MRI characteristics of the substantia nigra in Parkinson's disease: A combined quantitative T1 and DTI study

Fast and reliable quantitative measures of white matter development with magnetic resonance fingerprinting

Developmental cognitive neuroscience aims to shed light on evolving relationships between brain structure and cognitive development. To this end, quantitative methods that reliably measure individual differences in brain tissue properties are fundamental. Standard qualitative MRI sequences are influenced by scan parameters and hardware-related biases, and also lack physical units, making the analysis of individual differences problematic. In contrast, quantitative MRI can measure physical properties of the tissue but with the cost of long scan durations and sensitivity to motion. This poses a critical limitation for studying young children. Here, we examine the reliability and validity of an efficient quantitative multiparameter mapping method - Magnetic Resonance Fingerprinting (MRF) - in children scanned longitudinally. We focus on T1 values in white matter, since quantitative T1 values are known to primarily reflect myelin content, a key factor in brain development. Forty-nine children aged 8-13y (mean 10.3y ±1.4) completed two scanning sessions 2-4 months apart. In each session, two 2-minute 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan duration on image quality and scan-rescan reliability. A separate calibration scan was used to measure B0 inhomogeneity and correct for bias. We examined the impact of scan time and B0 inhomogeneity correction on scan-rescan reliability of values in white matter, by comparing single 2-min and combined two 2-min scans, with and without B0-correction. Whole-brain voxel-based reliability analysis showed that combining two 2-min MRF scans improved reliability (pearson's r=0.87) compared with a single 2-min scan (r=0.84), while B0-correction had no effect on reliability in white matter (r=0.86 and 0.83 4-min vs 2-min). Using diffusion tractography, we delineated MRF-derived T1 profiles along major white matter fiber tracts and found similar or higher reliability for T1 from MRF compared to diffusion parameters (based on a 10-minute dMRI scan). Lastly, we found that T1 values in multiple white matter tracts were significantly correlated with age. In sum, MRF-derived T1 values were highly reliable in a longitudinal sample of children and replicated known age effects. Reliability in white matter was improved by longer scan duration but was not affected by B0-correction, making it a quick and straightforward scan to collect. We propose that MRF provides a promising avenue for acquiring quantitative brain metrics in children and patient populations where scan time and motion are of particular concern.

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases

Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.

Anderson-Fabry Disease: A New Piece of the Lysosomal Puzzle in Parkinson Disease?

Anderson-Fabry disease (AFD) is an inherited lysosomal storage disorder characterized by a composite and multisystemic clinical phenotype and frequent involvement of the central nervous system (CNS). Research in this area has largely focused on the cerebrovascular manifestations of the disease, and very little has been described about further neurological manifestations, which are known in other lysosomal diseases, such as Gaucher disease. In particular, a clinical and neuroimaging phenotype suggesting neurodegeneration as a putative mechanism has never been fully described for AFD, but the increased survival of affected patients with early diagnosis and the possibility of treatment have given rise to some isolated reports in the literature on the association of AFD with a clinical phenotype of Parkinson disease (PD). The data are currently scarce, but it is possible to hypothesize the molecular mechanisms of cell damage that support this association; this topic is worthy of further study in particular in relation to the therapeutic possibilities, which have significantly modified the natural history of the disease but which are not specifically dedicated to the CNS. In this review, the molecular mechanisms underlying this association will be proposed, and the available data with implications for future research and treatment will be rewritten.

Atlas-based relaxometry and subsegment analysis of the substantia nigra pars compacta using quantitative MRI: a healthy volunteer study

OBJECTIVE

Parkinson's disease is a neurodegenerative disorder caused by neuronal cell loss in the substantia nigra pars compacta (SNpc). We aimed to perform atlas-based relaxometry using an anatomical SNpc atlas and obtain baseline values of SNpc regions in healthy volunteers.

METHODS

Neuromelanin (NM)-sensitive imaging of the midbrain and whole-brain 3D T1 weighted images of 27 healthy volunteers (20 males; aged 36.3 ± 11.5 years) were obtained. An anatomical SNpc atlas was created using NM-sensitive images in standard space, and divided into medial (MG), dorsal (DG), and ventrolateral (VG) groups. Proton density (PD), T1, and T2 values in these regions were obtained using quantitative MRI. The relationships between PD, T1, and T2 values in each SNpc region and age were evaluated.

RESULTS

The VG PD value was significantly higher than the MG and DG values. MG, DG, and VG T1 values were significantly different, whereas the T2 value of the MG was significantly lower than the DG and VG values. Moreover, a significant negative correlation between PD and T1 values of the MG and age was observed.

CONCLUSION

The PD, T1, and T2 values of the SNpc regions measured in standard space using an anatomical atlas can be used as baseline values. PD and T1 values of the SNpc regions may be associated with NM concentrations.

ADVANCES IN KNOWLEDGE

An anatomical SNpc atlas was created using NM-sensitive MRI and can be used for the quantitative evaluation of subsegments of the SNpc in standard space.

Four-modality imaging of unmedicated subjects with schizophrenia: 18F-fluorodeoxyglucose and 18F-fallypride PET, diffusion tensor imaging, and MRI

Diminished prefrontal function, dopaminergic abnormalities in the striatum and thalamus, reductions in white matter integrity and frontotemporal gray matter deficits are the most replicated findings in schizophrenia. We used four imaging modalities (¹⁸F-fluorodeoxyglucose and ¹⁸F-fallypride PET, diffusion tensor imaging, structural MRI) in 19 healthy and 25 schizophrenia subjects to assess the relationship between functional (dopamine D₂/D₃ receptor binding potential, glucose metabolic rate) and structural (fractional anisotropy, MRI) correlates of schizophrenia and their additive diagnostic prediction potential. Multivariate ANOVA was used to compare structural and functional image sets for identification of schizophrenia. Integration of data from all four modalities yielded better predictive power than less inclusive combinations, specifically in the thalamus, left dorsolateral prefrontal and temporal regions. Among the modalities, fractional anisotropy showed highest discrimination in white matter whereas ¹⁸F-fallypride binding showed highest discrimination in gray matter. Structural and functional modalities displayed comparable discriminative power but different topography, with higher sensitivity of structural modalities in the left prefrontal region. Combination of functional and structural imaging modalities with inclusion of both gray and white matter appears most effective in diagnostic discrimination. The highest sensitivity of ¹⁸F-fallypride PET to gray matter changes in schizophrenia supports the primacy of dopaminergic abnormalities in its pathophysiology.

Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson's disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson's disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review

For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.

OUP accepted manuscript

Genetic variants in the human leukocyte antigen and killer cell immunoglobulin-like receptor regions have been associated with many brain-related diseases, but how they shape brain structure and function remains unclear. To identify the genetic variants in HLA and KIR genes associated with human brain phenotypes, we performed a genetic association study of ∼30 000 European unrelated individuals using brain MRI phenotypes generated by the UK Biobank (UKB). We identified 15 HLA alleles in HLA class I and class II genes significantly associated with at least one brain MRI-based phenotypes (P < 5 × 10⁻⁸). These associations converged on several main haplotypes within the HLA. In particular, the human leukocyte antigen alleles within an ancestral haplotype 8.1 were associated with multiple MRI measures, including grey matter volume, cortical thickness (TH) and diffusion MRI (dMRI) metrics. These alleles have been strongly associated with schizophrenia. Additionally, associations were identified between HLA-DRB1*04∼DQA1*03:01∼DQB1*03:02 and isotropic volume fraction of diffusion MRI in multiple white matter tracts. This haplotype has been reported to be associated with Parkinson’s disease. These findings suggest shared genetic associations between brain MRI biomarkers and brain-related diseases. Additionally, we identified 169 associations between the complement component 4 (C4) gene and imaging phenotypes. We found that C4 gene copy number was associated with cortical TH and dMRI metrics. No KIR gene copy numbers were associated with image-derived phenotypes at genome-wide threshold. To address the multiple testing burden in the phenome-wide association study, we performed a multi-trait association analysis using trait-based association test that uses extended Simes procedure and identified MRI image-specific associations. This study contributes to insight into how critical immune genes affect brain-related traits as well as the development of neurological and neuropsychiatric disorders.

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen'd > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

Nigral diffusivity, but not free water, correlates with iron content in Parkinson's disease

The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson's disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson's disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson's disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson's cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson's disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson's Progression Marker's Initiative, consisting of 110 participants (58 control participants and 52 Parkinson's disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P < 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson's disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = -0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = -0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson's disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.

Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants

The prevalence of disabled survivors of prematurity has increased dramatically in the past 3 decades. These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age, are at high risk for neurodevelopmental impairments. Early and clinically effective personalized prediction of outcomes, which forms the basis for early treatment decisions, is urgently needed during the peak neuroplasticity window—the first couple of years after birth—for at-risk infants, when intervention is likely to be most effective. Advances in MRI enable the noninvasive visualization of infants' brains through acquired multimodal images, which are more informative than unimodal MRI data by providing complementary/supplementary depicting of brain tissue characteristics and pathology. Thus, analyzing quantitative multimodal MRI features affords unique opportunities to study early postnatal brain development and neurodevelopmental outcome prediction in VPIs. In this study, we investigated the predictive power of multimodal MRI data, including T2-weighted anatomical MRI, diffusion tensor imaging, resting-state functional MRI, and clinical data for the prediction of neurodevelopmental deficits. We hypothesize that integrating multimodal MRI and clinical data improves the prediction over using each individual data modality. Employing the aforementioned multimodal data, we proposed novel end-to-end deep multimodal models to predict neurodevelopmental (i.e., cognitive, language, and motor) deficits independently at 2 years corrected age. We found that the proposed models can predict cognitive, language, and motor deficits at 2 years corrected age with an accuracy of 88.4, 87.2, and 86.7%, respectively, significantly better than using individual data modalities. This current study can be considered as proof-of-concept. A larger study with external validation is important to validate our approach to further assess its clinical utility and overall generalizability.

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

BACKGROUND

Development of reliable and accurate imaging biomarkers of dopaminergic cell neurodegeneration is necessary to facilitate therapeutic drug trials in Parkinson's disease (PD). Neuromelanin-sensitive MRI techniques have been effective in detecting neurodegeneration in the substantia nigra pars compacta (SNpc). The objective of the current study was to investigate longitudinal neuromelanin signal changes in the SNpc in PD patients.

METHODS

In this prospective, longitudinal, observational case-control study, we included 140 PD patients and 64 healthy volunteers divided into 2 cohorts. Cohort I included 99 early PD patients (disease duration, 1.5 ± 1.0 years) and 41 healthy volunteers analyzed at baseline (V1), where 79 PD patients and 32 healthy volunteers were rescanned after 2.0 ± 0.2 years of follow-up (V2). Cohort II included 41 progressing PD patients (disease duration, 9.3 ± 3.7 years) and 23 healthy volunteers at V1, where 30 PD patients were rescanned after 2.4 ± 0.5 years of follow-up. Subjects were scanned at 3 T MRI using 3-dimensional T1-weighted and neuromelanin-sensitive imaging. Regions of interest were delineated manually to calculate SN volumes, volumes corrected by total intracranial volume, signal-to-noise ratio, and contrast-to-noise ratio.

RESULTS

Results showed (1) significant reduction in volume and volume corrected by total intracranial volume between visits, greater in progressing PD than nonsignificant changes in healthy volunteers; (2) no significant effects of visit for signal intensity (signal-to-noise ratio); (3) significant interaction in volume between group and visit; (4) greater volume corrected by total intracranial volume at baseline in female patients and greater decrease in volume and increase in the contrast-to-noise ratio in progressing female PD patients compared with male patients; and (5) correlations between neuromelanin SN changes and disease severity and duration.

CONCLUSIONS

We observed a progressive and measurable decrease in neuromelanin-based SN signal and volume in PD, which might allow a direct noninvasive assessment of progression of SN loss and could represent a target biomarker for disease-modifying treatments. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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

Pinpointing the brain dysconnectivity in idiopathic rapid eye movement sleep behaviour disorder (iRBD) can facilitate preventing the conversion of Parkinson's disease (PD) from prodromal phase. Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively. However, the intrinsic process of the human brain structural network evolving from iRBD to PD still remains largely unknown. To address this issue, 151 participants including iRBD, PD and age-matched normal controls were recruited to receive diffusion MRI scans and neuropsychological examinations. The connectome-wide association analysis was performed to detect reorganization of brain structural network along with PD progression. Eight brain seed regions in both cortical and subcortical areas demonstrated significant structural pattern changes along with the progression of PD. Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic. Our study shows that connectome-wide association analysis reveals the underlying structural connectivity patterns related to the progression of PD, and provide a promising distinct capability to predict prodromal PD patients.

Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain

Objectives

Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors.

Methods

We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve.

Results

We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10^-3 mm²/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10^-3 mm²/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors.

Conclusions

The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.

Clinical Features and Brain MRI Findings in Korean Patients with AGel Amyloidosis

PURPOSE

AGel amyloidosis is systemic amyloidosis caused by pathogenic variants in the GSN gene. In this study, we sought to characterize the clinical and brain magnetic resonance image (MRI) features of Korean patients with AGel amyloidosis.

MATERIALS AND METHODS

We examined 13 patients with AGel amyloidosis from three unrelated families. Brain MRIs were performed in eight patients and eight age- and sex-matched healthy controls. Therein, we analyzed gray and white matter content using voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and FreeSurfer.

RESULTS

The median age at examination was 73 (interquartile range: 64-76) years. The median age at onset of cutis laxa was 20 (interquartile range: 15-30) years. All patients over that age of 60 years had dysarthria, cutis laxa, dysphagia, and facial palsy. Two patients in their 30s had only mild cutis laxa. The median age at dysarthria onset was 66 (interquartile range: 63.5-70) years. Ophthalmoparesis was observed in three patients. No patient presented with muscle weakness of the limbs. Axial fluid-attenuated inversion recovery images of the brain showed no significant differences between the patient and control groups. Also, analysis of VBM, TBSS, and FreeSurfer revealed no significant differences in cortical thickness between patients and healthy controls at the corrected significance level.

CONCLUSION

Our study outlines the clinical manifestations of prominent bulbar palsy and early-onset cutis laxa in 13 Korean patients with AGel amyloidosis and confirms that AGel amyloidosis mainly affects the peripheral nervous system rather than the central nervous system.

Dopamine receptor density and white mater integrity: 18F-fallypride positron emission tomography and diffusion tensor imaging study in healthy and schizophrenia subjects

Dopaminergic dysfunction and changes in white matter integrity are among the most replicated findings in schizophrenia. A modulating role of dopamine in myelin formation has been proposed in animal models and healthy human brain, but has not yet been systematically explored in schizophrenia. We used diffusion tensor imaging and ¹⁸F-fallypride positron emission tomography in 19 healthy and 25 schizophrenia subjects to assess the relationship between gray matter dopamine D₂/D₃ receptor density and white matter fractional anisotropy in each diagnostic group. AFNI regions of interest were acquired for 42 cortical Brodmann areas and subcortical gray matter structures as well as stereotaxically placed in representative white matter areas implicated in schizophrenia neuroimaging literature. Welch's t-test with permutation-based p value adjustment was used to compare means of z-transformed correlations between fractional anisotropy and ¹⁸F-fallypride binding potentials in hypothesis-driven regions of interest in the diagnostic groups. Healthy subjects displayed an extensive pattern of predominantly negative correlations between ¹⁸F-fallypride binding across a range of cortical and subcortical gray matter regions and fractional anisotropy in rostral white matter regions (internal capsule, frontal lobe, anterior corpus callosum). These patterns were disrupted in subjects with schizophrenia, who displayed significantly weaker overall correlations as well as comparatively scant numbers of significant correlations with the internal capsule and frontal (but not temporal) white matter, especially for dopamine receptor density in thalamic nuclei. Dopamine D₂/D₃ receptor density and white matter integrity appear to be interrelated, and their decreases in schizophrenia may stem from hyperdopaminergia with dysregulation of dopaminergic impact on axonal myelination.

A machine learning-based classification approach on Parkinson's disease diffusion tensor imaging datasets

Introduction

The presence of motor signs and symptoms in Parkinson’s disease (PD) is the result of a long-lasting prodromal phase with an advancing neurodegenerative process. The identification of PD patients in an early phase is, however, crucial for developing disease-modifying drugs. The objective of our study is to investigate whether Diffusion Tensor Imaging (DTI) of the Substantia nigra (SN) analyzed by machine learning algorithms (ML) can be used to identify PD patients.

Methods

Our study proposes the use of computer-aided algorithms and a highly reproducible approach (in contrast to manually SN segmentation) to increase the reliability and accuracy of DTI metrics used for classification.

Results

The results of our study do not confirm the feasibility of the DTI approach, neither on a whole-brain level, ROI-labelled analyses, nor when focusing on the SN only.

Conclusions

Our study did not provide any evidence to support the hypothesis that DTI-based analysis, in particular of the SN, could be used to identify PD patients correctly.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

The impact of modern-day neuroimaging on the field of deep brain stimulation

PURPOSE OF REVIEW

Deep brain stimulation (DBS) is an established but growing treatment option for multiple brain disorders. Over the last decade, electrode placement and their effects were increasingly analyzed with modern-day neuroimaging methods like spatial normalization, fibertracking, or resting-state functional MRI. Similarly, specialized basal ganglia MRI sequences were introduced and imaging at high field strengths has become increasingly popular.

RECENT FINDINGS

To facilitate the process of precise electrode localizations, specialized software pipelines were introduced. By those means, DBS targets could recently be refined and significant relationships between electrode placement and clinical improvement could be shown. Furthermore, by combining electrode reconstructions with network imaging methods, relationships between electrode connectivity and clinical improvement were investigated. This led to a broad series of imaging-based insights about DBS that are reviewed in the present work.

SUMMARY

The reviewed literature makes a strong case that brain imaging plays an increasingly important role in DBS targeting and programming. Furthermore, brain imaging will likely help to better understand the mechanism of action of DBS.

[Method for Quantitative Evaluation of the Substantia Nigra Using Phase-sensitive Inversion Recovery in 1.5 T Magnetic Resonance Imaging]

PURPOSE

Parkinson's disease is a neurodegenerative disease with movement disorders caused by degeneration of the substantia nigra (SN) in the midbrain. Magnetic resonance imaging (MRI) is used to exclude similar diseases. Recently, neuro-melanin imaging (NMI) has been proposed as an imaging method for evaluating the SN. However, the evaluation of the image using this is qualitative, and normal SN cannot be visualized at 1.5 T. This study aimed to investigate whether the SN can be quantitatively evaluated by using phase-sensitive inversion recovery (PSIR) at 1.5 T.

METHOD

The phantom was imaged by PSIR and the accuracy of T₁ value was verified. Next, the healthy volunteers were imaged by PSIR, and the T₁ value and area of the SN were measured.

RESULT

As a result of the phantom study, the difference of the T₁value between PSIR and inversion recovery spin-echo in the range of the T₁ value the SN and the cerebral peduncle (500 to 1000 ms) was lesser than ±10%. The difference between the average of T₁ values of the SN measured by PSIR and the previously reported T₁ values of the SN was about 1.1%. Furthermore, the average of the area of the SN measured by PSIR was measured independently of imaging parameters by using the T₁ value as a reference.

CONCLUSION

These results indicate the possibility of a quantitative evaluation of the SN by measuring its area and T₁ value by using PSIR at 1.5 T.

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator

Deep brain stimulation (DBS) for Parkinson's disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS.

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease

Introduction: The loss of nigrosome-1, which is also referred to as the swallow tail sign (STS) in T2^*-weighted iron-sensitive magnetic resonance imaging (MRI), has recently emerged as a new biomarker for idiopathic Parkinson's disease (IPD). However, consistent recognition of the STS is difficult due to individual variations and different imaging parameters. Radiomics might have the potential to overcome these shortcomings. Therefore, we chose to explore whether radiomic features of nigrosome-1 of substantia nigra (SN) based on quantitative susceptibility mapping (QSM) could help to differentiate IPD patients from healthy controls (HCs). Methods: Three-dimensional multi-echo gradient-recalled echo images (0.86 × 0.86 × 1.00 mm³) were obtained at 3.0-T MRI for QSM in 87 IPD patients and 77 HCs. Regions of interest (ROIs) of the SN below the red nucleus were manually drawn on both sides, and subsequently, volumes of interest (VOIs) were segmented (these ROIs included four 1-mm slices). Then, 105 radiomic features (including 18 first-order features, 13 shape features, and 74 texture features) of bilateral VOIs in the two groups were extracted. Forty features were selected according to the ensemble feature selection method, which combined analysis of variance, random forest, and recursive feature elimination. The selected features were further utilized to distinguish IPD patients from HC using the SVM classifier with 10 rounds of 3-fold cross-validation. Finally, the representative features were analyzed using an unpaired t-test with Bonferroni correction and correlated with the UPDRS-III scores. Results: The classification results from SVM were as follows: area under curve (AUC): 0.96 ± 0.02; accuracy: 0.88 ± 0.03; sensitivity: 0.89 ± 0.06; and specificity: 0.87 ± 0.07. Five representative features were selected to show their detailed difference between IPD patients and HCs: 10th percentile and median in IPD patients were higher than those in HCs (all p < 0.00125), while Gray Level Run Length Matrix (GLRLM)-Long Run Low Gray Level Emphasis, Gray Level Size Zone Matrix (GLSZM)-Gray Level Non-Uniformity, and volume (all p < 0.00125) in IPD patients were lower than those in HCs. The 10th percentile was positively correlated with UPDRS-III score (r = 0.35, p = 0.001). Conclusion: Radiomic features of the nigrosome-1 region of SN based on QSM could be useful in the diagnosis of IPD and could serve as a surrogate marker for the STS.

Alteration of Diffusion-Tensor Magnetic Resonance Imaging Measures in Brain Regions Involved in Early Stages of Parkinson's Disease

Many nonmotor symptoms (e.g., hyposmia) appear years before the cardinal motor features of Parkinson's disease (PD). It is thus desirable to be able to use noninvasive brain imaging methods, such as magnetic resonance imaging (MRI), to detect brain abnormalities in early PD stages. Among the MRI modalities, diffusion-tensor imaging (DTI) is suitable for detecting changes in brain tissue structure due to neurological diseases. The main purpose of this study was to investigate whether DTI signals measured from brain regions involved in early stages of PD differ from those of healthy controls. To answer this question, we analyzed whole-brain DTI data of 30 early-stage PD patients and 30 controls using improved region of interest-based analysis methods. Results showed that (i) the fractional anisotropy (FA) values in the olfactory tract (connected with the olfactory bulb: one of the first structures affected by PD) are lower in PD patients than healthy controls; (ii) FA values are higher in PD patients than healthy controls in the following brain regions: corticospinal tract, cingulum (near hippocampus), and superior longitudinal fasciculus (temporal part). Experimental results suggest that the tissue property, measured by FA, in olfactory regions is structurally modulated by PD with a mechanism that is different from other brain regions.

Magnetic resonance T1w/T2w ratio: A parsimonious marker for Parkinson disease

OBJECTIVE

Newer magnetic resonance imaging (MRI) techniques have shown promise in capturing early Parkinson disease (PD)-related changes in the substantia nigra pars compacta (SNc), the key pathological loci. Their translational value, however, is hindered by technical complexity and inconsistent results.

METHODS

A novel yet simple MRI contrast, the T1w/T2w ratio, was used to study 76 PD patients and 70 controls. The T1w/T2w ratio maps were analyzed using both voxel-based and region-of-interest approaches in normalized space. The sensitivity and specificity of the SNc T1w/T2w ratio in discriminating between PD and controls also were assessed. In addition, its diagnostic performance was tested in a subgroup of PD patients with disease duration ≤2 years (PDE). A second independent cohort of 73 PD patients and 49 controls was used for validation.

RESULTS

Compared to controls, PD patients showed a higher T1w/T2w ratio in both the right (cluster size = 164mm3 , p < 0.0001) and left (cluster size = 213mm3 , p < 0.0001) midbrain that was located ventrolateral to the red nucleus and corresponded to the SNc. The region-of-interest approach confirmed the group difference in the SNc T1w/T2w ratio between PD and controls (p < 0.0001). The SNc T1w/T2w ratio had high sensitivity (0.908) and specificity (0.80) to separate PD and controls (area under the curve [AUC] = 0.926), even for PDE patients (AUC = 0.901, sensitivity = 0.857, specificity = 0.857). These results were validated in the second cohort.

INTERPRETATION

The T1w/T2w ratio can detect PD-related changes in the SNc and may be used as a novel, parsimonious in vivo biomarker for the disease, particularly for early stage patients, with high translational value for clinical practice and research. ANN NEUROL 2019;85:96-104.

Diffusion tensor imaging and ventricle volume quantification in patients with chronic shunt-treated hydrocephalus: a matched case-control study

OBJECTIVEThe object of this study was to use diffusion tensor imaging (DTI) and tract-based spatial statistics (TBSS) to characterize the long-term effects of hydrocephalus and shunting on white matter integrity and to investigate the relationship of ventricular size and alterations in white matter integrity with headache and quality-of-life outcome measures.METHODSPatients with shunt-treated hydrocephalus and age- and sex-matched healthy controls were recruited into the study and underwent anatomical and DTI imaging on a 3-T MRI scanner. All patients were clinically stable, had undergone CSF shunt placement before 2 years of age, and had a documented history of complaints of headaches. Outcome was scored based on the Headache Disability Inventory and the Hydrocephalus Outcome Questionnaire. Fractional anisotropy (FA) and other DTI-based measures (axial, radial, and mean diffusivity; AD, RD, and MD, respectively) were extracted in the corpus callosum and internal capsule with manual region-of-interest delineation and in other regions with TBSS. Paired t-tests, corrected with a 5% false discovery rate, were used to identify regions with significant differences between patients and controls. Within the patient group, linear regression models were used to investigate the relationship between FA or ventricular volume and outcome, as well as the effect of shunt-related covariates.RESULTSTwenty-one hydrocephalus patients and 21 matched controls completed the study, and their data were used in the final analysis. The authors found significantly lower FA for patients than for controls in 20 of the 48 regions, mostly posterior white matter structures, in periventricular as well as more distal tracts. Of these 20 regions, 17 demonstrated increased RD, while only 5 showed increased MD and 3 showed decreased AD. No areas of increased FA were observed. Higher FA in specific periventricular white matter tracts, tending toward FA in controls, was associated with increased ventricular size, as well as improved clinical outcome.CONCLUSIONSThe study shows that TBSS-based DTI is a sensitive technique for elucidating changes in white matter structures due to hydrocephalus and chronic CSF shunting and provides preliminary evidence that DTI may be a valuable tool for tailoring shunt procedures to monitor ventricular size following shunting and achieve optimal outcome, as well as for guiding the development of alternate therapies for hydrocephalus.

Structural MRI in Idiopathic Parkinson's Disease

Among modern neuroimaging modalities, magnetic resonance imaging (MRI) is a widely available, non-invasive, and cost-effective method to detect structural and functional abnormalities related to neurodegenerative disorders. In the last decades, MRI have been widely implemented to support PD diagnosis as well as to provide further insights into motor and non-motor symptoms pathophysiology, complications and treatment-related effects. Different aspects of the brain morphology and function may be derived from a single scan, by applying different analytic approaches. Biomarkers of neurodegeneration as well as tissue microstructural changes may be extracted from structural MRI techniques. In this chapter, we analyze the role of structural imaging to differentiate PD patients from controls and to define neural substrates of motor and non-motor PD symptoms. Evidence collected in the premotor PD phase will be also critically discussed. White matter as well as gray matter integrity imaging studies has been reviewed, aiming to highlight points of strength and limits to their potential application in clinical settings.

Comparative Study of MRI Biomarkers in the Substantia Nigra to Discriminate Idiopathic Parkinson Disease

BACKGROUND AND PURPOSE

Several new MR imaging techniques have shown promising results in patients with Parkinson disease; however, the comparative diagnostic values of these measures at the individual level remain unclear. Our aim was to compare the diagnostic value of MR imaging biomarkers of substantia nigra damage for distinguishing patients with Parkinson disease from healthy volunteers.

MATERIALS AND METHODS

Thirty-six patients and 20 healthy volunteers were prospectively included. The MR imaging protocol at 3T included 3D T2-weighted and T1-weighted neuromelanin-sensitive images, diffusion tensor images, and R2* mapping. T2* high-resolution images were also acquired at 7T to evaluate the dorsal nigral hyperintensity sign. Quantitative analysis was performed using ROIs in the substantia nigra drawn manually around the area of high signal intensity on neuromelanin-sensitive images and T2-weighted images. Visual analysis of the substantia nigra neuromelanin-sensitive signal intensity and the dorsolateral nigral hyperintensity on T2* images was performed.

RESULTS

There was a significant decrease in the neuromelanin-sensitive volume and signal intensity in patients with Parkinson disease. There was also a significant decrease in fractional anisotropy and an increase in mean, axial, and radial diffusivity in the neuromelanin-sensitive substantia nigra at 3T and a decrease in substantia nigra volume on T2* images. The combination of substantia nigra volume, signal intensity, and fractional anisotropy in the neuromelanin-sensitive substantia nigra allowed excellent diagnostic accuracy (0.93). Visual assessment of both substantia nigra dorsolateral hyperintensity and neuromelanin-sensitive images had good diagnostic accuracy (0.91 and 0.86, respectively).

CONCLUSIONS

The combination of neuromelanin signal and volume changes with fractional anisotropy measurements in the substantia nigra showed excellent diagnostic accuracy. Moreover, the high diagnostic accuracy of visual assessment of substantia nigra changes using dorsolateral hyperintensity analysis or neuromelanin-sensitive signal changes indicates that these techniques are promising for clinical practice.

The asymmetry of neural symptoms in Wilson's disease patients detecting by diffusion tensor imaging, resting-state functional MRI, and susceptibility-weighted imaging

OBJECTIVE

To investigate the cause of the motor asymmetry in Wilson's disease (WD) patients using functional MRI.

METHODS

Fifty patients with WD and 20 age-matched healthy controls were enrolled. Neurological symptoms were scored using the modified Young Scale. All study subjects underwent diffusion tensor imaging (DTI), susceptibility-weighted imaging (SWI), and resting-state functional MRI (rs-fMRI) of the brain. Six regions of interest (ROI) were chosen. Fiber volumes between ROIs on DTI, corrected phase (CP) values on SWI, amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (REHO) values on rs-fMRI were determined. Asymmetry index (right or left value/left or right value) was evaluated.

RESULTS

Asymmetry of rigidity, tremor, choreic movement, and gait abnormality (asymmetry index = 1.33, 1.39, 1.36, 1.40), fiber tracts between the GP and substantia nigra (SN), GP and PU, SN and thalamus (TH), SN and cerebellum, head of the caudate nucleus (CA) and SN, PU and CA, CA and TH, TH and cerebellum (asymmetry index = 1.233, 1.260, 1.269, 1.437, 1.503, 1.138, 1.145, 1.279), CP values in the TH, SN (asymmetry index = 1.327, 1.166), ALFF values, and REHO values of the TH (asymmetry index = 1.192, 1.233) were found. Positive correlation between asymmetry index of rigidity and fiber volumes between the GP and SN, SN and TH (r = .221, .133, p = .043, .036), and tremor and fiber volumes between the CA and TH (r = .045, p = .040) was found.

CONCLUSIONS

The neurological symptoms of patients with WD were asymmetry. The asymmetry of fiber projections may be the main cause of motor asymmetry in patients with WD.

Anatomical Templates of the Midbrain Ventral Tegmental Area and Substantia Nigra for Asian Populations

Increasing evidence shows that the midbrain dopaminergic system is involved in various functions. However, details of the role of the midbrain dopaminergic system in these functions are still to be determined in humans. Considering that the ventral tegmental area (VTA) and substantia nigra (SN) in the midbrain are the primary dopamine producers, creating reliable anatomical templates of the VTA and SN through neuroimaging studies would be useful for achieving a detailed understanding of this dopaminergic system. Although VTA and SN anatomical templates have been created, no specific templates exist for the Asian population. Thus, we conducted anatomical and resting-state functional magnetic resonance imaging (rs-fMRI) studies to create VTA and SN templates for the Asian population. First, a neuromelanin-sensitive MRI technique was used to visualize the VTA and SN, and then individual hand-drawn VTA and SN regions of interests (ROIs) were traced on a small sample of neuromelanin-sensitive MRIs (dataset 1). Second, individual hand-drawn VTA and SN ROIs were normalized to create normalized VTA and SN templates for the Asian population. Third, a seed-based functional connectivity analysis was performed on rs-fMRI data using hand-drawn ROIs to calculate neural networks of VTA and SN in dataset 1. Fourth, a seed-based functional connectivity analysis was performed using VTA and SN seeds that were created based on normalized templates from dataset 1. Subsequently, a seed-based functional connectivity analysis was performed using VTA and SN seeds in another, larger sample (dataset 2) to assess whether neural networks of VTA or SN seeds from dataset 1 would be replicated in dataset 2. The Asian VTA template was smaller and located in a more posterior and inferior part of the midbrain compared to the published VTA template, while the Asian SN template, relative to the published SN template, did not differ in size but was located in the more inferior part of the midbrain. The neural networks of the VTA and SN seeds in dataset 1 were replicated in dataset 2. Altogether, our normalized template of the VTA and SN could be used for measuring fMRI activities related to the VTA and SN in the Asian population.

Multi-modal Brain MRI in Subjects with PD and iRBD

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a condition that often evolves into Parkinson's disease (PD). Therefore, by monitoring iRBD it is possible to track the neurodegeneration of individuals who may progress to PD. Here we aimed at piloting the characterization of brain tissue properties in mid-brain subcortical regions of 10 healthy subjects, 8 iRBD, and 9 early-diagnosed PD. We used a battery of magnetic resonance imaging (MRI) contrasts at 3 T, including adiabatic and non-adiabatic rotating frame techniques developed by our group, along with diffusion tensor imaging (DTI) and resting-state fMRI. Adiabatic T_1ρ and T_2ρ, and non-adiabatic RAFF4 (Relaxation Along a Fictitious Field in the rotating frame of rank 4) were found to have lower coefficient of variations and higher sensitivity to detect group differences as compared to DTI parameters such as fractional anisotropy and mean diffusivity. Significantly longer T_1ρ were observed in the amygdala of PD subjects vs. controls, along with a trend of lower functional connectivity as measured by regional homogeneity, thereby supporting the notion that amygdalar dysfunction occurs in PD. Significant abnormalities in reward networks occurred in iRBD subjects, who manifested lower network strength of the accumbens. In agreement with previous studies, significantly longer T_1ρ occurred in the substantia nigra compacta of PD vs. controls, indicative of neuronal degeneration, while regional homogeneity was lower in the substantia nigra reticulata. Finally, other trend-level findings were observed, i.e., lower RAFF4 and T_2ρ in the midbrain of iRBD subjects vs. controls, possibly indicating changes in non-motor features as opposed to motor function in the iRBD group. We conclude that rotating frame relaxation methods along with functional connectivity measures are valuable to characterize iRBD and PD subjects, and with proper validation in larger cohorts may provide pathological signatures of iRBD and PD.

Evaluation of striatonigral connectivity using probabilistic tractography in Parkinson's disease

The cardinal movement abnormalities of Parkinson's disease (PD), including tremor, muscle rigidity, and reduced speed and frequency of movements, are caused by degeneration of dopaminergic neurons in the substantia nigra that project to the putamen, compromising information flow through frontal-subcortical circuits. Typically, the nigrostriatal pathway is more severely affected on the side of the brain opposite (contralateral) to the side of the body that manifests initial symptoms. Several studies have suggested that PD is also associated with changes in white matter microstructural integrity. The goal of the present study was to further develop methods for measuring striatonigral connectivity differences between PD patients and age-matched controls using diffusion weighted magnetic resonance imaging (MRI).

In this cross-sectional study, 40 PD patients and 44 controls underwent diffusion weighted imaging (DWI) using a 40-direction MRI sequence as well as an optimized 60-direction sequence with overlapping slices. Regions of interest (ROIs) encompassing the putamen and substantia nigra were hand drawn in the space of the 40-direction data using high-contrast structural images and then coregistered to the 60-direction data. Probabilistic tractography was performed in the native space of each dataset by seeding the putamen ROI with an ipsilateral substantia nigra classification target. The effect of disease group (PD versus control) on mean putamen-SN connection probability and streamline density were then analyzed using generalized linear models controlling for age, gender, education, as well as seed and target region characteristics.

Mean putamen-SN streamline density was lower in PD on both sides of the brain and in both 40- and 60-direction data. The optimized sequence provided a greater separation between PD and control means; however, individual values overlapped between groups. The 60-direction data also yielded mean connection probability values either trending (ipsilateral) or significantly (contralateral) lower in the PD group. There were minor between-group differences in average diffusion measures within the substantia nigra ROIs that did not affect the results of the GLM analyses when included as covariates. Based on these results, we conclude that mean striatonigral structural connectivity differs between PD and control groups and that use of an optimized 60-direction DWI sequence with overlapping slices increases the sensitivity of the technique to putative disease-related differences. However, overlap in individual values between disease groups limits its use as a classifier.

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The nigrostriatal pathway degenerates in Parkinson's disease.

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Two diffusion tensor imaging (DTI) sequences were acquired in 84 participants.

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Structural connectivity between putamen and substantia nigra was quantified.

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Parkinson's patients had lower connection probability and streamline density.

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A 60-direction DTI sequence with overlapping slices was most sensitive.

Magnetic resonance imaging for the diagnosis of Parkinson's disease

The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerative parkinsonism over the last three decades. This review aims to summarize research findings regarding the value of the different MRI techniques, including advanced sequences at high- and ultra-high-field MRI and modern image analysis algorithms, in the diagnostic work-up of Parkinson's disease. This includes not only the exclusion of alternative diagnoses for Parkinson's disease such as symptomatic parkinsonism and atypical parkinsonism, but also the diagnosis of early, new onset, and even prodromal Parkinson's disease.

Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

BACKGROUND

Neuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing.

METHODS

We compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean diffusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (DES) as the main variable; the heterogeneity index (I2) and Pearson's correlations between the DES and co-variables (demographic, clinical and MRI parameters) were also calculated.

RESULTS

Our results showed that FA-DES and MD-DES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-DES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter.

CONCLUSIONS

Despite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.

Automated segmentation of midbrain structures with high iron content

The substantia nigra (SN), the subthalamic nucleus (STN), and the red nucleus (RN) are midbrain structures of ample interest in many neuroimaging studies, which may benefit from the availability of automated segmentation methods. The high iron content of these structures awards them high contrast in quantitative susceptibility mapping (QSM) images. We present a novel segmentation method that leverages the information of these images to produce automated segmentations of the SN, STN, and RN. The algorithm builds a map of spatial priors for the structures by non-linearly registering a set of manually-traced training labels to the midbrain. The priors are used to inform a Gaussian mixture model of the image intensities, with smoothness constraints imposed to ensure anatomical plausibility. The method was validated on manual segmentations from a sample of 40 healthy younger and older subjects. Average Dice scores were 0.81 (0.05) for the SN, 0.66 (0.14) for the STN and 0.88 (0.04) for the RN in the left hemisphere, and similar values were obtained for the right hemisphere. In all structures, volumes of manual and automatically obtained segmentations were significantly correlated. The algorithm showed lower accuracy on R₂^* and T₂-weighted Fluid Attenuated Inversion Recovery (FLAIR) images, which are also sensitive to iron content. To illustrate an application of the method, we show that the automated segmentations were comparable to the manual ones regarding detection of age-related differences to putative iron content.

Right Hemisphere Grey Matter Volume and Language Functions in Stroke Aphasia

The role of the right hemisphere (RH) in recovery from aphasia is incompletely understood. The present study quantified RH grey matter (GM) volume in individuals with chronic stroke-induced aphasia and cognitively healthy people using voxel-based morphometry. We compared group differences in GM volume in the entire RH and in RH regions-of-interest. Given that lesion site is a critical source of heterogeneity associated with poststroke language ability, we used voxel-based lesion symptom mapping (VLSM) to examine the relation between lesion site and language performance in the aphasic participants. Finally, using results derived from the VLSM as a covariate, we evaluated the relation between GM volume in the RH and language ability across domains, including comprehension and production processes both at the word and sentence levels and across spoken and written modalities. Between-subject comparisons showed that GM volume in the RH SMA was reduced in the aphasic group compared to the healthy controls. We also found that, for the aphasic group, increased RH volume in the MTG and the SMA was associated with better language comprehension and production scores, respectively. These data suggest that the RH may support functions previously performed by LH regions and have important implications for understanding poststroke reorganization.

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704-3722, 2017. © 2017 Wiley Periodicals, Inc.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson's disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Enduring, Sexually Dimorphic Impact of In Utero Exposure to Elevated Levels of Glucocorticoids on Midbrain Dopaminergic Populations

Glucocorticoid hormones (GCs) released from the fetal/maternal glands during late gestation are required for normal development of mammalian organs and tissues. Accordingly, synthetic glucocorticoids have proven to be invaluable in perinatal medicine where they are widely used to accelerate fetal lung maturation when there is risk of pre-term birth and to promote infant survival. However, clinical and pre-clinical studies have demonstrated that inappropriate exposure of the developing brain to elevated levels of GCs, either as a result of clinical over-use or after stress-induced activation of the fetal/maternal adrenal cortex, is linked with significant effects on brain structure, neurological function and behaviour in later life. In order to understand the underlying neural processes, particular interest has focused on the midbrain dopaminergic systems, which are critical regulators of normal adaptive behaviours, cognitive and sensorimotor functions. Specifically, using a rodent model of GC exposure in late gestation (approximating human brain development at late second/early third trimester), we demonstrated enduring effects on the shape and volume of the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) (origins of the mesocorticolimbic and nigrostriatal dopaminergic pathways) on the topographical organisation and size of the dopaminergic neuronal populations and astrocytes within these nuclei and on target innervation density and neurochemical markers of dopaminergic transmission (receptors, transporters, basal and amphetamine-stimulated dopamine release at striatal and prefrontal cortical sites) that impact on the adult brain. The effects of antenatal GC treatment (AGT) were both profound and sexually-dimorphic, not only in terms of quantitative change but also qualitatively, with several parameters affected in the opposite direction in males and females. Although such substantial neurobiological changes might presage marked behavioural effects, in utero GC exposure had only a modest or no effect, depending on sex, on a range of conditioned and unconditioned behaviours known to depend on midbrain dopaminergic transmission. Collectively, these findings suggest that apparent behavioural normality in certain tests, but not others, arises from AGT-induced adaptations or compensatory mechanisms within the midbrain dopaminergic systems, which preserve some, but not all functions. Furthermore, the capacities for molecular adaptations to early environmental challenge are different, even opponent, in males and females, which may account for their differential resilience or failure to perform adequately in behavioural tests. Behavioural "normality" is thus achieved by the midbrain dopaminergic network operating outside its normal limits (in a state of allostasis), rendering it at greater risk to malfunction when challenged in later life. Sex-specific neurobiological programming of midbrain dopaminergic systems may, therefore, have psychopathological relevance for the sex bias commonly found in brain disorders associated with these systems, and which have a neurodevelopmental component, including schizophrenia, ADHD (attention/deficit hyperactivity disorders), autism, depression and substance abuse.

Imaging analysis of Parkinson's disease patients using SPECT and tractography

Parkinson's disease (PD) is a degenerative disorder that affects the central nervous system. PD-related alterations in structural and functional neuroimaging have not been fully explored. This study explored multi-modal PD neuroimaging and its application for predicting clinical scores on the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Multi-modal imaging that combined 123I-Ioflupane single-photon emission computed tomography (SPECT) and diffusion tensor imaging (DTI) were adopted to incorporate complementary brain imaging information. SPECT and DTI images of normal controls (NC; n = 45) and PD patients (n = 45) were obtained from a database. The specific binding ratio (SBR) was calculated from SPECT. Tractography was performed using DTI. Group-wise differences between NC and PD patients were quantified using SBR of SPECT and structural connectivity of DTI for regions of interest (ROIs) related to PD. MDS-UPDRS scores were predicted using multi-modal imaging features in a partial least-squares regression framework. Three regions and four connections within the cortico-basal ganglia thalamocortical circuit were identified using SBR and DTI, respectively. Predicted MDS-UPDRS scores using identified regions and connections and actual MDS-UPDRS scores showed a meaningful correlation (r = 0.6854, p < 0.001). Our study provided insight on regions and connections that are instrumental in PD.

9.4 T MR microscopy of the substantia nigra with pathological validation in controls and disease

BACKGROUND

The anatomy of the substantia nigra on conventional MRI is controversial. Even using histological techniques it is difficult to delineate with certainty from surrounding structures. We sought to define the anatomy of the SN using high field spin-echo MRI of pathological material in which we could study the anatomy in detail to corroborate our MRI findings in controls and Parkinson's disease and progressive supranuclear palsy.

METHODS

23 brains were selected from the Queen Square Brain Bank (10 controls, 8 progressive supranuclear palsy, 5 Parkinson's disease) and imaged using high field 9.4 Tesla spin-echo MRI. Subsequently brains were cut and stained with Luxol fast blue, Perls stain, and immunohistochemistry for substance P and calbindin. Once the anatomy was defined on histology the dimensions and volume of the substantia nigra were determined on high field magnetic resonance images.

RESULTS

The anterior border of the substantia nigra was defined by the crus cerebri. In the medial half it was less distinct due to the deposition of iron and the interdigitation of white matter and the substantia nigra. The posterior border was flanked by white matter bridging the red nucleus and substantia nigra and seen as hypointense on spin-echo magnetic resonance images. Within the substantia nigra high signal structures corresponded to confirmed nigrosomes. These were still evident in Parkinson's disease but not in progressive supranuclear palsy. The volume and dimensions of the substantia nigra were similar in Parkinson's disease and controls, but reduced in progressive supranuclear palsy.

CONCLUSIONS

We present a histologically validated anatomical description of the substantia nigra on high field spin-echo high resolution magnetic resonance images and were able to delineate all five nigrosomes. In accordance with the pathological literature we did not observe changes in the nigrosome structure as manifest by volume or signal characteristics within the substantia nigra in Parkinson's disease whereas in progressive supranuclear palsy there was microarchitectural destruction.

Advanced magnetic resonance imaging of neurodegenerative diseases

Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.

Alterations of diffusion tensor MRI parameters in the brains of patients with Parkinson's disease compared with normal brains: possible diagnostic use

OBJECTIVES

To investigate the diagnostic performance of diffusion tensor imaging in patients with Parkinson's disease (PD).

METHODS

We examined a total of 126 PD patients (68 males/58 females, mean age: 62.0 ±7.6 years) and 91 healthy controls (43 males/48 females, mean age: 59.8 ±7.2 years). Images were acquired on a 3 Tesla magnetic resonance scanner. The Camino software was used to normalize and parcellate diffusion-weighted images into 90 cerebral regions based on the automatic anatomical labelling template. The minimum, median, and maximum values of the mean/radial/axial diffusivity/fractional anisotropy were determined. The diagnostic performance was assessed by receiver operating characteristic analysis. The associations of imaging parameters with disease severity were tested using Pearson's correlation coefficients after adjustment for disease duration.

RESULTS

Compared with healthy controls, PD patients showed increased diffusivity in multiple cortical regions that extended beyond the basal ganglia. An area under curve of 85 % was identified for the maximum values of mean diffusivity in the ipsilateral middle temporal gyrus. The most significant intergroup difference was 26.8 % for the ipsilateral inferior parietal gyrus.

CONCLUSION

The measurement of water diffusion from the parcellated cortex may be clinically useful for the assessment of PD patients.

KEY POINTS

• Increased diffusivity was identified in multiple cortical regions of Parkinson's disease patients. • The area under the receiver operating curve was 85 % in the middle temporal gyrus. • The ipsilateral inferior parietal gyrus showed the most significant change.

Magnetic resonance imaging as a potential biomarker for Parkinson's disease

Although a magnetic resonance imaging (MRI) biomarker for Parkinson's disease (PD) remains an unfulfilled objective, there have been numerous developments in MRI methodology and some of these have shown promise for PD. With funding from the National Institutes of Health and the Michael J Fox Foundation there will be further validation of structural, diffusion-based, and iron-focused MRI methods as possible biomarkers for PD. In this review, these methods and other strategies such as neurochemical and metabolic MRI have been covered. One of the challenges in establishing a biomarker is in the selection of individuals as PD is a heterogeneous disease with varying clinical features, different etiologies, and a range of pathologic changes. Additionally, longitudinal studies are needed of individuals with clinically diagnosed PD and cohorts of individuals who are at great risk for developing PD to validate methods. Ultimately an MRI biomarker will be useful in the diagnosis of PD, predicting the course of PD, providing a means to track its course, and provide an approach to select and monitor treatments.

Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data

Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger impairs several brain regions and yields various motor and non-motor symptoms. Incidence of PD is predicted to double in the next two decades, which urges more research to focus on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model. The proposed JFSS model effectively discards poor samples and irrelevant features. As a result, the selected features play an important role in PD characterization, which will help identify the most relevant and critical imaging biomarkers for PD. Then, a robust classification framework is proposed to simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can also de-noise testing samples based on the cleaned training data. Unlike many previous works that perform de-noising in an unsupervised manner, we perform supervised de-noising for both training and testing data, thus boosting the diagnostic accuracy. Experimental results on both synthetic and publicly available PD datasets show promising results. To evaluate the proposed method, we use the popular Parkinson's progression markers initiative (PPMI) database. Our results indicate that the proposed method can differentiate between PD and normal control (NC), and outperforms the competing methods by a relatively large margin. It is noteworthy to mention that our proposed framework can also be used for diagnosis of other brain disorders. To show this, we have also conducted experiments on the widely-used ADNI database. The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods.