Magnetic resonance imaging markers of Parkinson's disease nigrostriatal signature

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing

PURPOSE

To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy.

METHOD

A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve.

RESULTS

The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy.

CONCLUSION

The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

Do toenail manganese and iron levels reflect brain metal levels or brain metabolism in welders?

Inhalation of welding fumes can cause metal accumulation in the brain, leading to Parkinsonian-like symptoms. Metal accumulation and altered neurochemical profiles have been observed using magnetic resonance imaging (MRI) in highly exposed welders, being associated with decreased motor function and cognition. While MRI is impractical to use as a health risk assessment tool in occupational settings, toenail metal levels are easier to assess and have been demonstrated to reflect an exposure window of 7-12 months in the past. Yet, it is unclear whether toenail metal levels are associated with brain metal levels or changes in metabolism, which are the root of potential health concerns. This study investigates whether toenail manganese (Mn) and iron (Fe) levels, assessed at several time points, correlate with brain Mn and Fe levels, measured by MRI, as well as brain GABA, glutamate (Glu), and glutathione (GSH) levels, measured by Magnetic Resonance Spectroscopy (MRS), in seventeen Mn-exposed welders. Quantitative T1 and R2* MRI maps of the whole brain, along with GABA, Glu, and GSH MRS measurements from the thalamus and cerebellum were acquired at baseline (T0). Toenail clippings were collected at T0 and every three months after the MRI for a year to account for different exposure periods being reflected by toenail clippings and MRI. Spearman correlations of toenail metal levels were run against brain metal and metabolite levels, but no significant associations were found for Mn at any timepoint. Cerebellar GSH positively correlated with toenail Fe clipped twelve months after the MRI (p = 0.05), suggesting an association with Fe exposure at the time of the MRI. Neither thalamic GABA nor Glu correlated with toenail Fe levels. In conclusion, this study cannot support toenail Mn as a proxy for brain Mn levels or metabolic changes, while toenail Fe appears linked to brain metabolic alterations, underscoring the importance of considering other metals, including Fe, in studying Mn neurotoxicity.

Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are fed data with known characteristics. We analyzed the ability of 3D CNNs to discriminate between original and altered whole-brain parametric maps derived from diffusion-weighted magnetic resonance imaging. The alteration consisted in linearly changing the voxel intensity of either one (monoregion) or two (biregion) anatomical regions in each brain volume, but without mimicking any neuropathology. Performing ten-fold cross-validation and using a hold-out set for testing, we assessed the CNNs' discrimination ability according to the intensity of the altered regions, comparing the latter's size and relative position. Monoregion CNNs showed that the larger the modified region, the smaller the intensity increase needed to achieve good performances. Biregion CNNs systematically outperformed monoregion CNNs, but could only detect one of the two target regions when tested on the corresponding monoregion images. Exploiting prior information on training data allowed for a better understanding of CNN behavior, especially when altered regions were combined. This can inform about the complexity of CNN pattern retrieval and elucidate misclassified examples, particularly relevant for pathological data. The proposed analytical approach may serve to gain insights into CNN behavior and guide the design of enhanced detection systems exploiting our prior knowledge.

Iron deposition in subcortical nuclei of Parkinson's disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies

BACKGROUND

Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs).

METHODS

A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2* and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression.

RESULTS

Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata (P <0.001) and compacta (P <0.001), SN (P <0.001), red nucleus (RN, P <0.001), globus pallidus (P <0.001), putamen (PUT, P = 0.009), and thalamus (P = 0.046) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT (P <0.001), RN (P = 0.003), SN (P = 0.017), and caudate nucleus (P = 0.027) than MSA patients, and lower iron content in RN (P = 0.001), PUT (P <0.001), globus pallidus (P = 0.004), SN (P = 0.015), and caudate nucleus (P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.84).

CONCLUSION

Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD.

REGISTRISION

PROSPERO; CRD42022344413.

The longitudinal volumetric and shape changes of subcortical nuclei in Parkinson's disease

Brain structural changes in Parkinson's disease (PD) are progressive throughout the disease course. Changes in surface morphology with disease progression remain unclear. This study aimed to assess the volumetric and shape changes of the subcortical nuclei during disease progression and explore their association with clinical symptoms. Thirty-four patients and 32 healthy controls were enrolled. The global volume and shape of the subcortical nuclei were compared between patients and controls at baseline. The volume and shape changes of the subcortical nuclei were also explored between baseline and 2 years of follow-up. Association analysis was performed between the volume of subcortical structures and clinical symptoms. In patients with PD, there were significantly atrophied areas in the left pallidum and left putamen, while in healthy controls, the right putamen was dilated compared to baseline. The local morphology of the left pallidum was correlated with Mini Mental State Examination scores. The left putamen shape variation was negatively correlated with changes in Unified Parkinson's Disease Rating Scale PART III scores. Local morphological atrophy of the putamen and pallidum is an important pathophysiological change in the development of PD, and is associated with motor symptoms and cognitive status in patients with PD.

A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study

BACKGROUND

MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD).

METHODS

27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs.

RESULTS

The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients.

CONCLUSIONS

MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.

The Role of the Anterior Thalamic Nuclei in the Genesis of Memory Disorders in Alzheimer's Disease: An Exploratory Study

BACKGROUND

Increasing evidence is demonstrating that degeneration of specific thalamic nuclei, in addition to the hippocampus, may occur in Alzheimer's disease (AD) from the prodromal stage (mild cognitive impairment - MCI) and contribute to memory impairment.

OBJECTIVE

Here, we evaluated the presence of macro and micro structural alterations at the level of the anterior thalamic nuclei (ATN) and medio-dorsal thalamic nuclei (MDTN) in AD and amnestic MCI (aMCI) and the possible relationship between such changes and the severity of memory impairment.

METHODS

For this purpose, a sample of 50 patients with aMCI, 50 with AD, and 50 age- and education-matched healthy controls (HC) were submitted to a 3-T MRI protocol with whole-brain T1-weighted and diffusion tensor imaging and a comprehensive neuropsychological assessment.

RESULTS

At macro-structural level, both the ATN and MDTN were found significantly smaller in patients with aMCI and AD when compared to HC subjects. At micro-structural level, instead, diffusion alterations that significantly differentiated aMCI and AD patients from HC subjects were found only in the ATN, but not in the MDTN. Moreover, diffusion values of the ATN were significantly associated with poor episodic memory in the overall patients' group.

CONCLUSIONS

These findings represent the first in vivo evidence of a relevant involvement of ATN in the AD-related neurodegeneration and memory profile and strengthen the importance to look beyond the hippocampus when considering neurological conditions characterized by memory decline.

Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder

The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.

Assessing traffic-related air pollution-induced fiber-specific white matter degradation associated with motor performance declines in aged rats

Fine particulate matter (PM2.5) is thought to exacerbate Parkinson's disease (PD) in the elderly, and early detection of PD progression may prevent further irreversible damage. Therefore, we used diffusion tensor imaging (DTI) for probing microstructural changes after late-life chronic traffic-related PM2.5 exposure. Herein, 1.5-year-old Fischer 344 rats were exposed to clean air (control), high-efficiency particulate air (HEPA)-filtered ambient air (HEPA group), and ambient traffic-related PM2.5 (PM2.5 group, 9.933 ± 1.021 µg/m3) for 3 months. Rotarod test, DTI tractographic analysis, and immunohistochemistry were performed in the end of study period. Aged rats exposed to PM2.5 exhibited motor impairment with decreased fractional anisotropy and tyrosine hydroxylase expression in olfactory and nigrostriatal circuits, indicating disrupted white matter integrity and dopaminergic (DA) neuronal loss. Additionally, increased radial diffusivity and lower expression of myelin basic protein in PM2.5 group suggested ageing progression of demyelination exacerbated by PM2.5 exposure. Significant production of tumor necrosis factor-α was also observed after PM2.5 exposure, revealing potential inflammation of injury to multiple fiber tracts of DA pathways. Microstructural changes demonstrated potential links between PM2.5-induced inflammatory white matter demyelination and behavioral performance, with indication of pre-manifestation of DTI-based biomarkers for early detection of PD progression in the elderly.

Nigral Pathology Contributes to Microstructural Integrity of Striatal and Frontal Tracts in Parkinson's Disease

BACKGROUND

Motor and cognitive impairment in Parkinson's disease (PD) is associated with dopaminergic dysfunction that stems from substantia nigra (SN) degeneration and concomitant α-synuclein accumulation. Diffusion magnetic resonance imaging (MRI) can detect microstructural alterations of the SN and its tracts to (sub)cortical regions, but their pathological sensitivity is still poorly understood.

OBJECTIVE

To unravel the pathological substrate(s) underlying microstructural alterations of SN, and its tracts to the dorsal striatum and dorsolateral prefrontal cortex (DLPFC) in PD.

METHODS

Combining post-mortem in situ MRI and histopathology, T1-weighted and diffusion MRI, and neuropathological samples of nine PD, six PD with dementia (PDD), five dementia with Lewy bodies (DLB), and 10 control donors were collected. From diffusion MRI, mean diffusivity (MD) and fractional anisotropy (FA) were derived from the SN, and tracts between the SN and caudate nucleus, putamen, and DLPFC. Phosphorylated-Ser129-α-synuclein and tyrosine hydroxylase immunohistochemistry was included to quantify nigral Lewy pathology and dopaminergic degeneration, respectively.

RESULTS

Compared to controls, PD and PDD/DLB showed increased MD of the SN and SN-DLPFC tract, as well as increased FA of the SN-caudate nucleus tract. Both PD and PDD/DLB showed nigral Lewy pathology and dopaminergic loss compared to controls. Increased MD of the SN and FA of SN-caudate nucleus tract were associated with SN dopaminergic loss. Whereas increased MD of the SN-DLPFC tract was associated with increased SN Lewy neurite load.

CONCLUSIONS

In PD and PDD/DLB, diffusion MRI captures microstructural alterations of the SN and tracts to the dorsal striatum and DLPFC, which differentially associates with SN dopaminergic degeneration and Lewy neurite pathology. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The applied value in brain gray matter nuclei of patients with early-stage Parkinson's disease : a study based on multiple magnetic resonance imaging techniques

PURPOSE

This study compares the observation efficiency of brain gray matter nuclei of patients with early-stage Parkinson's disease among various Magnetic Resonance Imaging techniques, which include susceptibility weighted imaging (SWI), quantitative susceptibility imaging (QSM), diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Based on the findings, this study suggests an efficient combination of scanning techniques for brain gray matter nuclei observation, aiming to provide an opportunity to advance the understanding of clinical diagnosis of early-stage Parkinson's disease.

METHODS

Forty examinees, including twenty patients who were clinically diagnosed with early Parkinson's disease with a course of 0.5-6 years (PD group) and twenty healthy controls (HC group), underwent head MRI examination. Philips 3.0T (tesla) MR machine was used to measure the imaging indexes of gray matter nuclei in patients with early Parkinson's disease. SWI, QSM, DTI and DKI were used for diagnosis. SPSS (Statistical Product and Service Solutions) 21.0 was used for data analysis.

RESULTS

When SWI was used, fifteen PD patients and six healthy volunteers were diagnosed correctly. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic coincidence rate about the diagnosis of nigrosome-1 on imaging were 75.0%, 30.0%, 51.7%, 54.5% and 52.5% respectively. By contrast, when QSM was used, 19 PD patients and 11 healthy volunteers were diagnosed correctly. The sensitivity, specificity, positive predictive value, negative predictive value and diagnostic coincidence rate about the diagnosis of Nigrosome-one on imaging were 95.0%, 55.0%, 67.9%, 91.7% and 75.0% respectively. The mean kurtosis (MK) value within both the substantia nigra and thalamus, together with the mean diffusivity (MD) within both the substantia nigra and the head of caudate nucleus in PD group was greater than that of HC group. The susceptibility values within the substantia nigra, red nucleus, head of caudate nucleus and putamen of PD group was greater than that of HC group. The MD value in substantia nigra reveals the optimal diagnostic efficiency to distinguish the HC group and the PD group, followed by the MK value in substantia nigra. Specifically, the maximum area under ROC curve (AUC) of the MD value was 0.823, the sensitivity 70.0%, the specificity 85.0%, and the diagnostic threshold 0.414. The area under ROC curve (AUC) of the MK value was 0.695, the sensitivity 95.0%, the specificity 50.0%, and the diagnostic threshold was 0.667. Both of them were statistically significant.

CONCLUSIONS

In the early diagnosis of Parkinson's disease, QSM is more efficient than SWI in observing nigrosome-1 in substantia nigra. In the early diagnosis of Parkinson's disease, MD and MK values of substantia nigra in DKI parameters have higher diagnostic efficiency. The combined scanning of DKI and QSM has the highest diagnostic efficiency and provides imaging basis for clinical diagnosis of early Parkinson's disease.

Serial deep gray nuclear DTI changes in Parkinson's disease over twelve years

Background

Deep gray nuclear pathology relates to motor deterioration in idiopathic Parkinson's disease (PD). Inconsistent deep nuclear diffusion tensor imaging (DTI) findings in cross-sectional or short-term longitudinal studies have been reported. Long-term studies in PD are clinically challenging; decade-long deep nuclear DTI data are nonexistent. We investigated serial DTI changes and clinical utility in a case-control PD cohort of 149 subjects (72 patients/77 controls) over 12 years.

Methods

Participating subjects underwent brain MRI at 1.5T; DTI metrics from segmented masks of caudate, putamen, globus pallidus and thalamus were extracted from three timepoints with 6-year gaps. Patients underwent clinical assessment, including Unified Parkinson Disease Rating Scale Part 3 (UPDRS-III) and Hoehn and Yahr (H&Y) staging. A multivariate linear mixed-effects regression model with adjustments for age and gender was used to assess between-group differences in DTI metrics at each timepoint. Partial Pearson correlation analysis was used to correlate clinical motor scores with DTI metrics over time.

Results

MD progressively increased over time and was higher in the putamen (p < 0.001) and globus pallidus (p = 0.002). FA increased (p < 0.05) in the thalamus at year six, and decreased in the putamen and globus pallidus at year 12. Putaminal (p = 0.0210), pallidal (p = 0.0066) and caudate MD (p < 0.0001) correlated with disease duration. Caudate MD (p < 0.05) also correlated with UPDRS-III and H&Y scores.

Conclusion

Pallido-putaminal MD showed differential neurodegeneration in PD over 12 years on longitudinal DTI; putaminal and thalamic FA changes were complex. Caudate MD could serve as a surrogate marker to track late PD progression.

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.

Higher hippocampal diffusivity values in welders are associated with greater R2* in the red nucleus and lower psychomotor performance

INTRODUCTION

Chronic excessive welding exposure may be related to higher metal accumulation and structural differences in different subcortical structures. We examined how welding affected brain structures and their associations with metal exposure and neurobehavioral consequences.

METHODS

Study includes 42 welders and 31 controls without a welding history. Welding-related structural differences were assessed by volume and diffusion tensor imaging (DTI) metrics in basal ganglia, red nucleus (RN), and hippocampus. Metal exposure was estimated by both exposure questionnaires and whole blood metal levels. Brain metal accumulations were estimated by R1 (for Mn) and R2* (for Fe). Neurobehavioral status was assessed by standard neuropsychological tests.

RESULTS

Compared to controls, welders displayed higher hippocampal mean (MD), axial (AD), and radial diffusivity (RD) (p's < 0.036), but similar DTI or volume in other ROIs (p's > 0.117). Welders had higher blood metal levels (p's < 0.004), higher caudate and RN R2* (p's < 0.014), and lower performance on processing/psychomotor speed, executive function, and visuospatial processing tasks (p's < 0.046). Higher caudate and RN R2* were associated with higher blood Fe and Pb (p's < 0.043), respectively. RN R2* was a significant predictor of all hippocampal diffusivity metrics (p's < 0.006). Higher hippocampal MD and RD values were associated with lower Trail Making Test-A scores (p's < 0.025). A mediation analysis of both groups revealed blood Pb indirectly affected hippocampal diffusivity via RN R2* (p's < 0.041).

DISCUSSION

Welding-related higher hippocampal diffusivity metrics may be associated with higher RN R2* and lower psychomotor speed performance. Future studies are warranted to test the role of Pb exposure in these findings.

Distribution pattern of iron deposition in the basal ganglia of different motor subtypes of Parkinson's disease

OBJECTIVE

The quantitative susceptibility mapping (QSM) technique was used to analyze the distribution pattern of iron deposition in the basal ganglia region of patients with motor subtypes of Parkinson's disease (PD) and to explore the difference in iron content in the basal ganglia region of PD motor subtypes on the major motor symptomatic side.

METHODS

The study included 76 patients with PD and 37 healthy controls (HC). Patients with PD were divided into two groups: postural instability/gait disorder (PIGD)(n = 48), and tremor dominance (TD)(n = 28). We classified patients with PD according to the side of the major motor symptoms as left PIGD (n = 23), left TD (n = 14), right PIGD (n = 25), and right TD (n = 14). All subjects underwent brain magnetic resonance scanning to obtain QSM and susceptibility values in the corresponding regions of interest (ROI).

RESULTS

(1) Compared with the HC, the bilateral SN in the PD-PIGD and TD group showed greater susceptibility values. The susceptibility values in the left CN, bilateral PUT were also greater in the PD-PIGD group than the HC. (2) Compared with the TD, the left PUT susceptibility values were greater in the PIGD group, especially in patients whose major symptomatic side were on the right limb. (3) Correlation analysis showed that in the PD group, bilateral SN was positively correlated with the unified Parkinson's disease rating scale III part scores of the Movement Disorder Society (MDS-UPDRS III) and the Hoehn-Yahr stage. Bilateral dentate nucleus (DN) susceptibility values were significantly positively correlated with TD scores, and left PUT susceptibility values were positively correlated with PIGD scores. The left SN within the PIGD group was positively correlated with the PIGD score.

CONCLUSION

There were different iron deposition patterns in the basal ganglia between the PD-PIGD and TD groups. There also seems to be a difference in iron deposition in PD motor subtypes on different major motor symptom sides.

Comparison of mean diffusivity, R2* relaxation rate and morphometric biomarkers for the clinical differentiation of parkinsonism

INTRODUCTION

Quantitative biomarkers for clinical differentiation of parkinsonian syndromes are still lacking. Our aim was to evaluate the value of combining clinically feasible manual measurements of R2* relaxation rates and mean diffusivity (MD) in subcortical regions and brainstem morphometric measurements to improve the discrimination of parkinsonian syndromes.

METHODS

Twenty-two healthy controls (HC), 25 patients with Parkinson's disease (PD), 19 with progressive supranuclear palsy (PSP) and 27 with multiple system atrophy (MSA, 21 with the parkinsonian variant -MSAp, 6 with the cerebellar variant -MSAc) were recruited. R2*, MD measurements and morphometric biomarkers including the midbrain to pons area ratio and the Magnetic Resonance Parkinsonism Index (MRPI) were compared between groups and their diagnostic performances were assessed.

RESULTS

Morphometric biomarkers discriminated better patients with PSP (ratio: AUC 0.89, MRPI: AUC 0.89) and MSAc (ratio: AUC 0.82, MRPI: AUC 0.75) from other groups. R2* and MD measurements in the posterior putamen performed better in separating patients with MSAp from PD (R2*: AUC 0.89; MD: AUC 0.89). For the three-class classification "MSA vs PD vs PSP", the combination of MD and R2* measurements in the posterior putamen with morphometric biomarkers (AUC: 0.841) outperformed each marker separately. At the individual-level, there were seven discordances between imaging-based prediction and clinical diagnosis involving MSA. Using the new Movement Disorder Society criteria for the diagnosis of MSA, three of these seven patients were clinically reclassified as predicted by quantitative imaging.

CONCLUSION

Combining R2* and MD measurements in the posterior putamen with morphometric biomarkers improves the discrimination of parkinsonism.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach

BACKGROUND AND PURPOSE

Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD.

MATERIALS AND METHODS

A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SN_NM) boundaries and iron-based SN (SN_QSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SN_NM volume, SN_QSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD.

RESULTS

In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SN_NM, and 0.87 and 1.05 for the SN_QSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SN_NM, and 0.87 and 0.96 for the SN_QSM, respectively. The SN_QSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SN_NM volume, SN_QSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SN_QSM volume showed a negative correlation with the MDS-UPDRS III (R² = 0.1, p = 0.036) and the Hoehn and Yahr scale (R² = 0.04, p = 0.013) in PD patients.

CONCLUSION

In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SN_NM volume, SN_QSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Evaluation of an Adoptive Cellular Therapy-Based Vaccine in a Transgenic Mouse Model of α-synucleinopathy

Aggregated α-synuclein, a major constituent of Lewy bodies plays a crucial role in the pathogenesis of α-synucleinopathies (SPs) such as Parkinson's disease (PD). PD is affected by the innate and adaptive arms of the immune system, and recently both active and passive immunotherapies targeted against α-synuclein are being trialed as potential novel treatment strategies. Specifically, dendritic cell-based vaccines have shown to be an effective treatment for SPs in animal models. Here, we report on the development of adoptive cellular therapy (ACT) for SP and demonstrate that adoptive transfer of pre-activated T-cells generated from immunized mice can improve survival and behavior, reduce brain microstructural impairment via magnetic resonance imaging (MRI), and decrease α-synuclein pathology burden in a peripherally induced preclinical SP model (M83) when administered prior to disease onset. This study provides preclinical evidence for ACT as a potential immunotherapy for LBD, PD and other related SPs, and future work will provide necessary understanding of the mechanisms of its action.

Nigral volumetric and microstructural measures in individuals with scans without evidence of dopaminergic deficit

Introduction

Striatal dopamine transporter (DAT) imaging using ¹²³I-ioflupane single photon positron emitted computed tomography (SPECT) (DaTScan, GE) identifies 5-20% of newly diagnosed Parkinson's disease (PD) subjects enrolling in clinical studies to have scans without evidence of dopaminergic deficit (SWEDD). These individuals meet diagnostic criteria for PD, but do not clinically progress as expected, and they are not believed to have neurodegenerative Parkinsonism. Inclusion of SWEDD participants in PD biomarker studies or therapeutic trials may therefore cause them to fail. DaTScan can identify SWEDD individuals, but it is expensive and not widely available; an alternative imaging approach is needed. Here, we evaluate the use of neuromelanin-sensitive, iron-sensitive, and diffusion contrasts in substantia nigra pars compacta (SNpc) to differentiate SWEDD from PD individuals.

Methods

Neuromelanin-sensitive, iron-sensitive, and diffusion imaging data for SWEDD, PD, and control subjects were downloaded from the Parkinson's progression markers initiative (PPMI) database. SNpc volume, SNpc iron (R ₂), and SNpc free water (FW) were measured for each participant.

Results

Significantly smaller SNpc volume was seen in PD as compared to SWEDD (P < 10^-3) and control (P < 10^-3) subjects. SNpc FW was elevated in the PD group relative to controls (P = 0.017). No group difference was observed in SNpc R ₂.

Conclusion

In conclusion, nigral volume and FW in the SWEDD group were similar to that of controls, while a reduction in nigral volume and increased FW were observed in the PD group relative to SWEDD and control participants. These results suggest that these MRI measures should be explored as a cost-effective alternative to DaTScan for evaluation of the nigrostriatal system.

Intrasubject subcortical quantitative referencing to boost MRI sensitivity to Parkinson's disease

Several postmortem studies have shown iron accumulation in the substantia nigra of Parkinson's disease patients. Iron concentration can be estimated via MRI-R₂^∗ mapping. To assess the changes in R₂^∗ occurring in Parkinson's disease patients compared to controls, a multicentre transversal study was carried out on a large cohort of Parkinson's disease patients (n = 163) with matched controls (n = 82). In this study, 44 patients and 11 controls were removed due to motion artefacts, 21 patient and 6 controls to preserve matching. Thus, 98 patients and 65 age and sex-matched healthy subjects were selected with enough image quality. The study was conducted on patients with early to late stage Parkinson's disease. The images were acquired at 3Tesla in 12 clinical centres. R₂^∗ values were measured in subcortical regions of interest (substantia nigra, red nucleus, striatum, globus pallidus externus and globus pallidus internus) contralateral (dominant side) and ipsilateral (non dominant side) to the most clinically affected hemibody. As the observed inter-subject R₂^∗ variability was significantly higher than the disease effect, an original strategy (intrasubject subcortical quantitative referencing, ISQR) was developed using the measurement of R₂^∗ in the red nucleus as an intra-subject reference. R₂^∗ values significantly increased in Parkinson's disease patients when compared with controls; in the substantia nigra (SN) in the dominant side (D) and in the non dominant side (ND), respectively (P_{SN_D} and P_{SN_ND} < 0.0001). After stratification into four subgroups according to the disease duration, no significant R₂^∗ difference was found in all regions of interest when comparing Parkinson's disease subgroups. By applying our ISQR strategy, R_2(ISQR)^∗ values significantly increased in the substantia nigra (P_{SN_D} and P_{SN_ND} < 0.0001) when comparing all Parkinson's disease patients to controls. R_2(ISQR)^∗ values in the substantia nigra significantly increased with the disease duration (P_{SN_D} = 0.01; P_{SN_ND} = 0.03) as well as the severity of the disease (Hoehn & Yahr scale <2 and ≥ 2, P_{SN_D} = 0.02). Additionally, correlations between R_2(ISQR)^∗ and clinical features, mainly related to the severity of the disease, were found. Our results support the use of ISQR to reduce variations not directly related to Parkinson's disease, supporting the concept that ISQR strategy is useful for the evaluation of Parkinson's disease.

Lactoferrin: from the structure to the functional orchestration of iron homeostasis

Iron is by far the most widespread and essential transition metal, possessing crucial biological functions for living systems. Despite chemical advantages, iron biology has forced organisms to face with some issues: ferric iron insolubility and ferrous-driven formation of toxic radicals. For these reasons, acquisition and transport of iron constitutes a formidable challenge for cells and organisms, which need to maintain adequate iron concentrations within a narrow range, allowing biological processes without triggering toxic effects. Higher organisms have evolved extracellular carrier proteins to acquire, transport and manage iron. In recent years, a renewed interest in iron biology has highlighted the role of iron-proteins dysregulation in the onset and/or exacerbation of different pathological conditions. However, to date, no resolutive therapy for iron disorders has been found. In this review, we outline the efficacy of Lactoferrin, a member of the transferrin family mainly secreted by exocrine glands and neutrophils, as a new emerging orchestrator of iron metabolism and homeostasis, able to counteract iron disorders associated to different pathologies, including iron deficiency and anemia of inflammation in blood, Parkinson and Alzheimer diseases in the brain and cystic fibrosis in the lung.

Ex vivo susceptibility-weighted imaging anatomy of canine brain–comparison of imaging and histological sections

Now that access of large domestic mammals to high-field MRI becomes more common, techniques initially implemented for human patients can be used for the structural and functional study of the brain of these animals. Among them, susceptibility-weighted imaging (SWI) is a recent technique obtained from gradient echo (GE) imaging that allow for an excellent anatomical tissue contrast and a non-invasive assessment of brain iron content. The goal of this study was to design an optimal GE SWI imaging protocol to be used in dogs undergoing an MRI examination of the brain in a 3-Tesla scanner. This imaging protocol was applied to ex vivo brains from four dogs. The imaging protocol was validated by visual inspection of the SWI images that provided a high anatomical detail, as demonstrated by their comparison with corresponding microscopic sections. As resolvable brain structures were labeled, this study is the first to provide an anatomic description of SWI images of the canine brain. Once validated in living animals, this GE SWI imaging protocol could be easily included in routine neuroimaging protocols to improve the diagnosis of various intracranial diseases of dogs, or be used in future comparative studies aiming at evaluating brain iron content in animals.

Dynamics of Nigral Iron Accumulation in Parkinson's Disease: From Diagnosis to Late Stage

BACKGROUND

Higher nigral iron has been reported in Parkinson's disease (PD).

OBJECTIVE

The aim is to understand the dynamics of nigral iron accumulation in PD and its association with drug treatment.

METHODS

Susceptibility magnetic resonance imaging data were obtained from 79 controls and 18 drug-naive (PD_DN ) and 87 drug-treated (PD_DT ) PD patients. Regional brain iron in basal ganglia and cerebellar structures was estimated using quantitative susceptibility mapping. Nigral iron was compared between PD_DN and PD_DT subgroups defined by disease duration (early [PDE, <2 years], middle [PDM, 2-6 years], and later [PDL, >6 years]). Associations with both disease duration and types of antiparkinson drugs were explored using regression analysis.

RESULTS

Compared to controls, PD_DN had lower iron in the substantia nigra (P = 0.018), caudate nucleus (P = 0.038), and globus pallidus (P = 0.01) but not in the putamen or red nucleus. In contrast, PD_DT had higher iron in the nigra (P < 0.001) but not in other regions, compared to either controls or PD_DN . Iron in the nigra increased with disease duration (PDE > PD_DN [P = 0.001], PDM > PDE [P = 0.045]) except for PDM versus PDL (P = 0.226). Levodopa usage was associated with higher (P = 0.013) nigral iron, whereas lower nigral iron was correlated with selegiline usage (P = 0.030).

CONCLUSION

Nigral iron is lower before the start of dopaminergic medication and then increases throughout the disease until it plateaus at late stages, suggesting increased iron may not be an etiological factor. Interestingly, PD medications may have differential associations with iron accumulation that need further investigation. © 2022 International Parkinson and Movement Disorder Society.

Microstructural alterations predict impaired bimanual control in Parkinson’s disease

Bimanual coordination is impaired in Parkinson’s disease affecting patients’ ability to perform activities of daily living and to maintain independence. Conveyance of information between cortical and subcortical areas is essential for bimanual coordination and relies on the integrity of cerebral microstructure. As pathological deposition of alpha-synuclein compromises microstructure in Parkinson’s disease, we investigated the relationship between microstructural integrity and bimanual coordination using diffusion-weighted MRI in 23 patients with Parkinson’s disease (mean age ± standard deviation: 56.0 ± 6.45 years; 8 female) and 26 older adults (mean age ± standard deviation: 58.5 ± 5.52 years). Whole-brain analysis revealed specific microstructural alterations between patients and healthy controls matched for age, sex, handedness, and cognitive status congruent with the literature and known Parkinson’s disease pathology. A general linear model revealed distinct microstructural alterations associated with poor bimanual coordination in Parkinson’s disease, corrected for multiple comparisons using a permutation-based approach. Integrating known functional topography, we conclude that distinct changes in microstructure cause an impediment of structures involved in attention, working memory, executive function, motor planning, motor control, and visual processing contributing to impaired bimanual coordination in Parkinson’s disease.

Relationship between brain iron deposition and mitochondrial dysfunction in idiopathic Parkinson's disease

Background

The underlying pathophysiology of Parkinson's disease is complex, involving different molecular pathways, including brain iron deposition and mitochondrial dysfunction. At a molecular level, these disease mechanisms are likely interconnected. Therefore, they offer potential strategies for disease-modifying treatments. We aimed to investigate subcortical brain iron deposition as a potential predictor of the bioenergetic status in patients with idiopathic Parkinson’s disease.

Methods

Thirty patients with idiopathic Parkinson's disease underwent multimodal MR imaging (T1, susceptibility-weighted imaging, SWI) and ³¹phosphorus magnetic resonance spectroscopy imaging. SWI contrast-to-noise ratios served as a measure for brain iron deposition in the putamen, caudate, globus pallidus, and thalamus and were used in a multiple linear regression model to predict in-vivo energy metabolite ratios.

Results

Subcortical brain iron deposition, particularly in the putamen and globus pallidus, was highly predictive of the region-specific amount of high-energy-containing phosphorus metabolites in our subjects.

Conclusions

Our study suggests that brain iron deposition but not the variability of individual volumetric measurements are highly predictive of mitochondrial impairment in vivo. These findings offer the opportunity, e.g., by using chelating therapies, to improve mitochondrial bioenergetics in patients with idiopathic Parkinson's disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00426-9.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

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.

Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges

Background

Parkinson’s disease (PD) diagnosis is yet largely based on the related clinical aspects. However, genetics, biomarkers, and neuroimaging studies have demonstrated a confirming role in the diagnosis, and future developments might be used in a pre-symptomatic phase of the disease.

Main text

This review provides an update on the current applications of neuroimaging modalities for PD diagnosis. A literature search was performed to find published studies that were involved on the application of different imaging modalities for PD diagnosis. An organized search of PubMed/MEDLINE, Embase, ProQuest, Scopus, Cochrane, and Google Scholar was performed based on MeSH keywords and suitable synonyms. Two researchers (TM and JPI) independently and separately performed the literature search. Our search strategy in each database was done by the following terms: ((Parkinson [Title/Abstract]) AND ((“Parkinsonian syndromes ”[Mesh]) OR Parkinsonism [Title/Abstract])) AND ((PET [Title/Abstract]) OR “SPECT”[Mesh]) OR ((Functional imaging, Transcranial sonography [Title/Abstract]) OR “Magnetic resonance spectroscopy ”[Mesh]). Database search had no limitation in time, and our last update of search was in February 2021. To have a comprehensive search and to find possible relevant articles, a manual search was conducted on the reference list of the articles and limited to those published in English.

Conclusion

Early diagnosis of PD could be vital for early management and adequate neuroprotection. Recent neuroimaging modalities such as SPECT and PET imaging using radiolabeled tracers, MRI, and CT are used to discover the disease. By the modalities, it is possible to early diagnose dopaminergic degeneration and also to differentiate PD from others parkinsonian syndromes, to monitor the natural progression of the disease and the effect of neuroprotective treatments on the progression. In this regard, functional imaging techniques have provided critical insights and roles on PD.

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.

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

Background

Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD.

Objective

To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD.

Methods

Thirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone.

Results

For the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42).

Conclusion

Our findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.

Utility of quantitative susceptibility mapping and diffusion kurtosis imaging in the diagnosis of early Parkinson's disease

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Putamen susceptibility value was higher in PD than controls one year into diagnosis.

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Putamen susceptibility value was associated with clinical motor scores in early PD.

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Mean diffusivity revealed greater cellular loss in the lateral substantial nigra.

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Putamen and caudate microstructural degradation were driven by radial diffusivity.

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A composite putamen-caudate DKI-QSM marker classified early PD from controls.

Objective

To investigate the utility of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) as complementary tools in characterizing pathological changes in the deep grey nuclei in early Parkinson’s disease (PD) and their clinical correlates to aid in diagnosis of PD.

Method

Patients with a diagnosis of PD made within a year and age-matched healthy controls were recruited. All participants underwent clinical evaluation using the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) and Hoehn & Yahr stage (H&Y), and brain 3 T MRI including QSM and DKI. Regions-of-interest (ROIs) in the caudate nucleus, putamen, globus pallidus, and medial and lateral substantia nigra (SN) were manually drawn to compare the mean susceptibility (representing iron deposition) and DKI indices (representing restricted water diffusion) between PD patients and healthy controls and in correlation with MDS-UPDRS III and H&Y, focusing on susceptibility value, mean diffusivity (MD) and mean kurtosis (MK).

Results

There were forty-seven PD patients (aged 68.7 years, 51% male, disease duration 0.78 years) and 16 healthy controls (aged 67.4 years, 63% male). Susceptibility value was increased in PD in all ROIs except the caudate, and was significantly different after multiple comparison correction in the putamen (PD: 64.75 ppb, HC: 44.61 ppb, p = 0.004). MD was significantly higher in PD in the lateral SN, putamen and caudate, the regions with the lowest susceptibility value. In PD patients, we found significant association between the MDS-UPDRS III score and susceptibility value in the putamen after correcting for age and sex (β = 0.21, p = 0.003). A composite DKI-QSM diagnostic marker based on these findings successfully differentiated the groups (p < 0.0001) and had “good” classification performance (AUC = 0.88).

Conclusions

QSM and DKI are complementary tools allowing a better understanding of the complex contribution of iron deposition and microstructural changes in the pathophysiology of PD.

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.

HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease

Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.

Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance

Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

Cell Death-Osis of Dopaminergic Neurons and the Role of Iron in Parkinson's Disease

Significance: There is still no cure for neurodegenerative diseases, such as Parkinson's disease (PD). Current treatments are based on the attempt to reduce dopaminergic neuronal loss, and multidisciplinary approaches have been used to provide only a temporary symptoms' relief. In addition to the difficulties of drugs developed against PD to access the brain, the specificity of those inhibitory compounds could be a concern. This because neurons might degenerate by activating distinct signaling pathways, which are often initiated by the same stimulus. Recent Advances: Apoptosis, necroptosis, and ferroptosis were shown to significantly contribute to PD progression and, so far, are the main death programs described as capable to alter brain homeostasis. Their activation is characterized by different biochemical and morphological features, some of which might even share the same molecular players. Critical Issues: If there is a pathological need to engage, in PD, multiple death programs, sequentially or simultaneously, is not clear yet. Possibly the activation of apoptosis, necroptosis, and/or ferroptosis correlates to different PD stages and symptom severities. This would imply that the efficacy of therapeutic approaches against neuronal death might depend on the death program they target and the relevance of this death pathway on a specific PD phase. Future Directions: In this review, we describe the molecular mechanisms underlying the activation of apoptosis, necroptosis, and ferroptosis in PD. Understanding the interrelationship between different death pathways' activation in PD is of utmost importance for the development of therapeutic approaches against disease progression. Antioxid. Redox Signal. 35, 453-473.

Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson's Disease: A 7 Tesla Imaging Study

Background:

In postmortem analysis of late stage Parkinson’s disease (PD) neuronal loss in the substantial nigra (SN) correlates with the antemortem severity of bradykinesia and rigidity, but not tremor.

Objective:

To investigate the relationship between midbrain nuclei volume as an in vivo biomarker for surviving neurons in mild-to-moderate patients using 7.0 Tesla MRI.

Methods:

We performed ultra-high resolution quantitative susceptibility mapping (QSM) on the midbrain in 32 PD participants with less than 10 years duration and 8 healthy controls. Following blinded manual segmentation, the individual volumes of the SN, subthalamic nucleus, and red nucleus were measured. We then determined the associations between the midbrain nuclei and clinical metrics (age, disease duration, MDS-UPDRS motor score, and subscores for bradykinesia/rigidity, tremor, and postural instability/gait difficulty).

Results:

We found that smaller SN correlated with longer disease duration (r = –0.49, p = 0.004), more severe MDS-UPDRS motor score (r = –0.42, p = 0.016), and more severe bradykinesia-rigidity subscore (r = –0.47, p = 0.007), but not tremor or postural instability/gait difficulty subscores. In a hemi-body analysis, bradykinesia-rigidity severity only correlated with SN contralateral to the less-affected hemi-body, and not contralateral to the more-affected hemi-body, possibly reflecting the greatest change in dopamine neuron loss early in disease. Multivariate generalized estimating equation model confirmed that bradykinesia-rigidity severity, age, and disease duration, but not tremor severity, predicted SN volume.

Conclusions:

In mild-to-moderate PD, SN volume relates to motor manifestations in a motor domain-specific and laterality-dependent manner. Non-invasive in vivo 7.0 Tesla QSM may serve as a biomarker in longitudinal studies of SN atrophy and in studies of people at risk for developing PD.

Motor abnormalities and basal ganglia in first-episode psychosis (FEP)

BACKGROUND

Motor abnormalities (MAs) are the primary manifestations of schizophrenia. However, the extent to which MAs are related to alterations of subcortical structures remains understudied.

METHODS

We aimed to investigate the associations of MAs and basal ganglia abnormalities in first-episode psychosis (FEP) and healthy controls. Magnetic resonance imaging was performed on 48 right-handed FEP and 23 age-, gender-, handedness-, and educational attainment-matched controls, to obtain basal ganglia shape analysis, diffusion tensor imaging techniques (fractional anisotropy and mean diffusivity), and relaxometry (R2*) to estimate iron load. A comprehensive motor battery was applied including the assessment of parkinsonism, catatonic signs, and neurological soft signs (NSS). A fully automated model-based segmentation algorithm on 1.5T MRI anatomical images and accurate corregistration of diffusion and T2* volumes and R2* was used.

RESULTS

FEP patients showed significant local atrophic changes in left globus pallidus nucleus regarding controls. Hypertrophic changes in left-side caudate were associated with higher scores in sensory integration, and in right accumbens with tremor subscale. FEP patients showed lower fractional anisotropy measures than controls but no significant differences regarding mean diffusivity and iron load of basal ganglia. However, iron load in left basal ganglia and right accumbens correlated significantly with higher extrapyramidal and motor coordination signs in FEP patients.

CONCLUSIONS

Taken together, iron load in left basal ganglia may have a role in the emergence of extrapyramidal signs and NSS of FEP patients and in consequence in the pathophysiology of psychosis.

Progressive microstructural alterations in subcortical nuclei in Parkinson's disease: A diffusion magnetic resonance imaging study

OBJECTIVES

To explore the microstructural alterations in subcortical nuclei in Parkinson's disease (PD) at different stages with diffusion kurtosis imaging (DKI) and tensor imaging and to test the performance of diffusion metrics in identifying PD.

METHODS

108 PD patients (64 patients in early-stage PD group (EPD) and 44 patients in moderate-late-stage PD group (MLPD)) and 64 healthy controls (HC) were included. Tensor and kurtosis metrics in the subcortical nuclei were compared. Partial correlation was used to correlate the diffusion metrics and Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) score. Logistic regression and receiver operating characteristic analysis were applied to test the diagnostic performance of the diffusion metrics.

RESULTS

Compared with HC, both EPD and MLPD patients showed higher fractional anisotropy and axial diffusivity, lower mean kurtosis (MK) and axial kurtosis in substantia nigra, lower MK and radial kurtosis (RK) in globus pallidus (GP) and thalamus (all p < 0.05). Compared with EPD, MLPD patients showed lower MK and RK in GP and thalamus (all p < 0.05). MK and RK in GP and thalamus were negatively correlated with UPDRS-III score (all p < 0.01). The logistic regression model combining kurtosis and tensor metrics showed the best performance in diagnosing PD, EPD, and MLPD (areas under curve were 0.817, 0.769, and 0.914, respectively).

CONCLUSIONS

PD has progressive microstructural alterations in the subcortical nuclei. DKI is sensitive to detect microstructural alterations in GP and thalamus during PD progression. Combining kurtosis and tensor metrics can achieve a good performance in diagnosing PD.

Measuring the iron content of dopaminergic neurons in substantia nigra with MRI relaxometry

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Dopaminergic neurons dominate effective transverse relaxation in nigrosome 1.

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Ion beam microscopy reveals highest iron concentrations in dopaminergic neurons.

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Developed biophysical model links MRI parameters to cellular iron content.

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Ferritin- and neuromelanin-bound iron impact MRI parameters differently.

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Quantitative MRI provides a potential biomarker of iron in dopaminergic neurons.

In Parkinson’s disease, the depletion of iron-rich dopaminergic neurons in nigrosome 1 of the substantia nigra precedes motor symptoms by two decades. Methods capable of monitoring this neuronal depletion, at an early disease stage, are needed for early diagnosis and treatment monitoring. Magnetic resonance imaging (MRI) is particularly suitable for this task due to its sensitivity to tissue microstructure and in particular, to iron. However, the exact mechanisms of MRI contrast in the substantia nigra are not well understood, hindering the development of powerful biomarkers. In the present report, we illuminate the contrast mechanisms in gradient and spin echo MR images in human nigrosome 1 by combining quantitative 3D iron histology and biophysical modeling with quantitative MRI on post mortem human brain tissue. We show that the dominant contribution to the effective transverse relaxation rate (R2*) in nigrosome 1 originates from iron accumulated in the neuromelanin of dopaminergic neurons. This contribution is appropriately described by a static dephasing approximation of the MRI signal. We demonstrate that the R2* contribution from dopaminergic neurons reflects the product of cell density and cellular iron concentration. These results demonstrate that the in vivo monitoring of neuronal density and iron in nigrosome 1 may be feasible with MRI and provide directions for the development of biomarkers for an early detection of dopaminergic neuron depletion in Parkinson’s disease.

Assessing brain iron and volume of subcortical nuclei in idiopathic rapid eye movement sleep behavior disorder

STUDY OBJECTIVES

The relationship of iron with cognitive and motor impairment in idiopathic rapid eye movement sleep behavior disorder (iRBD) remains unknown.

METHODS

Twenty-nine (29) patients and 28 healthy controls (HCs) underwent susceptibility weighted imaging and susceptibility mapping. These images were used to evaluate the nigrosome-1 (N1) sign in the substantia nigra (SN), global and regional high-iron (RII) content and volume of subcortical nuclei.

RESULTS

The number of iRBD patients with N1 loss (12) was significantly higher than HCs (2) (P=0.005). Compared with HCs, the iRBD patients had reduced volume of the right caudate nucleus (RCN) (P<0.05, FDR correction) but no significant changes in global and RII iron of the subcortical nuclei (all P>0.05, FDR correction). Multiple regression analysis revealed that: for cognitive function, the RII iron of the RCN was significantly correlated with visuospatial function and the global iron of the right dentate nucleus (RDN) was correlated with memory function; for motor function, the RII iron of the left DN (LDN) and global iron of the left CN correlated with the Alternate-Tap test (left, average), the global iron of the LDN correlated with the Alternate-Tap test (right), and the global iron of the left GP correlated with the 3-meter Timed Up and Go test (all P<0.05, FDR correction).

CONCLUSIONS

Our exploratory analysis found that iRBD patients had a higher incidence of N1 loss and reduced RCN volume after FDR correction. Cognitive and motor impairment were associated with iron deposition in several cerebral nuclei after FDR correction.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Subcortical microstructural diffusion changes correlate with gait impairment in Parkinson's disease

BACKGROUND

Gait impairments are common in Parkinson's Disease (PD) and are likely caused by degeneration in multiple brain circuits, including the basal ganglia, thalamus and mesencephalic locomotion centers (MLC). Diffusion tensor imaging (DTI) assesses fractional anisotropy (FA) and mean diffusivity (MD) that reflect the integrity of neuronal microstructure. We hypothesized that DTI changes in motor circuits correlate with gait changes in PD.

OBJECTIVE

We aimed to identify microstructural changes of brain locomotion control centers in PD via DTI and their correlations with clinical and quantitative measures of gait.

METHODS

Twenty-one PD patients reporting gait impairment and 15 controls were recruited. Quantitative gait and clinical tests were recorded in PD subjects' medication ON and OFF states. Region of Interest (ROI) analysis of the thalamus, basal ganglia and MLC was performed using ExploreDTI. Correlations between FA/MD with clinical gait parameters were examined.

RESULTS

Microstructural changes were seen in the thalamus, caudate and MLC in the PD compared to the control group. Thalamic microstructural changes significantly correlated with gait parameters in the pace domain including the Timed Up and Go in the ON state. Caudate changes correlated with cadence and stride time in the OFF state.

CONCLUSIONS

Our pilot study suggests that PD is associated with a characteristic regional pattern of microstructural degradation in the thalamus, caudate and MLC. The DTI changes may represent subcortical locomotion network failure. Overall, DTI ROI analyses might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait, and potentially could serve as an imaging marker.

Brain Microstructural Properties Related to Subjective Well-Being: Diffusion Tensor Imaging Analysis

Although it is known that health is not merely the absence of disease, the positive aspects of mental health have been less comprehensively researched compared with its negative aspects. Subjective well-being (SWB) is one of the indicators of positive psychology, and high SWB is considered to benefit individuals in multiple ways. However, the neural mechanisms underlying individual differences in SWB remain unclear, particularly in terms of brain microstructural properties as detected by diffusion tensor imaging. The present study aimed to investigate the relationship between measurements of diffusion tensor imaging [mean diffusivity (MD) and fractional anisotropy] and the degree of SWB as measured using a questionnaire. Voxel-based analysis was used to investigate the association between MD and SWB scores in healthy young adults (age, 20.7 ± 1.8 years; 695 males and 514 females). Higher levels of SWB were found to be associated with lower MD in areas surrounding the right putamen, insula, globus pallidus, thalamus and caudate. These results indicated that individual SWB is associated with variability in brain microstructural properties.

Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis

INTRODUCTION

The current diagnosis of Parkinson's disease (PD) comorbidity with depression (DPD) largely depends on clinical evaluation. However, the modality may tend to lack precision in detecting PD with depression. A radiomic approach that combines functional connectivity and activity with clinical scores has the potential to achieve accurate and differential diagnosis between PD and DPD.

METHODS

In this study, we aimed to employ the radiomic approach to extract large-scale features of functional connectivity and activity for differentiating among DPD, PD with no depression (NDPD), and healthy controls (HC). We extracted 6,557 features of five types from all subjects including clinical characteristics, resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and voxel-mirrored homotopic connectivity (VMHC). Lasso, random forest, and support vector machine (SVM) were implemented for feature selection and dimension reduction based on the training sets, and the prediction performance for different methods in the testing sets was compared.

RESULTS

The results showed that nineteen features were selected for the group of DPD versus HC, 34 features were selected for the group of NDPD versus HC, and 17 features were retained for the group of DPD versus NDPD. In the testing sets, Lasso prediction achieved the accuracies of 0.95, 0.96, and 0.85 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively. Random forest achieved the accuracies of 0.90, 0.82, and 0.90 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively, while SVM yielded the accuracies of 1, 0.86 and 0.65 for distinguishing between DPD and HC, NDPD and HC, and DPD and NDPD, respectively.

CONCLUSIONS

By identifying aberrant functional connectivity and activity as potential biomarkers, the radiomic approach facilitates a deeper understanding and provides new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning

Background: Patients with Parkinson's disease (PD) and progressive supranuclear palsy Richardson's syndrome (PSP-RS) often show overlapping clinical features, leading to misdiagnoses. The objective of this study was to investigate the feasibility and utility of using multi-modal MRI datasets for an automatic differentiation of PD patients, PSP-RS patients, and healthy control (HC) subjects. Material and Methods: T1-weighted, T2-weighted, and diffusion-tensor (DTI) MRI datasets from 45 PD patients, 20 PSP-RS patients, and 38 HC subjects were available for this study. Using an atlas-based approach, regional values of brain morphology (T1-weighted), brain iron metabolism (T2-weighted), and microstructural integrity (DTI) were measured and employed for feature selection and subsequent classification using combinations of various established machine learning methods. Results: The optimal machine learning model using regional morphology features only achieved a classification accuracy of 65% (67/103 correct classifications) differentiating PD patients, PSP-RS patients, and HC subjects. The optimal machine learning model using only quantitative T2 values performed slightly better and achieved an accuracy of 75.7% (78/103). The optimal classifier using DTI features alone performed considerably better with 95.1% accuracy (98/103). The optimal multi-modal classifier using all features also achieved an accuracy of 95.1% but required more features and achieved a slightly lower F1-score compared to the optimal model using DTI features alone. Conclusion: Machine learning models using multi-modal MRI perform significantly better than uni-modal machine learning models using morphological parameters based on T1-weighted MRI datasets alone or brain iron metabolism markers based on T2-weighted MRI datasets alone. However, machine learnig models using regional brain microstructural integrity metrics computed from DTI datasets perform similar to the optimal multi-modal machine learning model. Thus, given the results from this study cohort, it appears that morphology and brain iron metabolism markers may not provide additional value for classification compared to using DTI metrics alone.

Utility of manual fractional anisotropy measurements in the management of patients with Parkinson disease: a feasibility study with a 1.5-T magnetic resonance imaging system

Background

Diffusion tensor imaging has emerged as a promising tool for quantitative analysis of neuronal damage in Parkinson disease, with potential value for diagnostic and prognostic evaluation.

Purpose

The aim of this study was to examine Parkinson disease-associated alterations in specific brain regions revealed by diffusion tensor imaging and how such alterations correlate with clinical variables.

Material and Methods

Diffusion tensor imaging was performed on 42 Parkinson disease patients and 20 healthy controls with a 1.5-T scanner. Manual fractional anisotropy measurements were performed for the ventral, intermediate, and dorsal portions of the substantia nigra, as well as for the cerebral peduncles, putamen, thalamus, and supplementary motor area. The correlation analysis between these measurements and the clinical variables was performed using χ² variance and multiple linear regression.

Results

Compared to healthy controls, Parkinson disease patients had significantly reduced fractional anisotropy values in the substantia nigra (P < .05). Some fractional anisotropy measurements in the substantia nigra correlated inversely with duration of Parkinson disease and Parkinson disease severity scores. Reduced fractional anisotropy values in the substantia nigra were also correlated inversely with age variable. fractional anisotropy values obtained for the right and left putamen varied significantly between males and females in both groups.

Conclusion

Manual fractional anisotropy measurements in the substantia nigra were confirmed to be feasible with a 1.5-T scanner. Diffusion tensor imaging data can be used as a reliable biomarker of Parkinson disease that can be used to support diagnosis, prognosis, and progression/treatment monitoring.

Biomarkers and the Role of α-Synuclein in Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the presence of α-synuclein (α-Syn)-rich Lewy bodies (LBs) and the preferential loss of dopaminergic (DA) neurons in the substantia nigra (SN) pars compacta (SNpc). However, the widespread involvement of other central nervous systems (CNS) structures and peripheral tissues is now widely documented. The onset of the molecular and cellular neuropathology of PD likely occurs decades before the onset of the motor symptoms characteristic of PD, so early diagnosis of PD and adequate tracking of disease progression could significantly improve outcomes for patients. Because the clinical diagnosis of PD is challenging, misdiagnosis is common, which highlights the need for disease-specific and early-stage biomarkers. This review article aims to summarize useful biomarkers for the diagnosis of PD, as well as the biomarkers used to monitor disease progression. This review article describes the role of α-Syn in PD and how it could potentially be used as a biomarker for PD. Also, preclinical and clinical investigations encompassing genetics, immunology, fluid and tissue, imaging, as well as neurophysiology biomarkers are discussed. Knowledge of the novel biomarkers for preclinical detection and clinical evaluation will contribute to a deeper understanding of the disease mechanism, which should more effectively guide clinical applications.

Cell specific quantitative iron mapping on brain slices by immuno-µPIXE in healthy elderly and Parkinson's disease

Iron is essential for neurons and glial cells, playing key roles in neurotransmitter synthesis, energy production and myelination. In contrast, high concentrations of free iron can be detrimental and contribute to neurodegeneration, through promotion of oxidative stress. Particularly in Parkinson’s disease (PD) changes in iron concentrations in the substantia nigra (SN) was suggested to play a key role in degeneration of dopaminergic neurons in nigrosome 1. However, the cellular iron pathways and the mechanisms of the pathogenic role of iron in PD are not well understood, mainly due to the lack of quantitative analytical techniques for iron quantification with subcellular resolution. Here, we quantified cellular iron concentrations and subcellular iron distributions in dopaminergic neurons and different types of glial cells in the SN both in brains of PD patients and in non-neurodegenerative control brains (Co). To this end, we combined spatially resolved quantitative element mapping using micro particle induced X-ray emission (µPIXE) with nickel-enhanced immunocytochemical detection of cell type-specific antigens allowing to allocate element-related signals to specific cell types. Distinct patterns of iron accumulation were observed across different cell populations. In the control (Co) SNc, oligodendroglial and astroglial cells hold the highest cellular iron concentration whereas in PD, the iron concentration was increased in most cell types in the substantia nigra except for astroglial cells and ferritin-positive oligodendroglial cells. While iron levels in astroglial cells remain unchanged, ferritin in oligodendroglial cells seems to be depleted by almost half in PD. The highest cellular iron levels in neurons were located in the cytoplasm, which might increase the source of non-chelated Fe³⁺, implicating a critical increase in the labile iron pool. Indeed, neuromelanin is characterised by a significantly higher loading of iron including most probable the occupancy of low-affinity iron binding sites. Quantitative trace element analysis is essential to characterise iron in oxidative processes in PD. The quantification of iron provides deeper insights into changes of cellular iron levels in PD and may contribute to the research in iron-chelating disease-modifying drugs.