disease progression, predict outcomes, and monitor treatment response. The obstacles of reliability and reproducibility in the field need to be addressed to improve the potential of the functional connectome as a biomarker for these purposes in PD and atypical parkinsonian syndromes.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy

Background

Structural connectivity is a promising methodology to detect patterns of neural network dysfunction in neurodegenerative diseases. This approach has not been tested in progressive supranuclear palsy (PSP).

Objectives

The aim of this study is reconstructing the structural connectome to characterize and detect the pathways of degeneration in PSP patients compared with healthy controls and their correlation with clinical features. The second objective is to assess the potential of structural connectivity measures to distinguish between PSP patients and healthy controls at the single-subject level.

Methods

Twenty healthy controls and 19 PSP patients underwent diffusion-weighted MRI with a 3T scanner. Structural connectivity, represented by number of streamlines, was derived from probabilistic tractography. Global and local network metrics were calculated based on graph theory.

Results

Reduced numbers of streamlines were predominantly found in connections between frontal areas and deep gray matter (DGM) structures in PSP compared with controls. Significant changes in structural connectivity correlated with clinical features in PSP patients. An abnormal small-world architecture was detected in the subnetwork comprising the frontal lobe and DGM structures in PSP patients. The classification procedure achieved an overall accuracy of 82.23% with 94.74% sensitivity and 70% specificity.

Conclusion

Our findings suggest that modelling the brain as a structural connectome is a useful method to detect changes in the organization and topology of white matter tracts in PSP patients. Secondly, measures of structural connectivity have the potential to correctly discriminate between PSP patients and healthy controls.

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Reduced structural connectivity in PSP patients compared with healthy controls

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Connectivity reductions in fronto-DGM tracts correlate with PSPRS and FAB scores

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PSP patients present abnormal small-world architecture in the fronto-DGM network.