Subcortical Structure Volumes and Correlation to Clinical Variables in Parkinson's Disease

Subcortical structure alteration in patients with drug-induced parkinsonism: Evidence from neuroimaging

Parkinson's Disease (PD) and Drug-induced parkinsonism (DIP) are the most common subtypes of parkinsonism, yet no studies have reported that the subcortical volume alterations in DIP patients. This study aimed to identify specific alterations of subcortical structures volume in DIP patients, and investigate association between the subcortical structure modifications and clinical symptoms. We recruited 27 PD patients, 25 DIP patients and 30 healthy controls (HCs). The clinical symptom-related parameters (Unified Parkinson's Disease Rating Scale, UPDRS) were evaluated. Structural imaging was performed on a 3.0 T scanner, and volumes of subcortical structures were obtained using FreeSurfer software. Analysis of covariance (ANCOVA) and partial correlation analysis were performed. DIP group had significantly smaller volume of the thalamus, pallidum, hippocampus and amygdala compared to HCs. ROC curve analysis demonstrated that the highest area under curve (AUC) value was in the right pallidum (AUC = 0.831) for evaluating the diagnostic efficacy in DIP from HCs. Moreover, the volumes of the putamen, hippocampus and amygdala were negatively correlated with UPDRSII in the DIP patients. The volume of the amygdala was negatively correlated with UPDRSIII. The present study provides novel information regarding neuroanatomical alteration of subcortical nuclei in DIP patients, suggesting that these methods might provide the basis for early diagnosis and differential diagnosis of DIP.

Striatal and thalamic automatic segmentation, morphology, and clinical correlates in Parkinsonism: Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

Parkinson's disease (PD), multisystem atrophy (MSA), and progressive supranuclear palsy (PSP) present similarly with bradykinesia, tremor, rigidity, and cognitive impairments. Neuroimaging studies have found differential changes in the nigrostriatal pathway in these disorders, however whether the volume and shape of specific regions within this pathway can distinguish between atypical Parkinsonian disorders remains to be determined. This paper investigates striatal and thalamic volume and morphology as distinguishing biomarkers, and their relationship to neuropsychiatric symptoms. Automatic segmentation to calculate volume and shape analysis of the caudate nucleus, putamen, and thalamus were performed in 18 PD patients, 12 MSA, 15 PSP, and 20 healthy controls, then correlated with clinical measures. PSP bilateral thalami and right putamen were significantly smaller than controls, but not MSA or PD. The left caudate and putamen significantly correlated with the Neuropsychiatric Inventory total score. Bilateral thalamus, caudate, and left putamen had significantly different morphology between groups, driven by differences between PSP and healthy controls. This study demonstrated that PSP patient striatal and thalamic volume and shape are significantly different when compared with controls. Parkinsonian disorders could not be differentiated on volumetry or morphology, however there are trends for volumetric and morphological changes associated with PD, MSA, and PSP.

Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation

Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-scale database consisting of 7039 brain T1-weighted MRI data samples. We evaluated the method by using experiments focused on three distinct topics, namely, the necessity of preprocessing steps, cross-institutional and longitudinal reproducibility, and volumetric accuracy. The optimized model, MX_RW-where "MX" is a mix of RW and nonuniform intensity normalization data and "RW" is raw data with basic preprocessing-did not require time-consuming preprocessing steps, such as nonuniform intensity normalization or image registration, for brain MRI before segmentation. Cross-institutional testing revealed that MX_RW (Dice similarity coefficient: 0.809, coefficient of variation: 4.6%, and Pearson's correlation coefficient: 0.979) exhibited a performance comparable with that of FreeSurfer (Dice similarity coefficient: 0.798, coefficient of variation: 5.6%, and Pearson's correlation coefficient: 0.973). The computation time per dataset of MX_RW was generally less than 5 s (even when tested without graphics processing units), which was notably faster than FreeSurfer. Thus, for time-restricted applications, MX_RW represents a competitive alternative to FreeSurfer.

Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis

Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.

Statistical shape analysis of putamen in early-onset Parkinson's disease

OBJECTIVE

To investigate the shape differences in the putamen of early-onset Parkinson's patients compared with healthy controls and to assess and to assess sub-regional brain abnormalities.

METHODS

This study was conducted using the 3-T MRI scans of 23 early-onset Parkinson's patients and age and gender matched control subjects. Landmark coordinate data obtained and Procrustes analysis was used to compare mean shapes. The relationships between the centroid sizes of the left and right putamen, and the durations of disease examined using growth curve models.

RESULTS

While there was a significant difference between the right putamen shape of control and patient groups, there was not found a significant difference in terms of left putamen. Sub-regional analyses showed that for the right putamen, the most prominent deformations were localized in the middle-posterior putamen and minimal deformations were seen in the anterior putamen.

CONCLUSION

Although they were not as pronounced as those in the right putamen, the deformations in the left putamen mimic the deformations in the right putamen which are found mainly in the middle-posterior putamen and at a lesser extend in the anterior putamen.

A Robust and Accurate Deep-learning-based Method for the Segmentation of Subcortical Brain: Cross-dataset Evaluation of Generalization Performance

Purpose:

To analyze subcortical brain volume more reliably, we propose a deep learning segmentation method of subcortical brain based on magnetic resonance imaging (MRI) having high generalization performance, accuracy, and robustness.

Methods:

First, local images of three-dimensional (3D) bounding boxes were extracted for seven subcortical structures (thalamus, putamen, caudate, pallidum, hippocampus, amygdala, and accumbens) from a whole brain MR image as inputs to the neural network. Second, dilated convolution layers, which input information of variable scope, were introduced to the blocks that make up the neural network. These blocks were connected in parallel to simultaneously process global and local information obtained by the dilated convolution layers. To evaluate generalization performance, different datasets were used for training and testing sessions (cross-dataset evaluation) because subcortical brain segmentation in clinical analysis is assumed to be applied to unknown datasets.

Results:

The proposed method showed better generalization performance that can obtain stable accuracy for all structures, whereas the state-of-the-art deep learning method obtained extremely low accuracy for some structures. The proposed method performed segmentation for all samples without failing with significantly higher accuracy (P < 0.005) than conventional methods such as 3D U-Net, FreeSurfer, and Functional Magnetic Resonance Imaging of the Brain’s (FMRIB’s) Integrated Registration and Segmentation Tool in the FMRIB Software Library (FSL-FIRST). Moreover, when applying this proposed method to larger datasets, segmentation was robustly performed for all samples without producing segmentation results on the areas that were apparently different from anatomically relevant areas. On the other hand, FSL-FIRST produced segmentation results on the area that were apparently and largely different from the anatomically relevant area for about one-third to one-fourth of the datasets.

Conclusion:

The cross-dataset evaluation showed that the proposed method is superior to existing methods in terms of generalization performance, accuracy, and robustness.

Cognition, function and awareness of disease impact in early Parkinson's and Huntington's disease

Purpose: Patients with Parkinson's and Huntington's Disease (PD and HD) present impairments in cognitively challenging everyday activities. This study contrasts these two basal ganglia disorders on the ability to perform daily life- like tasks and their level of awareness regarding the disease impact on function.Methods: 19 controls, 10 early-onset PD, 20 early stage PD, and 15 early manifest HD patients were compared in the "EcoKitchen," a virtual reality task with increasing executive load, the "Behavioural Assessment of Dysexecutive Syndrome battery - BADS," and "The Adults and Older Adults Functional Assessment Inventory - IAFAI," a self-report functional questionnaire. The EcoKitchen clinical correlates were investigated.Results: All clinical groups presented slower EcoKitchen performance than controls, however, only HD patients showed decreased accuracy. HD and PD patients exhibited reduced BADS scores compared to the other study participants. Importantly, on the IAFAI, PD patients signalled more physically related incapacities and HD patients indicated more cognitively related incapacities. Accordingly, the EcoKitchen performance was significantly associated with PD motor symptom severity.Conclusions: Our findings suggest differential disease impact on cognition and function across PD and HD patients, with preserved awareness regarding disease- related functional sequelae. These observations have important implications for clinical management, research and rehabilitation.Implications for rehabilitationPatients with early stage Parkinson's and Huntington's disease have diagnosis-specific impairments in the performance of executively demanding everyday activities and, yet, show preserved awareness about the disease impact on their daily life.An active involvement of patients in the rehabilitation process should be encouraged, as their appraisal of the disease effects can help on practical decisions about meaningful targets for intervention, vocational choices, quality-of-life issues and/or specific everyday skills to boost.The EcoKitchen, a non-immersive virtual reality task, can detect and quantify early deficits in everyday-like tasks and is therefore a valuable tool for assessing the effects of rehabilitation strategies on the functional cognition of these patients.Rehabilitation efforts in the mild stages of Parkinson's and Huntington's disease should be aware of greater time needs from the patients in the performance of daily life tasks, target executive skills, and give a more prominent role to patients in symptoms report and management.

Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning

Background:

This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images.

Discussion:

Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners’ algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully.

Conclusion:

Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.

Multi-modal brain fingerprinting: A manifold approximation based framework

This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting-state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis.

Moving to music: effects of heard and imagined musical cues on movement-related brain activity

Music is commonly used to facilitate or support movement, and increasingly used in movement rehabilitation. Additionally, there is some evidence to suggest that music imagery, which is reported to lead to brain signatures similar to music perception, may also assist movement. However, it is not yet known whether either imagined or musical cueing changes the way in which the motor system of the human brain is activated during simple movements. Here, functional magnetic resonance imaging was used to compare neural activity during wrist flexions performed to either heard or imagined music with self-pacing of the same movement without any cueing. Focusing specifically on the motor network of the brain, analyses were performed within a mask of BA4, BA6, the basal ganglia (putamen, caudate, and pallidum), the motor nuclei of the thalamus, and the whole cerebellum. Results revealed that moving to music compared with self-paced movement resulted in significantly increased activation in left cerebellum VI. Moving to imagined music led to significantly more activation in pre-supplementary motor area (pre-SMA) and right globus pallidus, relative to self-paced movement. When the music and imagery cueing conditions were contrasted directly, movements in the music condition showed significantly more activity in left hemisphere cerebellum VII and right hemisphere and vermis of cerebellum IX, while the imagery condition revealed more significant activity in pre-SMA. These results suggest that cueing movement with actual or imagined music impacts upon engagement of motor network regions during the movement, and suggest that heard and imagined cues can modulate movement in subtly different ways. These results may have implications for the applicability of auditory cueing in movement rehabilitation for different patient populations.