Patterns of Grey Matter Atrophy at Different Stages of Parkinson's and Alzheimer's Diseases and Relation to Cognition

MRI Radiomics Nomogram for Predicting Disease Transition Time and Risk Stratification in Preclinical Alzheimer's Disease

RATIONALE AND OBJECTIVES

Accurate prediction of the progression of preclinical Alzheimer's disease (AD) is crucial for improving clinical management and disease prognosis. The objective of this study was to develop and validate clinical-radimoics integrated model to predict the time to progression (TTP) and disease risk stratification of preclinical AD.

MATERIALS AND METHODS

A total of 244 cases (mean age: 73.8 ± 5.5 years, 120 women) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly divided into the training cohort (n = 172) and validation cohort (n = 72) using a 7:3 ratio. Clinical factors were identified by univariate and multivariate COX regression. Radiomics features were extracted from GM, WM and CSF of T1WI images and selected by Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO). Using selected clinical factors and radiomics features, the clinical, radimocis and clinical-radiomics nomogram models were developed for predicting the TTP. The performance of each model was assessed by C-index. The risk stratification ability and predicting efficacy of the clinical-radiomics model were utilizing the Kaplan-Meier curve and receiver operator characteristic (ROC) curve.

RESULTS

The C-index of clinical, radimocis and clinical-radiomics models were 0.852 (95% confidence interval[CI]:0.810-0.893), 0.863 (95%CI:0.816-0.910) and 0.903 (95%:0.870-0.936) in the training cohort and 0.725 (95%CI:0.630-0.820), 0.788 (95%CI:0.678-0.898), 0.813(95%CI:0.734-0.892) in the validation cohort. The AUCs of the multi-predictor nomogram at 1-, 3-, 5- and 7-year were 0.894, 0.908, 0.930, 0.979 in the training cohort and 0.671, 0.726, 0.839, 0.931 in the validation cohort.

CONCLUSION

In this study, we constructed a clinical-radimoics integrated model to predict the progression of preclinical AD and stratified the risk of disease progression in preclinical AD.

Spatiotemporal neurodegeneration of the substantia nigra and its connecting cortex and subcortex in Parkinson's disease

BACKGROUND AND PURPOSE

Neurodegeneration is uneven in Parkinson's disease (PD). This study aimed to investigate spatiotemporal neurodegeneration in functional subregions of the substantia nigra (SN) and their connected cortex and subcortex in people with PD.

METHODS

A total of 120 patients with early-stage PD, 45 patients with advanced PD, and 120 healthy controls (HCs) were enrolled. The SN, cortex, and subcortex were divided into sensorimotor, associative, and limbic regions, respectively. Iron deposition in the SN was assessed by quantitative susceptibility mapping (QSM). Cortex and subcortex volumes were calculated based on T1-weighted imaging. Region of interest (ROI) analysis and voxel-based analysis (VBA) were performed to explore spatiotemporal neurodegeneration in patients with PD. p values were corrected for false discovery rate.

RESULTS

In the ROI analysis, the QSM values for the limbic (p = 0.018) and sensorimotor SN subregions (p = 0.018) were higher in PD patients than in HCs, but were not higher in the associative SN subregion (p = 0.295). In VBA, all SN functional subregions had clusters with higher QSM values in PD patients than in HCs (p < 0.001). The limbic SN subregion was the only one in which iron deposition increased from early-stage to advanced PD (p = 0.023). The QSM values of VBA_limbic, sensorimotor, and associative SN had subregion-specific correlations with disease severity (p = 0.001 for the limbic and sensorimotor subregions, p = 0.003 for the associative subregion), motor symptoms (p = 0.057 for the limbic and sensorimotor subregion), and depression scores (p = 0.036 for the limbic subregion).

CONCLUSION

Iron deposition in SN functional subregions and atrophy of cortical and subcortical structures connected with the SN showed spatiotemporal selectivity. These findings reveal the potential pathogenesis of clinical heterogeneity in PD.

Biomarkers for cognitive impairment in alpha-synucleinopathies: an overview of systematic reviews and meta-analyses

Cognitive impairment (CI) is common in α-synucleinopathies, i.e., Parkinson's disease, Lewy bodies dementia, and multiple system atrophy. We summarize data from systematic reviews/meta-analyses on neuroimaging, neurophysiology, biofluid and genetic diagnostic/prognostic biomarkers of CI in α-synucleinopathies. Diagnostic biomarkers include atrophy/functional neuroimaging brain changes, abnormal cortical amyloid and tau deposition, and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers, cortical rhythm slowing, reduced cortical cholinergic and glutamatergic and increased cortical GABAergic activity, delayed P300 latency, increased plasma homocysteine and cystatin C and decreased vitamin B12 and folate, increased CSF/serum albumin quotient, and serum neurofilament light chain. Prognostic biomarkers include brain regional atrophy, cortical rhythm slowing, CSF amyloid biomarkers, Val66Met polymorphism, and apolipoprotein-E ε2 and ε4 alleles. Some AD/amyloid/tau biomarkers may diagnose/predict CI in α-synucleinopathies, but single, validated diagnostic/prognostic biomarkers lack. Future studies should include large consortia, biobanks, multi-omics approach, artificial intelligence, and machine learning to better reflect the complexity of CI in α-synucleinopathies.

Small effects of olfactory identification and discrimination on global cognitive and executive performance over 1 year in aging people without a history of age-related cognitive impairment

Olfactory and cognitive performance share neural correlates profoundly affected by physiological aging. However, whether odor identification and discrimination scores predict global cognitive status and executive function in healthy older people with intact cognition is unclear. Therefore, in the present study, we set out to elucidate these links in a convenience sample of 204 independently living, cognitively intact healthy Czech adults aged 77.4 ± 8.7 (61-97 years) over two waves of data collection (one-year interval). We used the Czech versions of the Montreal Cognitive Assessment (MoCA) to evaluate global cognition, and the Prague Stroop Test (PST), Trail Making Test (TMT), and several verbal fluency (VF) tests to assess executive function. As a subsidiary aim, we aimed to examine the contribution of olfactory performance towards achieving a MoCA score above vs. below the published cut-off value. We found that the MoCA scores exhibited moderate associations with both odor identification and discrimination. Furthermore, odor identification significantly predicted PST C and C/D scores. Odor discrimination significantly predicted PST C/D, TMT B/A, and standardized composite VF scores. Our findings demonstrate that olfaction, on the one hand, and global cognition and executive function, on the other, are related even in healthy older people.

Gray matter morphometric biomarkers for distinguishing manganese-exposed welders from healthy adults revealed by source-based morphometry

BACKGROUND

Chronic overexposure to manganese (Mn) may result in neurotoxicity, which is characterized by motor and cognitive dysfunctions. This study aimed to utilize multivariate source-based morphometry (SBM) to explore the biomarkers for distinguishing Mn-exposed welders from healthy controls (HCs).

METHODS

High-quality 3D T1-weighted MRI scans were obtained from 45 Mn-exposed full-time welders and 33 age-matched HCs in this study. After extracting gray matter structural covariation networks by SBM, multiple classic interaction linear models were applied to investigate distinct patterns in welders compared to HCs, and Z-transformed loading coefficients were compared between the two groups. A receiver operating characteristic (ROC) curve was used to identify potential biomarkers for distinguishing Mn-exposed welders from HCs. Additionally, we assessed the relationships between clinical features and gray matter volumes in the welders group.

RESULTS

A total of 78 subjects (45 welders, mean age 46.23±4.93 years; 33 HCs, mean age 45.55±3.40 years) were evaluated. SBM identified five components that differed between the groups. These components displayed lower loading weights in the basal ganglia, thalamus, default mode network (including the lingual gyrus and precuneus), and temporal lobe network (including the temporal pole and parahippocampus), as well as higher loading weights in the sensorimotor network (including the supplementary motor cortex). ROC analysis identified the highest classification power in the thalamic network.

CONCLUSIONS

Altered brain structures might be implicated in Mn overexposure-related disturbances in motivative modulation, cognitive control and information integration. These results encourage further studies that focus on the interaction mechanisms, including the basal ganglia network, thalamic network and default mode network. Our study identified potential neurobiological markers in Mn-exposed welders and illustrated the utility of a multivariate method of gray matter analysis.

Does the Montreal Cognitive Assessment (MoCA) identify cognitive impairment profiles in Parkinson's disease? An exploratory study

An important proportion of patients with Parkinson's Disease (PD) present signs of cognitive impairment, although this is heterogeneous. In an attempt to classify this, the dual syndrome hypothesis distinguishes between two profiles: one defined by attentional and executive problems with damage in anterior cerebral regions, and another with mnesic and visuospatial alterations, with damage in posterior cerebral regions. The Montreal Cognitive Assessment (MoCA) is one of the recommended screening tools, and one of the most used, to assess cognitive impairment in PD. However, its ability to specifically identify these two profiles of cognitive impairment has not been studied. The aim of this study was, therefore, to analyze the capacity of the MoCA to detect cognitive impairment, and also to identify anterior and posterior profiles defined by the dual syndrome hypothesis. For this purpose, 59 patients with idiopathic PD were studied with the MoCA and a neuropsychological battery of tests covering all cognitive domains. Results of logistic regression analysis with ROC (Receiver Operating Characteristic) curves showed that MoCA detected cognitive impairment and identified patients with a profile of anterior/attentional and executive deficit, with acceptable sensibility and specificity. However, it did not identify patients with a posterior/mnesic-visuospatial impairment. We discuss the reasons for the lack of sensitivity of MoCA in this profile, and other possible implications of these results with regards the usefulness of this tool to assess cognitive impairment in PD.

Striato-cortical functional connectivity changes in mild cognitive impairment with Lewy bodies

BACKGROUND

Functional connectivity changes in clinically overt neurodegenerative diseases such as dementia with Lewy bodies have been described, but studies on connectivity changes in the pre-dementia phase are scarce.

OBJECTIVES

We concentrated on evaluating striato-cortical functional connectivity differences between patients with Mild Cognitive Impairment with Lewy bodies and healthy controls and on assessing the relation to cognition.

METHODS

Altogether, we enrolled 77 participants (47 patients, of which 35 met all the inclusion criteria for the final analysis, and 30 age- and gender-matched healthy controls, of which 28 met all the inclusion criteria for the final analysis) to study the seed-based connectivity of the dorsal, middle, and ventral striatum. We assessed correlations between functional connectivity in the regions of between-group differences and neuropsychological scores of interest (visuospatial and executive domains z-scores).

RESULTS

Subjects with Mild Cognitive Impairment with Lewy Bodies, as compared to healthy controls, showed increased connectivity from the dorsal part of the striatum particularly to the bilateral anterior part of the temporal cortex with an association with executive functions.

CONCLUSIONS

We were able to capture early abnormal connectivity within cholinergic and noradrenergic pathways that correlated with cognitive functions known to be linked to cholinergic/noradrenergic deficits. The knowledge of specific alterations may improve our understanding of early neural changes in pre-dementia stages and enhance research of disease modifying therapy.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study

Background

Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry.

Objective

We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain.

Methods

Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates.

Results

aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction).

Conclusions

Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Histological Study on the Thickness of Gray Matter at the Summit and Bottom of Folium in Different Age Groups of Bangladeshi People

Context The cerebellum is a part of the hindbrain and consists of cortical gray matter (GM) at the surface and a medullary core of white matter (WM). The GM contains a cell body of neurons that helps process and transmit any command type through nerve fibers found in the WM. The main functions of GM in the central nervous system empower persons to control motor activity, recollection, and passion. So, this research aims to assess the thickness of GM at the summit and bottom of folia by histologically studying the cerebellum cortex. Methods The collection of data was a descriptive type of cross-sectional study. The method was the purposive type. This study was conducted from August 2016 to March 2017, and the research was carried out at Mymensingh Medical College's Department of Anatomy, Bangladesh. Specimens containing cerebellum were preserved from Bangladeshi cadavers according to sexes and ages ranging in years. We chose fresh specimens from people who died within the last 12 hours and preserved them in 10% formol saline. The size of the tissue that was collected for the histological study was not more than 2 cm² and not more than 4-5 mm thick. Then the tissue was placed in 10% formol saline. This fluid was used for quick fixation and partial dehydration of the tissue. After dehydration, each tissue segment is processed for infiltration and embedding separately. Every section was stained with hematoxylin and eosin stain (H&E) before being coated with dibutyl phthalate polystyrene xylene (DPX) coverslips on slides. Result The mean (±SD) thickness of GM at the summit of folium was 886.2±29.7µm in Group A, 925.2±25.9µm in Group B, 912.7±22.3µm in Group C, and 839.9±40.7µm in Group D. Mean (±SD) GM thickness at the bottom of the fissure was 395.6±12.2 µm, 403.9±26.0µm, 380.4±23.4 µm, and 375.8±28.8 µm in Groups A, B, C, and D respectively. Conclusion The thickness of the cortex is an essential factor in the normal development process, and it was similar in the current study. Normal aging, Alzheimer's disease, and other dementias cause reduced GM which makes the cortical sheet thin. Huntington's disease, corticobasal degeneration, amyotrophic lateral sclerosis, and schizophrenia are all examples of neurological disorders. Cortical thinning is typically locally localized, and the progression of atrophy can thus disclose much about a disease's history and causal variables. The present study correspondingly found that GM was reduced after the age of 50 years onward. Furthermore, longitudinal investigations of cortical atrophy have the potential to be extremely useful in measuring the efficacy of a wide range of treatments.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

BACKGROUND

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

METHODS

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

RESULTS

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

CONCLUSIONS

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Anesthetic-loaded nanodroplets with focused ultrasound reduces agitation in Alzheimer's mice

OBJECTIVE

Alzheimer's disease (AD) is often associated with neuropsychiatric symptoms, including agitation and aggressive behavior. These symptoms increase with disease severity, ranging from 10% in mild cognitive impairment to 50% in patients with moderate-to-severe AD, pose a great risk for self-injury and injury to caregivers, result in high rates of institutionalization and great suffering for patients and families. Current pharmacological therapies have limited efficacy and a high potential for severe side effects. Thus, there is a growing need to develop novel therapeutics tailored to safely and effectively reduce agitation and aggressive behavior in AD. Here, we investigate for the first time the use of focused ultrasound combined with anesthetic-loaded nanodroplets (nanoFUS) targeting the amygdala (key structure in the neurocircuitry of agitation) as a novel minimally invasive tool to modulate local neural activity and reduce agitation and aggressive behavior in the TgCRND8 AD transgenic mice.

METHODS

Male and female animals were tested in the resident-intruder (i.e., aggressive behavior) and open-field tests (i.e., motor agitation) for baseline measures, followed by treatment with active- or sham-nanoFUS. Behavioral testing was then repeated after treatment.

RESULTS

Active-nanoFUS neuromodulation reduced aggressive behavior and agitation in male mice, as compared to sham-treated controls. Treatment with active-nanoFUS increased the time male mice spent in social-non-aggressive behaviors.

INTERPRETATION

Our results show that neuromodulation with active-nanoFUS may be a potential therapeutic tool for the treatment of neuropsychiatric symptoms, with special focus on agitation and aggressive behaviors. Further studies are necessary to establish cellular, molecular and long-term behavioral changes following treatment with nanoFUS.

Prediction value of the genetic risk of type 2 diabetes on the amnestic mild cognitive impairment conversion to Alzheimer’s disease

Amnestic mild cognitive impairment (aMCI) and Type 2 diabetes mellitus (T2DM) are both important risk factors for Alzheimer’s disease (AD). We aimed to investigate whether a T2DM-specific polygenic risk score (PRS_sT2DM) can predict the conversion of aMCI to AD and further explore the underlying neurological mechanism. All aMCI patients were from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database and were divided into conversion (aMCI-C, n = 164) and stable (aMCI-S, n = 222) groups. PRS_sT2DM was calculated by PRSice-2 software to explore the predictive efficacy of the aMCI conversion to AD. We found that PRS_sT2DM could independently predict the aMCI conversion to AD after removing the common variants of these two diseases. PRS_sT2DM was significantly negatively correlated with gray matter volume (GMV) of the right superior frontal gyrus in the aMCI-C group. In all aMCI patients, PRS_sT2DM was significantly negatively correlated with the cortical volume of the right superior occipital gyrus. The cortical volume of the right superior occipital gyrus could significantly mediate the association between PRS_sT2DM and aMCI conversion. Gene-based analysis showed that T2DM-specific genes are highly expressed in cortical neurons and involved in ion and protein binding, neural development and generation, cell junction and projection, and PI3K-Akt and MAPK signaling pathway, which might increase the aMCI conversion by affecting the Tau phosphorylation and amyloid-beta (Aβ) accumulation. Therefore, the PRS_sT2DM could be used as a measure to predict the conversion of aMCI to AD.

Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms

The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.

Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer's Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort

BACKGROUND

Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker.

OBJECTIVE

We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients.

METHODS

One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform.

RESULTS

The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed.

CONCLUSION

Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.

Morphological basis of Parkinson disease-associated cognitive impairment: an update

Cognitive impairment is one of the most salient non-motor symptoms of Parkinson disease (PD) that poses a significant burden on the patients and carers as well as being a risk factor for early mortality. People with PD show a wide spectrum of cognitive dysfunctions ranging from subjective cognitive decline and mild cognitive impairment (MCI) to frank dementia. The mean frequency of PD with MCI (PD-MCI) is 25.8% and the pooled dementia frequency is 26.3% increasing up to 83% 20 years after diagnosis. A better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Modern neuroimaging studies revealed considerable changes in gray and white matter in PD patients with cognitive impairment, cortical atrophy, hypometabolism, dopamine/cholinergic or other neurotransmitter dysfunction and increased amyloid burden, but multiple mechanism are likely involved. Combined analysis of imaging and fluid markers is the most promising method for identifying PD-MCI and Parkinson disease dementia (PDD). Morphological substrates are a combination of Lewy- and Alzheimer-associated and other concomitant pathologies with aggregation of α-synuclein, amyloid, tau and other pathological proteins in cortical and subcortical regions causing destruction of essential neuronal networks. Significant pathological heterogeneity within PD-MCI reflects deficits in various cognitive domains. This review highlights the essential neuroimaging data and neuropathological changes in PD with cognitive impairment, the amount and topographical distribution of pathological protein aggregates and their pathophysiological relevance. Large-scale clinicopathological correlative studies are warranted to further elucidate the exact neuropathological correlates of cognitive impairment in PD and related synucleinopathies as a basis for early diagnosis and future disease-modifying therapies.

Automated High-Definition MRI Processing Routine Robustly Detects Longitudinal Morphometry Changes in Alzheimer's Disease Patients

Longitudinal MRI studies are of increasing importance to document the time course of neurodegenerative diseases as well as neuroprotective effects of a drug candidate in clinical trials. However, manual longitudinal image assessments are time consuming and conventional assessment routines often deliver unsatisfying study outcomes. Here, we propose a profound analysis pipeline that consists of the following coordinated steps: (1) an automated and highly precise image processing stream including voxel and surface based morphometry using latest highly detailed brain atlases such as the HCP MMP 1.0 atlas with 360 cortical ROIs; (2) a profound statistical assessment using a multiplicative model of annual percent change (APC); and (3) a multiple testing correction adopted from genome-wide association studies that is optimally suited for longitudinal neuroimaging studies. We tested this analysis pipeline with 25 Alzheimer's disease patients against 25 age-matched cognitively normal subjects with a baseline and a 1-year follow-up conventional MRI scan from the ADNI-3 study. Even in this small cohort, we were able to report 22 significant measurements after multiple testing correction from SBM (including cortical volume, area and thickness) complementing only three statistically significant volume changes (left/right hippocampus and left amygdala) found by VBM. A 1-year decrease in brain morphometry coincided with an increasing clinical disability and cognitive decline in patients measured by MMSE, CDR GLOBAL, FAQ TOTAL and NPI TOTAL scores. This work shows that highly precise image assessments, APC computation and an adequate multiple testing correction can produce a significant study outcome even for small study sizes. With this, automated MRI processing is now available and reliable for routine use and clinical trials.

Structural Alteration of Medial Temporal Lobe Subfield in the Amnestic Mild Cognitive Impairment Stage of Alzheimer’s Disease

Objective. Volume reduction and structural abnormality is the most replicated finding in neuroimaging studies of Alzheimer’s disease (AD). Amnestic mild cognitive impairment (aMCI) is the early stage of AD development. Thus, it is necessary to investigate the link between atrophy of regions of interest (ROIs) in medial temporal lobe, the variation trend of ROI densities and volumes among patients with cognitive impairment, and the distribution characteristics of ROIs in the aMCI group, Alzheimer’s disease (AD) group, and normal control (NC) group. Methods. 30 patients with aMCI, 16 patients with AD, and 30 NC are recruited; magnetic resonance imaging (MRI) brain scans are conducted. Voxel-based morphometry was employed to conduct the quantitative measurement of gray matter densities of the hippocampus, amygdala, entorhinal cortex, and mammillary body (MB). FreeSurfer was utilized to automatically segment the hippocampus into 21 subregions and the amygdala into 9 subregions. Then, their subregion volumes and total volume were calculated. Finally, the ANOVA and multiple comparisons were performed on the above-mentioned data from these three groups. Results. AD had lower GM densities than MCI, and MCI had lower GM densities than NC, but not all of the differences were statistically significant. In the comparisons of AD-aMCI-NC, AD-aMCI, and AD-NC, the hippocampus, amygdala, and entorhinal cortex showed differences in the gray matter densities ( $p<0.05$ ); the differences of mammillary body densities were not significant in the random comparison between these three groups ( $p>0.05$ ). The hippocampus densities and volumes of the subjects from the aMCI group and the AD group were bilaterally symmetric. The gray matter densities of the right side of the entorhinal cortex inside each group and the hippocampus from the NC group were higher than those of the left side ( $p<0.05$ ), and the gray matter densities of the amygdala and mammillary body were bilaterally symmetric in the three groups ( $p>0.05$ ). There were no gender differences of four ROIs in the AD, aMCI, and NC groups ( $p>0.05$ ). The volume differences of the hippocampus presubiculum-body and parasubiculum manifest no statistical significance ( $p>0.05$ ) in the random comparison between these three groups. Volume differences of the left amygdala basal nucleus, the left lateral nucleus, the left cortical amygdala transitional area, the left paravamnion nucleus, and bilateral hippocampal amygdala transition area (HATA) had statistical differences only between the AD group and the NC group ( $p<0.05$ ). Conclusion. Structural defects of medial temporal lobe subfields were revealed in the aMCI and AD groups. Decreased gray matter densities of the hippocampus, entorhinal cortex, and amygdala could distinguish patients with early stage of AD between aMCI and NC. Volume decline of the hippocampus and amygdala subfields could only distinguish AD between NC.

Shannon entropy: A novel parameter for quantifying pentagon copying performance in non-demented Parkinson's disease patients

Introduction

Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI).

Methods

We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001.

Results

PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = −0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL).

Conclusion

Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention.

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Shannon entropy (SE) quantifies in-air movements during pentagon copy test (PCT).

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SE during PCT correlated with the level of attention in PD.

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SE correlated with volume in the region involved in spatial attention.

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Visual assessment of PCT showed a ceiling effect in non-demented PD.

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SE is useful for quantitative assessment of PCT in non-demented PD.

Synaptic potentiation of anterior cingulate cortex contributes to chronic pain of Parkinson's disease

Parkinson’s disease (PD) is a multi-system neurodegenerative disorder. Patients with PD often suffer chronic pain. In the present study, we investigated motor, sensory and emotional changes in three different PD mice models. We found that 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treatment caused significant changes in all measurements. Mechanical hypersensitivity of PD model induced by MPTP peaked at 3 days and persisted for at least 14 days. Using Fos transgenic mice, we found that neurons in the anterior cingulate cortex (ACC) were activated after MPTP treatment. Inhibiting ACC by bilateral microinjection of muscimol significantly reduced mechanical hypersensitivity and anxiety-like responses. By contrast, MPTP induced motor deficit was not affected, indicating ACC activity is mostly responsible for sensory and emotional changes. We also investigated excitatory synaptic transmission and plasticity using brain slices of MPTP treated animals. While L-LTP was blocked or significantly reduced. E-LTP was not significantly affected in slices of MPTP treated animals. LTD induced by repetitive stimulation was not affected. Furthermore, we found that paired-pulse facilitation and spontaneous release of glutamate were also altered in MPTP treated animals, suggesting presynaptic enhancement of excitatory transmission in PD. Our results suggest that ACC synaptic transmission is enhanced in the animal model of PD, and cortical excitation may play important roles in PD related pain and anxiety.

Gout Is Not Just Arthritis: Abnormal Cortical Thickness and Structural Covariance Networks in Gout

Background: Hyperuricemia is the cause of gout. The antioxidant and neuroprotective effects of uric acid seem to benefit some patients with central nervous system injury. However, changes in the brain structure have not been discovered in patients with gout.

Object: Clarify the changes in cortical thickness in patients with gout and the alteration of the structural covariance networks (SCNs) based on cortical thickness.

Methods: We collected structural MRIs of 23 male gout patients and 23 age-matched healthy controls. After calculating and comparing the difference in cortical thickness between the two groups, we constructed and analyzed the cortical thickness covariance networks of the two groups, and we investigated for any changes in SCNs of gout patients.

Results: Gout patients have thicker cortices in the left postcentral, left supramarginal, right medial temporal, and right medial orbitofrontal regions; and thinner cortices were found in the left insula, left superior frontal, right pericalcarine, and right precentral regions. In SCN analysis, between-group differences in global network measures showed that gout patients have a higher global efficiency. In regional network measures, more nodes in gout patients have increased centrality. In network hub analysis, we found that the transfer of the core hub area, rather than the change in number, may be the characteristic of the gout's cortical thickness covariance network.

Conclusion: This is the first study on changes in brain cortical thickness and SCN based on graph theory in patients with gout. The present study found that, compared with healthy controls, gout patients show regional cortical thinning or thickening, and variation in the properties of the cortical thickness covariance network also changed. These alterations may be the combined effect of disease damage and physiological compensation. More research is needed to fully understand the complex underlying mechanisms of gout brain variation.

Altered brain activity in the bilateral frontal cortices and neural correlation with cognitive impairment in schizophrenia

Cognitive impairments are core aspects of schizophrenia and are highly related to poor outcomes. However, the effect of therapy on cognitive impairments remains unsatisfactory as its biological mechanisms are not fully understood. The purpose of this study was to investigate the disrupted intrinsic neural activity of the frontal areas and to further examine the functional connectivity of frontal areas related to cognitive impairments in schizophrenia. We collected brain imaging data using a 3T Siemens Prisma MRI system in 32 patients with schizophrenia and 34 age- and sex-matched healthy controls. The mean fractional amplitude of low-frequency fluctuation (mfALFF) in the frontal regions was calculated and analyzed to evaluate regional neural activity alterations in schizophrenia. Seed regions were generated from clusters showing significant changes in mfALFF in schizophrenia, and its resting-state functional connectivity (rs-FC) with other brain regions were estimated to detect possible aberrant rs-FC indicating cognitive impairments in schizophrenia. We found that mfALFF in the bilateral frontal cortices was increased in schizophrenia. mfALFF-based rs-FC revealed that decreased rs-FC between left middle frontal gyrus (MFG) and left medial superior frontal gyrus (MFSG) was associated with poor delayed memory (r = 0.566, Bonferroni-corrected p = 0.012). These findings demonstrate increased neural activity in the frontal cortices in schizophrenia. FC analysis revealed a diminished rs-FC pattern between the left MFG and left MSFG that was associated with cognitive impairments. These findings have provided deeper insight into the alterations in brain function related to specific domains of cognitive impairment and may provide evidence for precise interventions for cognitive deficits in schizophrenia.

Grey Matter Loss at Different Stages of Cognitive Decline: A Role for the Thalamus in Developing Alzheimer's Disease

Background:

Alzheimer’s disease (AD) is characterized by cognitive impairment and large loss of grey matter volume and is the most prevalent form of dementia worldwide. Mild cognitive impairment (MCI) is the stage that precedes the AD dementia stage, but individuals with MCI do not always convert to the AD dementia stage, and it remains unclear why.

Objective:

We aimed to assess grey matter loss across the brain at different stages of the clinical continuum of AD to gain a better understanding of disease progression.

Methods:

In this large-cohort study (N = 1,386) using neuroimaging data from the Alzheimer’s Disease Neuroimaging Initiative, voxel-based morphometry analyses were performed between healthy controls, individuals with early and late and AD dementia stage.

Results:

Clear patterns of grey matter loss in mostly hippocampal and temporal regions were found across clinical stages, though not yet in early MCI. In contrast, thalamic volume loss seems one of the first signs of cognitive decline already during early MCI, whereas this volume loss does not further progress from late MCI to AD dementia stage. AD dementia stage converters already show grey matter loss in hippocampal and mid-temporal areas as well as the posterior thalamus (pulvinar) and angular gyrus at baseline.

Conclusion:

This study confirms the role of temporal brain regions in AD development and suggests additional involvement of the thalamus/pulvinar and angular gyrus that may be linked to visuospatial, attentional, and memory related problems in both early MCI and AD dementia stage conversion.

Diffusion kurtosis imaging detects the time-dependent progress of pathological changes in the oral rotenone mouse model of Parkinson's disease

Clinical diagnosis of Parkinson's disease (PD) occurs typically when a substantial proportion of dopaminergic neurons in the substantia nigra (SN) already died, and the first motor symptoms appear. Therefore, tools enabling the early diagnosis of PD are essential to identify early-stage PD patients in which neuroprotective treatments could have a significant impact. Here, we test the utility and sensitivity of the diffusion kurtosis imaging (DKI) in detecting progressive microstructural changes in several brain regions of mice exposed to chronic intragastric administration of rotenone, a mouse model that mimics the spatiotemporal progression of PD-like pathology from the ENS to the SN as described by Braak's staging. Our results show that DKI, especially kurtosis, can detect the progression of pathology-associated changes throughout the CNS. Increases in mean kurtosis were first observed in the dorsal motor nucleus of the vagus (DMV) after 2 months of exposure to rotenone and before the loss of dopaminergic neurons in the SN occurred. Remarkably, we also show that limited exposure to rotenone for 2 months is enough to trigger the progression of the disease in the absence of the environmental toxin, thus suggesting that once the first pathological changes in one region appear, they can self-perpetuate and progress within the CNS. Overall, our results show that DKI can be a useful radiological marker for the early detection and monitoring of PD pathology progression in patients with the potential to improve the clinical diagnosis and the development of neuroprotective treatments.

Aberrant cerebral perfusion pattern in amnestic mild cognitive impairment and Parkinson's disease with mild cognitive impairment: a comparative arterial spin labeling study

Background

Mild cognitive impairment (MCI) has been defined as the prodromal stage of Alzheimer's disease and Parkinson's disease (PD) with dementia. We investigated the differences in regional perfusion properties among MCI subtypes and healthy control (HC) subjects by using arterial spin labeling (ASL).

Methods

Regional normalized CBF (z-CBF) and CBF-connectivity were analyzed from ASL data in 44 amnestic MCI (aMCI) patients, 42 PD-MCI patients, and 50 matched HC participants. The correlations between these significant regions and clinical performance were investigated separately using Spearman correlation analysis. Receiver operating characteristic analysis was generated to determine the differentiating ability of z-CBF values. z-CBF values in disease-related specific regions were extracted for group comparison.

Results

MCI subgroups showed overlapped impaired regions, aMCI group seemed more extensive than the PD-MCI group. PD-MCI patients had reduced z-CBF in the bilateral putamen, left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus compared to aMCI group. Correlations to executive performance and motor severity were found in PD-MCI group, and correlations were to memory performance found in aMCI group. CBF-connectivity in left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus were significantly altered. All of the significant clusters had good discriminatory ability.

Conclusions

Normalized CBF as measured by ASL revealed different patterns of perfusion between aMCI and PD-MCI, which were probably linked to distinct neural mechanisms. The present study indicates that z-CBF can provide specific perfusion information for further pathological and neuropsychological studies.

Cellular and synaptic mechanisms for Parkinson's disease-related chronic pain

Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease. Chronic pain is experienced by the vast majority of patients living with Parkinson’s disease. The degeneration of dopaminergic neuron acts as the essential mechanism of Parkinson’s disease in the midbrain dopaminergic pathway. The impairment of dopaminergic neurons leads to dysfunctions of the nociceptive system. Key cortical areas, such as the anterior cingulate cortex (ACC) and insular cortex (IC) that receive the dopaminergic projections are involved in pain transmission. Dopamine changes synaptic transmission via several pathway, for example the D2-adenly cyclase (AC)-cyclic AMP (cAMP)-protein kinase A (PKA) pathway and D1-G protein-coupled receptor kinase 2 (GRK2)-fragile X mental retardation protein (FMRP) pathway. The management of Parkinson’s disease-related pain implicates maintenance of stable level of dopaminergic drugs and analgesics, however a more selective drug targeting at key molecules in Parkinson’s disease-related pain remains to be investigated.

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning

Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.

An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease

OBJECTIVE

To identify Parkinson's disease with mild cognitive impairment (PD-MCI) through surface-based morphometry (SBM) based machine learning model.

METHODS

93 patients with parkinson's disease (35 PD with normal cognition, 58 PD-MCI) were examined, obtaining 276 SBM variables per subject. 20 healthy control subjects were used as the reference. After extracting features with statistically significance, support vector machine (SVM) model with grid search method was applied to identify patients with PD-MCI. Accuracy, matthews correlation coefficient (MCC), receiver operating characteristic curve (ROC), precision-recall curve (PR), AUC-ROC, AUC-PR and leave-one-out cross validation (LOOCV) strategy were employed for model evaluation.

RESULTS

PD-MCI is characterized by widespread structural abnormality. SVM model with SBM features achieved an accuracy of 80.00% and area under the ROC of 0.86 for identifying PD-MCI. MCC, AUC-PR, and LOOCV classification accuracy were 0.56, 0.89, and 78.08%, respectively.

CONCLUSION

Automatic identification of PD-MCI could be realized by SBM-based machine learning model.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

Cortical thickness in Parkinson disease: A coordinate-based meta-analysis

BACKGROUND

A growing number of studies have used surface-based morphometry (SBM) analyses to investigate gray matter cortical thickness (CTh) abnormalities in Parkinson disease (PD). However, the results across studies are inconsistent and have not been systematically reviewed. A clear picture of CTh alterations in PD remains lacked. Coordinate-based meta-analysis (CBMA) is a powerful tool to quantitatively integrate the results of individual voxel-based neuroimaging studies to identify the functional or structural neural substrates of particular neuropsychiatric disorders. Recently, CBMA has been updated for integrating SBM studies.

METHODS

The online databases PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed were comprehensively searched without language limitations from the database inception to February 2, 2020. We will include all SBM studies that compared regional CTh between patients with idiopathic PD and healthy control subjects at the whole-cortex level using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI). In addition to the main CBMA, we will conduct several supplementary analyses to test the robustness of the results, such as jackknife analyses, subgroup analyses, heterogeneity analyses, publication bias analyses, and meta-regression analyses.

RESULTS

This CBMA will offer the latest evidence of CTh alterations in PD.

CONCLUSIONS

Consistent and robust evidence of CTh alterations will feature brain morphometry of PD and may facilitate biomarker development.

PROSPERO REGISTRATION NUMBER

CRD42020148775.

Brain volumes and dual-task performance correlates among individuals with cognitive impairment: a retrospective analysis

Cognitive impairment (CI) is a prevalent condition characterized by loss of brain volume and changes in cognition, motor function, and dual-tasking ability. To examine associations between brain volumes, dual-task performance, and gait and balance in those with CI to elucidate the mechanisms underlying loss of function. We performed a retrospective analysis of medical records of patients with CI and compared brain volumes, dual-task performance, and measures of gait and balance. Greater cognitive and combined dual-task effects (DTE) are associated with smaller brain volumes. In contrast, motor DTE is not associated with distinct pattern of brain volumes. As brain volumes decrease, dual-task performance becomes more motor prioritized. Cognitive DTE is more strongly associated with decreased performance on measures of gait and balance than motor DTE. Decreased gait and balance performance are also associated with increased motor task prioritization. Cognitive DTE appears to be more strongly associated with decreased automaticity and gait and balance ability than motor DTE and should be utilized as a clinical and research outcome measure in this population. The increased motor task prioritization associated with decreased brain volume and function indicates a potential for accommodative strategies to maximize function in those with CI. Counterintuitive correlations between motor brain volumes and motor DTE in our study suggest a complicated interaction between brain pathology and function.

Brain structure changes in nondemented seniors after six-month dance-exercise intervention

OBJECTIVES

To evaluate effects of a six-month intensive dance-exercise intervention (DI) on cognition and brain structure in a mixed group of healthy seniors and people with mild cognitive impairment.

METHODS

Subjects (aged ˃ 60 years with no dementia or depression) were randomly assigned to either a DI group or a life as usual (LAU) group. Detailed neuropsychological testing, measures of physical fitness and brain MRI encompassing T1 structural and diffusion tensor imaging (DTI) were performed at baseline and after 6 months. We assessed changes in cortical thickness and DTI parameters derived from tract-based spatial statistics.

RESULTS

Altogether 62 individuals (n = 31 in the DI group) completed the protocol. The groups were matched for their demographic and clinical variables. After 6 months, we found significant cortical thickening in the right inferior temporal, fusiform and lateral occipital regions in the dancers compared to controls. Significant increases of radial and mean diffusivity were observed in various white matter tracts in the dancers; however, no differences were observed between the DI and LAU groups. The DI group as compared to the LAU group showed subtle improvements in executive functions.

CONCLUSIONS

We observed DI-induced improvement in executive functions and increases of cortical thickness in the lateral occipitotemporal cortex which is engaged in action observation, visuomotor integration and action imitation, that is activities that are all important for motor learning and executing skilled movements.

Brain MRI Reveals Ascending Atrophy in Parkinson's Disease Across Severity

Models which assess the progression of Lewy pathology in Parkinson's disease have proposed ascending spread in a caudal-rostral pattern. In-vivo human evidence for this theory is limited, in part because there are no biomarkers that allow for direct assessment of Lewy pathology. Here, we measured neurodegeneration via MRI, an outcome which may serve as a proxy for a more direct assessment of ascending models using a combination of (1) MRI-based measures of gray matter density and (2) regions of interest (ROIs) corresponding to cortical and subcortical loci implicated in past MRI and stereological studies of Parkinson's disease. Gray matter density was measured using brain MRI voxel-based morphometry from three cohorts: (1) early Parkinson's disease, (2) more advanced Parkinson's disease and (3) healthy controls. Early Parkinson's disease patients (N = 228, mean age = 61.9 years, mean disease duration = 0.6 years) were newly diagnosed by the Parkinson's Progression Markers Initiative (PPMI). Advanced Parkinson's disease patients (N = 136, mean age = 63.5 years, mean disease duration = 8.0 years) were collected retrospectively from a local cohort undergoing evaluation for functional neurosurgery. Control subjects (N = 103, mean age = 60.2 years) were from PPMI. Comparative analyses focused on gray matter regions ranging from deep gray subcortical structures to the neocortex. ROIs were defined with existing probabilistic cytoarchitectonic brain maps. For subcortical regions of the basal forebrain, amygdala, and entorhinal cortex, advanced Parkinson's disease patients had significantly lower gray matter density when compared to both early Parkinson's disease and healthy controls. No differences were seen in neocortical regions that are "higher" in any proposed ascending pattern. Across early and advanced Parkinson's disease, gray matter density from nearly all subcortical regions significantly decreased with disease duration; no neocortical regions showed this effect. These results demonstrate that atrophy in advanced Parkinson's patients compared to early patients and healthy controls is largely confined to subcortical gray matter structures. The degree of atrophy in subcortical brain regions was linked to overall disease duration, suggesting an organized pattern of atrophy across severity.

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

PURPOSE OF REVIEW

We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results.

RECENT FINDINGS

The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion.

Regional gray matter reductions associated with mild cognitive impairment in Parkinson's disease: A meta-analysis of voxel-based morphometry studies

Mild cognitive impairment (MCI) is inconclusively associated with regional gray matter (GM) abnormalities in Parkinson's disease (PD). We aimed to quantitatively evaluate whole-brain voxel-based morphometry (VBM) studies that have investigated brain GM changes in PD patients with MCI (PD-MCI). Seed-based d Mapping, a well-validated coordinate-based meta-analytic approach, was utilized. We included 20 VBM studies that reported 22 datasets containing 504 patients with PD-MCI and 554 PD patients without MCI (PD-NCI). The most reliable finding identified in this meta-analysis was that patients with PD-MCI exhibited greater GM atrophy in the left anterior insula than those with PD-NCI. Our findings further suggest that several moderators (age, gender, educational level, disease stage, severity of motor disability, and the severity of cognitive impairments) in PD-MCI individuals, as well as scanner field-strength, may drive heterogeneous GM changes across studies. GM abnormalities in the anterior insula, an important cognitive hub involved in switching between neural networks, contribute to understanding the neural substrates of MCI in PD, which may serve as a biomarker of PD-MCI.

Cingulate cortex in Parkinson's disease

Once a diagnosis of Parkinson's disease (PD) has been made, even in its earliest prodromal form of subjective memory impairment, cognitive impairment has begun and involves anterior cingulate cortex (ACC). While the Braak staging scheme showed mid- to later-stage PD progression from cingulate allocortex adjacent to the corpus callosum and progressing into its neocortical moieties, the last decade has produced substantial information on the role of cingulate cortex in multiple symptoms, not just global measures of cognition. Voxel-based morphometry has been used in many studies of mild cognitive impairment (MCI) in PD to show reduced thickness in ACC and posterior cingulate cortex (PCC). Regional cerebral blood flow is altered in association with verbal IQ in all the PCC and anterior midcingulate cortex and executive impairments in ACC. Diffusion tensor imaging shows reduced fractional anisotropy throughout the entire cingulum bundle. Amnestic MCI is associated with reduced dopamine-2 receptor binding in ACC and, even in cognitively normal PD cases, dopaminergic pathways in ACC are impaired early in association with executive and language functions. The cholinergic system also has substantial changes in nicotinic and muscarinic receptor binding, and therapy with donepezil improves Mini-Mental State Exam scores and metabolism in pACC and dPCC. Cingulate cortex is also engaged in two critical symptoms: apathy and visual hallucinations. Finally, one can be optimistic that cingulate cortex will play an important role in developing new biomarkers of early PD. These methods have already been shown to be useful in cingulate cortex and include magnetic resonance spectroscopy, next-generation gene expression, and the new α-synuclein proximity ligation assay that specifically recognizes α-synuclein oligomers. Thus the future is bright for developing multivariate, multimodal biomarkers that include cingulate cortex.

Novel Treatment Opportunities Against Cognitive Impairment in Parkinson's Disease with an Emphasis on Diabetes-Related Pathways

Cognitive impairment is highly prevalent in patients with Parkinson's disease (PD) and causes adverse health outcomes. Novel procognitive therapies are needed to address this unmet need. It is now established that there is an increased risk of dementia in patients with type 2 diabetes mellitus (T2DM) and, moreover, T2DM and PD may have common underlying biological mechanisms. As such, T2DM medications are emerging as potential therapies in the context of PD dementia (PDD). In this review, we provide an update on pathophysiological mechanisms underlying cognitive impairments and PDD, focusing on diabetes-related pathways. Finally, we have conducted a review of ongoing clinical trials in PD patients with dementia, highlighting the multiple pharmacological mechanisms that are targeted to achieve cognitive enhancement.