Magnetic resonance imaging: a biomarker for cognitive impairment in Parkinson's disease?

Global biomarker trends in Parkinson's disease research: A bibliometric analysis

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

Global biomarker trends in Parkinson's disease research: A bibliometric analysis

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.

Differentiation of the motor cost associated with cognitive tasks in Parkinson's disease: a dual-task study

Parkinson's disease is a neurodegenerative condition associated with motor and cognitive impairments. While the execution of dual cognitive-motor tasks imposes a cost on gait velocity, it has been barely determined if the gait deterioration depends on the specific cognitive domain involved in the dual-task. Twenty-four subjects (twelve patients with Parkinson's disease and twelve healthy subjects) carried out a single task (gait alone) and several dual tasks where the concurrent second task was the Trail Making Test (Part A) and the six tasks of the Frontal Assessment Battery. Gait variables were measured by accelerometry via smartphone. Data analysis included analysis of variance and exploratory factorial analysis. Both groups showed a similar gait performance, except for velocity, where patients exhibited a bradykinetic profile. The dual-task during the Trail Making Test showed the highest motor cost. Frontal Assessment Battery's tasks as conceptualization, mental flexibility, and motor programming showed a higher motor cost than the other tasks (sensibility to interference, inhibitory control, and environmental autonomy). The factorial analysis applied to the motor costs confirmed two profiles, grouping those related to the dorsolateral prefrontal cortex (mental flexibility and motor programming tasks) in an independent factor. Among cognitive functions, attention is critical for gait control in Parkinson's disease and healthy elderly people. The interference posed by several executive operations suggests a specific competition in prefrontal regions that support dual tasks. Moreover, the higher cost for Parkinson's disease patients emphasizes the cognitive decline and compensatory cognitive strategy for gait control related to attention and executive functions.

Neuroimaging approaches to cognition in Parkinson's disease

While direct visualization of Lewy body accumulation within the brain is not yet possible in living Parkinson's disease patients, brain imaging studies offer insights into how the buildup of Lewy body pathology impacts different regions of the brain. Unlike biological biomarkers and purely behavioral research, these brain imaging studies therefore offer a unique opportunity to relate brain localization to cognitive function and dysfunction in living patients. Magnetic resonance imaging studies can reveal physical changes in brain structure as they relate to different cognitive domains and task specific impairments. Functional imaging studies use a combination of task and resting state magnetic resonance imaging, as well as positron emission tomography and single photon emission computed tomography, and can be used to determine changes in blood flow, neuronal activation and neurochemical changes in the brain associated with PD cognition and cognitive impairments. Other unique advantages to brain imaging studies are the ability to monitor changes in brain structure and function longitudinally as patients progress and the ability to study changes in brain function when patients are exposed to different pharmacological manipulations. This is particularly true when assessing the effects of dopaminergic replacement therapy on cognitive function in Parkinson's disease patients. Together, this chapter will describe imaging studies that have helped identify structural and functional brain changes associated with cognition, cognitive impairment, and dementia in Parkinson's disease.

OUP accepted manuscript

The progression of cognitive decline is heterogeneous in the three most common idiopathic parkinsonian diseases: Parkinson disease, multiple system atrophy and progressive supranuclear palsy. The causes for this heterogeneity are not fully understood, and there are no validated biomarkers that can accurately identify patients who will develop dementia and when. In this population-based, prospective study, comprehensive neuropsychological testing was performed repeatedly in new-onset, idiopathic parkinsonism. Dementia was diagnosed until 10 years and participants (N = 210) were deeply phenotyped by multimodal clinical, biochemical, genetic and brain imaging measures. At baseline, before the start of dopaminergic treatment, mild cognitive impairment was prevalent in 43.4% of the patients with Parkinson disease, 23.1% of the patients with multiple system atrophy and 77.8% of the patients with progressive supranuclear palsy. Longitudinally, all three diseases had a higher incidence of cognitive decline compared with healthy controls, but the types and severity of cognitive dysfunctions differed. In Parkinson disease, psychomotor speed and attention showed signs of improvement after dopaminergic treatment, while no such improvement was seen in other diseases. The 10-year cumulative probability of dementia was 54% in Parkinson disease and 71% in progressive supranuclear palsy, while there were no cases of dementia in multiple system atrophy. An easy-to-use, multivariable model that predicts the risk of dementia in Parkinson disease within 10 years with high accuracy (area under the curve: 0.86, P < 0.001) was developed. The optimized model adds CSF biomarkers to four easily measurable clinical features at baseline (mild cognitive impairment, olfactory function, motor disease severity and age). The model demonstrates a highly variable but predictable risk of dementia in Parkinson disease, e.g. a 9% risk within 10 years in a patient with normal cognition and CSF amyloid-β₄₂ in the highest tertile, compared with an 85% risk in a patient with mild cognitive impairment and CSF amyloid-β₄₂ in the lowest tertile. Only small or no associations with cognitive decline were found for factors that could be easily modifiable (such as thyroid dysfunction). Risk factors for cognitive decline in multiple system atrophy and progressive supranuclear palsy included signs of systemic inflammation and eye movement abnormalities. The predictive model has high accuracy in Parkinson disease and might be used for the selection of patients into clinical trials or as an aid to improve the prevention of dementia.

Experimental Models of Cognitive Impairment for Use in Parkinson's Disease Research: The Distance Between Reality and Ideal

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Cognitive impairment is one of the key non-motor symptoms of PD, affecting both mortality and quality of life. However, there are few experimental studies on the pathology and treatments of PD with mild cognitive impairment (PD-MCI) and PD dementia (PDD) due to the lack of representative models. To identify new strategies for developing representative models, we systematically summarized previous studies on PD-MCI and PDD and compared differences between existing models and diseases. Our initial search identified 5432 articles, of which 738 were duplicates. A total of 227 articles met our inclusion criteria and were included in the analysis. Models fell into three categories based on model design: neurotoxin-induced, transgenic, and combined. Although the neurotoxin-induced experimental model was the most common type that was used during every time period, transgenic and combined experimental models have gained significant recent attention. Unfortunately, there remains a big gap between ideal and actual experimental models. While each model has its own disadvantages, there have been tremendous advances in the development of PD models of cognitive impairment, and almost every model can verify a hypothesis about PD-MCI or PDD. Finally, our proposed strategies for developing novel models are as follows: a set of plans that integrate symptoms, biochemistry, neuroimaging, and other objective indicators to judge and identify that the novel model plays a key role in new strategies for developing representative models; novel models should simulate different clinical features of PD-MCI or PDD; inducible α-Syn overexpression and SH-SY5Y-A53T cellular models are good candidate models of PD-MCI or PDD.

Neurofilament Levels Are Reflecting the Loss of Presynaptic Dopamine Receptors in Movement Disorders

Aims: Neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH) are biomarkers for neuroaxonal damage. We assessed whether NfL and other biomarker levels in the CSF are correlated to the loss of presynaptic dopamine transporters in neurons as detected with dopamine transporter SPECT (DaTscan). Methods: We retrospectively identified 47 patients (17 Alzheimer's dementia, 10 idiopathic Parkinson's disease, 7 Lewy body dementia, 13 progressive supranuclear palsy or corticobasal degeneration) who received a DaTscan and a lumbar puncture. DaTscan imaging was performed according to current guidelines, and z-scores indicating the decrease in uptake were software based calculated for the nucleus caudatus and putamen. The CSF biomarkers progranulin, total-tau, alpha-synuclein, NfL, and pNfH were correlated with the z-scores. Results: DaTscan results in AD patients did not correlate with any biomarker. Subsuming every movement disorder with nigrostriatal neurodegeneration resulted in a strong correlation between putamen/nucleus caudatus and NfL (nucleus caudatus right p < 0.01, putamen right p < 0.05, left p < 0.05) and between pNfH and putamen (right p < 0.05; left p < 0.042). Subdividing in disease cohorts did not reveal significant correlations. Progranulin, alpha-synuclein, and total-tau did not correlate with DaTscan results. Conclusion: We show a strong correlation of NfL and pNfH with pathological changes in presynaptic dopamine transporter density in the putamen concomitant to nigrostriatal degeneration. This correlation might explain the reported correlation of impaired motor functions in PD and NfL as seen before, despite the pathological heterogeneity of these diseases.

A Comparison of Serial Position Effects in Patients with Mild Cognitive Impairment due to Parkinson's Disease or to Alzheimer's Disease

OBJECTIVE

The first (primacy region) and last (recency region) items of a word list are generally better memorized than items from the middle region. The recency effect depends on short-term memory (STM) and the primacy effect on long-term memory (LTM), where verbal information is transferred from STM into LTM by maintenance rehearsal. We compared the serial position effects (SPE) between patients with mild cognitive impairment (MCI) due to Parkinson's disease (PD), i.e., PD-MCI, and patients with MCI due to Alzheimer's disease (AD-MCI), and evaluated the influence of SPE and frontostriatal deficits on verbal memory recall.

METHODS

Four similar groups of subjects participated in the study: 26 PD-MCI patients, 26 cognitively normal patients with PD (PD-CN), 26 AD-MCI patients, and 26 normal controls (NC). Verbal episodic memory, verbal span, attentional capacity, executive functions, and verbal working memory performance were assessed. Measures for primacy and recency regions were defined at the first trial of a 16-items word list. Hierarchical regression models were used to investigate the contribution of frontostriatal deficits beyond SPE on verbal memory recall performance ("long-delay free recall") in PD and AD patients.

RESULTS

Primacy effects were significantly diminished in both PD-MCI and AD-MCI patients relative to NC and PD-CN (all p < 0.01). Compared to PD-MCI patients, AD-MCI patients exhibited significantly worse "delayed-recall 'savings'." Reduced primacy effect was predictive for decreased recall performance in PD and AD. The conducted hierarchical regression model revealed that in PD, but not in AD patients, performance of attention and executive function significantly increased the prediction of free recalled words.

CONCLUSIONS

Reduced recall performance is likely due to impaired transition of newly learned material from STM into LTM in AD and in PD. Whereas AD-MCI patients suffer from a storage deficit, the similarly reduced recall performance found in patients with PD-MCI may additionally be related to deficient attentional and executive capacity.

Cognitive impairment in Parkinson's disease: A clinical and pathophysiological overview

Cognitive dysfunction in Parkinson's disease (PD) has received increasing attention, and, together with other non-motor symptoms, exert a significant functional impact in the daily lives of patients. This article aims to compile and briefly summarize selected published data about clinical features, cognitive evaluation, biomarkers, and pathophysiology of PD-related dementia (PDD). The literature search included articles indexed in the MEDLINE/PubMed database, published in English, over the last two decades. Despite significant progress on clinical criteria and cohort studies for PD-mild cognitive impairment (PD-MCI) and PDD, there are still knowledge gaps about its exact molecular and pathological basis. Here we overview the scientific literature on the role of functional circuits, neurotransmitter systems (monoaminergic and cholinergic), basal forebrain, and brainstem nuclei dysfunction in PD-MCI. Correlations between neuroimaging and cerebrospinal fluid (CSF) biomarkers, clinical outcomes, and pathological results are described to aid in uncovering the neurodegeneration pattern in PD-MCI and PDD.

Effects of MP2RAGE B1+ sensitivity on inter-site T1 reproducibility and hippocampal morphometry at 7T

Most neuroanatomical studies are based on T₁-weighted MR images, whose intensity profiles are not solely determined by the tissue's longitudinal relaxation times (T₁), but also affected by varying non-T₁ contributions, hampering data reproducibility. In contrast, quantitative imaging using the MP2RAGE sequence, for example, allows direct characterization of the brain based on the tissue property of interest. Combined with 7 Tesla (7T) MRI, this offers unique opportunities to obtain robust high-resolution brain data characterized by a high reproducibility, sensitivity and specificity. However, specific MP2RAGE parameter choices - e.g., to emphasize intracortical myelin-dependent contrast variations - can substantially impact image quality and cortical analyses through remnants of B₁⁺-related intensity variations, as illustrated in our previous work. To follow up on this: we (1) validate this protocol effect using a dataset acquired with a particularly B₁⁺ insensitive set of MP2RAGE parameters combined with parallel transmission excitation; and (2) extend our analyses to evaluate the effects on hippocampal morphometry. The latter remained unexplored initially, but can provide important insights related to generalizability and reproducibility of neurodegenerative research using 7T MRI. We confirm that B₁⁺ inhomogeneities have a considerably variable effect on cortical T₁ estimates, as well as on hippocampal morphometry depending on the MP2RAGE setup. While T₁ differed substantially across datasets initially, we show the inter-site T₁ comparability improves after correcting for the spatially varying B₁⁺ field using a separately acquired Sa2RAGE B₁⁺ map. Finally, removal of B₁⁺ residuals affects hippocampal volumetry and boundary definitions, particularly near structures characterized by strong intensity changes (e.g. cerebral spinal fluid). Taken together, we show that the choice of MP2RAGE parameters can impact T₁ comparability across sites and present evidence that hippocampal segmentation results are modulated by B₁⁺ inhomogeneities. This calls for careful (1) consideration of sequence parameters when setting acquisition protocols, as well as (2) acquisition of a B₁⁺ map to correct MP2RAGE data for potential B₁⁺ variations to allow comparison across datasets.

NfL as a biomarker for neurodegeneration and survival in Parkinson disease

OBJECTIVE

To determine whether neurofilament light chain protein in CSF (cNfL), a sensitive biomarker of neuroaxonal damage, reflects disease severity or can predict survival in Parkinson disease (PD).

METHODS

We investigated whether disease severity, phenotype, or survival in patients with new-onset PD correlates with cNfL concentrations around the time of diagnosis in the population-based New Parkinsonism in Umeå (NYPUM) study cohort (n = 99). A second, larger new-onset PD cohort (n = 194) was used for independent validation. Association of brain pathology with the cNfL concentration was examined with striatal dopamine transporter imaging and repeated diffusion tensor imaging at baseline and 1 and 3 years.

RESULTS

Higher cNfL in the early phase of PD was associated with greater severity of all cardinal motor symptoms except tremor in both cohorts and with shorter survival and impaired olfaction. cNfL concentrations above the median of 903 ng/L conferred an overall 5.8 times increased hazard of death during follow-up. After adjustment for age and sex, higher cNfL correlated with striatal dopamine transporter uptake deficits and lower fractional anisotropy in diffusion tensor imaging of several axonal tracts.

CONCLUSIONS

cNfL shows usefulness as a biomarker of disease severity and to predict survival in PD. The present results indicate that the cNfL concentration reflects the intensity of the neurodegenerative process, which could be important in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with PD, cNfL concentrations are associated with more severe disease and shorter survival.

Parietal Perfusion Alterations in Parkinson's Disease Patients Without Dementia

Fronto-parietal regions are involved in cognitive processes that are commonly affected in Parkinson's disease (PD). The aims of this study were to investigate cerebral blood flow (CBF) and gray matter (GM) volume within the regions belonging to the fronto-parietal circuit in people with PD (pwPD) without dementia, and to assess their association with cognitive performance. Twenty-seven pwPD without dementia (mean [SD] age = 67.4 [8.1] years, 20 males, mean [SD] Montreal Cognitive Assessment, MoCA score = 24.2 [2.9], median [IQR] Hoehn and Yahr scale = 1.5 [1–2]) and twenty-six age- and sex-matched healthy controls (HC) were scanned with arterial spin labeling (ASL) and T1-weighted magnetic resonance imaging (MRI) sequences to investigate CBF and GM volume, respectively. The cognitive performance of the enrolled pwPD was assessed with MoCA, Trail Making Test (TMT, part A, B, B-A), phonemic fluency and semantic fluency tests. The scores were adjusted for age and education. After standard preprocessing, CBF differences between pwPD and HC were tested with a voxel-wise approach. Voxel-based morphometry was used to compare pwPD and HC in terms of GM volume. Both voxel-wise comparisons between pwPD and HC were restricted to regions of the fronto-parietal circuit. The following additional voxel-wise analyses were performed within regions showing either perfusion or GM volume alterations: (1) correlation with neuropsychological test scores; (2) subgroup comparison after median split on each neuropsychological test score. Family-wise error-corrected (FWE) p-values lower than 0.05 were considered significant. Significant hypoperfusion was identified in the left inferior parietal lobule (IPL, p_peak = 0.037) and in the bilateral superior parietal lobule (SPL, left hemisphere: p_peak = 0.037; right hemisphere: p_peak = 0.049) of pwPD when compared to HC. No significant GM atrophy was observed. Local hypoperfusion did not correlate with any neuropsychological test scores. However, significantly lower CBF was observed in the left SPL and IPL of the pwPD subgroup who performed poorer on TMT part A in comparison with the pwPD subgroup that performed better. Perfusion alterations may occur in parietal regions of pwPD without dementia, and may be associated with lower visuomotor skills. Parietal CBF may be considered as a suitable early biomarker for longitudinal studies investigating cognitive decline in PD.

Predictors of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis of prospective cohort studies

INTRODUCTION

Cognitive impairment is a debilitating manifestation in Parkinson's disease (PD). We sought to investigate predictors of PD-CI (PD with cognitive impairment).

METHODS

We systematically searched PubMed and Cochrane Library for prospective cohort studies and pooled estimates via random-effects models. Primary analyses for all types of cognitive impairments and subgroup analyses by separate outcomes were conducted.

RESULTS

A total of 28,009 studies were identified, of which 57 studies with 31 factors were included in the meta-analysis. In the primary analysis, 13 factors were associated with PD-CI, comprising advanced age [relative risk (RR) = 1.07, 95% confidence interval (CI) = 1.03-1.12], age at onset (RR = 4.43, 95% CI = 1.87-10.54), postural-instability-gait disorder (RR = 3.76, 95% CI = 1.36-10.40), higher Hoehn and Yahr stage (RR = 1.83, 95% CI = 1.35-2.47), higher UPDRS III score (RR = 1.04, 95% CI = 1.01-1.08), rapid eye movement sleep behavior disorder (RR = 3.72, 95% CI = 1.20-11.54), hallucinations (RR = 3.09, 95% CI = 1.61-5.93), orthostatic hypotension (RR = 2.98, 95% CI = 1.41-6.28), anxiety (RR = 2.59, 95% CI = 1.18-5.68), APOE ε2 (RR = 6.47, 95% CI = 1.29-32.53), APOE ε4 (RR = 3.04, 95% CI = 1.88-4.91), electroencephalogram theta power > median (RR = 2.93, 95% CI = 1.61-5.33), and alpha power < median (RR = 1.77, 95% CI = 1.07-2.92). In the subgroup analysis, MAPT H1/H1 genotype increased the risk of dementia in PD. Sixty-four studies were included in the systematic review, of which 12 factors were additionally correlated with PD-CI using single studies.

CONCLUSIONS

Advanced age, genetic variation in APOE and MAPT, gait disturbance, motor assessments, non-motor symptoms, and electroencephalogram may be promising predictors for PD-CI.

Evaluation of single molecule array digital immunoassay technology to quantitate neurofilament light chain

Aim: Globally, neurodegeneration accounts for significant morbidity and mortality among the elderly. Millions of people are afflicted with neurodegenerative diseases, with the most notable cases attributed to Alzheimer's, Huntington's, amyotrophic lateral sclerosis and Parkinson's diseases. Sensitive assays that can detect proteopathic anomalies indicative of early neurodegeneration have remained elusive. Therefore, there is an urgent need for sensitive diagnostic and prognostic biomarker assays that can guide the therapeutic regimen in the clinic. Materials & methods: Single molecule array digital immunoassay platform has sensitivity about 1000-fold higher than traditional ligand binding assays. Consequently, we are now beginning to implement ultrasensitive techniques in bioanalysis. Conclusion: In the current study, we evaluated single molecule array technology and report specifications to quantitate neurofilament light chain, a bona-fide biomarker for neurodegeneration. Preliminary neurofilament light screening results from 100 human geriatric cerebrospinal fluid samples displayed huge biological variation and warrants further investigation.

Genome-wide association study of white matter hyperintensity volume in elderly persons without dementia

•

We conducted a GWAS to identify genetic loci linked to WMHV in non-demented elders.

•

Rs7220676 near HS3ST3A1 and MIR548H3 genes was significantly associated with WMHV.

•

Rs7220676 was also correlated with rates of cognitive decline.

Background

White matter hyperintensity has been correlated with cognitive disorders and its genetic predictors remain unclear. Here we conducted a genome-wide association study to identify novel genetic determinants that were correlated with white matter hyperintensity volume (WMHV) among non-demented elders.

Methods

Three hundred and fifty non-Hispanic Caucasian subjects aged 55–80 years were included from the Alzheimer's Disease Neuroimaging Initiative cohort. Associations of WMHV with genetic polymorphisms were explored using multiple linear regression under an additive genetic model. Further studies were conducted to explore the influence of genetic variants on cognition-related phenotypes.

Results

Rs7220676 near HS3ST3A1 and MIR548H3 genes was associated with WMHV levels at genome-wide significance (P = 2.96 × 10⁻⁸). Single nucleotide polymorphisms comprising rs9675262 (near HS3ST3A1 and MIR548H3 genes, P = 1.15 × 10⁻⁷), rs9820240 (in DCLK3 gene, P = 2.23 × 10⁻⁷), rs10916409 (near ISCA1P2 gene, P = 4.55 × 10⁻⁶), and rs540422 (in PICALM gene, P = 9.68 × 10⁻⁶) were identified as suggestive loci linked to WMHV levels. The minor allele of rs7220676 (C) showed association with lower log (WMHV) in a dose-dependent manner. Besides, rs7220676 was correlated with rates of cognitive decline assessed by Mini-mental State Examination and memory scores.

Conclusions

A novel locus near HS3ST3A1 and MIR548H3 genes was associated with WMHV levels and it may be involved in neurodegenerative diseases.

Imaging Mild Cognitive Impairment and Dementia in Parkinson's Disease

Cognitive dysfunction is a significant non-motor feature of Parkinson's disease, with the risk of dementia increasing with prolonged disease duration. Multiple cognitive domains are affected, and the pathophysiology cannot be explained by dopaminergic loss alone. Sophisticated neuroimaging techniques can detect the nature and extent of extra-nigral involvement by targeting neurotransmitters, abnormal protein aggregates and tissue metabolism. This review identifies the functional and anatomical imaging characteristics that predict cognitive impairment in PD, the limitations that challenge this process, and the avenues of potential research.

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.

Genome-wide association study of hippocampal atrophy rate in non-demented elders

Hippocampal atrophy rate has been correlated with cognitive decline and its genetic modifiers are still unclear. Here we firstly performed a genome-wide association study (GWAS) to identify genetic loci that regulate hippocampal atrophy rate. Six hundred and two non-Hispanic Caucasian elders without dementia were included from the Alzheimer's Disease Neuroimaging Initiative cohort. Three single nucleotide polymorphisms (SNPs) (rs4420638, rs56131196, rs157582) in the TOMM40-APOC1 region were associated with hippocampal atrophy rate at genome-wide significance and 3 additional SNPs (in TOMM40 and near MIR302F gene) reached a suggestive level of significance. Strong linkage disequilibrium between rs4420638 and rs56131196 was found. The minor allele of rs4420638 (G) and the minor allele of rs157582 (T) showed associations with lower Mini-mental State Examination score, higher Alzheimer Disease Assessment Scale-cognitive subscale 11 score and smaller entorhinal volume using both baseline and longitudinal measurements, as well as with accelerated cognitive decline. Moreover, rs56131196 (P = 1.96 × 10-454) and rs157582 (P = 9.70 × 10-434) were risk loci for Alzheimer's disease. Collectively, rs4420638, rs56131196 and rs157582 were found to be associated with hippocampal atrophy rate. Besides, they were also identified as genetic loci for cognitive decline.

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.

Structural brain signature of cognitive decline in Parkinson's disease: DTI-based evidence from the LANDSCAPE study

Background:

The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia.

Methods:

Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures.

Results:

Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score.

Conclusions:

The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.

Electrocortical networks in Parkinson's disease patients with Mild Cognitive Impairment. The PaCoS study

INTRODUCTION

Parkinson's Disease (PD) is frequently associated with cognitive dysfunction ranging from Mild Cognitive Impairment (PD-MCI) to dementia. Few electrophysiological studies are available evaluating potential pathogenetic mechanisms linked to cognitive impairment in PD since its initial phases. The objective of the study is to analyze electrocortical networks related with cognitive decline in PD-MCI for identifying possible early electrophysiological markers of cognitive impairment in PD.

METHODS

From the PaCoS (Parkinson's disease Cognitive impairment Study) cohort, a sample of 102 subjects including 46 PD-MCI and 56 PD with normal cognition (PD-NC) was selected based on the presence of a neuropsychological assessment and at least one EEG recording. EEG signal epochs were analysed using Independent Component Analysis LORETA and spectral analysis by computing the Power Spectral Density (PSD) of site-specific signal epochs.

RESULTS

LORETA analysis revealed significant differences in PD-MCI patients compared to PD-NC, with a decreased network involving alpha activity over the occipital lobe, an increased network involving beta activity over the frontal lobe associated with a reduction over the parietal lobe, an increased network involving theta and delta activity over the frontal lobe and a reduction of networks involving theta and delta activity in the parietal lobe. Quantitative EEG analysis showed a significant decrease of alpha PSD over the occipital regions and an increase of delta PSD over the left temporal region in PD-MCI as compared to PD-NC.

CONCLUSION

Electrocortical abnormalities detected in PD-MCI patients may represent the instrumental counterpart of early cognitive decline in PD.

The Full Spectrum of Alzheimer's Disease Is Rooted in Metabolic Derangements That Drive Type 3 Diabetes

The standard practice in neuropathology is to diagnose Alzheimer's disease (AD) based on the distribution and abundance of neurofibrillary tangles and Aβ deposits. However, other significant abnormalities including neuroinflammation, gliosis, white matter degeneration, non-Aβ microvascular disease, and insulin-related metabolic dysfunction require further study to understand how they could be targeted to more effectively remediate AD. This review addresses non-Aβ and non-pTau AD-associated pathologies, highlighting their major features, roles in neurodegeneration, and etiopathic links to deficits in brain insulin and insulin-like growth factor signaling and cognitive impairment. The discussion delineates why AD with its most characteristic clinical and pathological phenotypic profiles should be regarded as a brain form of diabetes, i.e., type 3 diabetes, and entertains the hypothesis that type 3 diabetes is just one of the categories of insulin resistance diseases that can occur independently or overlap with one or more of the others, including type 2 diabetes, metabolic syndrome, and nonalcoholic fatty liver disease.

Structural and Functional Magnetic Resonance Imaging of Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer's disease (AD). Although diagnosis may be challenging, there is increasing evidence that the use of biomarkers according to 2017 revised criteria for diagnosis and management of dementia with Lewy bodies can increase diagnostic accuracy. Apart from nuclear medicine techniques, various magnetic resonance imaging (MRI) techniques have been utilized in attempt to enhance diagnostic accuracy. This chapter reviews structural, functional and diffusion MRI studies in DLB cohorts being compared to healthy controls, AD or dementia in Parkinson's disease (PDD). We also included relatively new MRI methods that may have potential to identify early DLB subjects and aim at examining brain iron and neuromelanin.

White matter abnormalities in the corpus callosum with cognitive impairment in Parkinson disease

OBJECTIVE

To evaluate microstructural characteristics of the corpus callosum using diffusion tensor imaging (DTI) and their relationships to cognitive impairment in Parkinson disease (PD).

METHODS

Seventy-five participants with PD and 24 healthy control (HC) participants underwent structural MRI brain scans including DTI sequences and clinical and neuropsychological evaluations. Using Movement Disorder Society criteria, PD participants were classified as having normal cognition (PD-NC, n = 23), mild cognitive impairment (PD-MCI, n = 35), or dementia (PDD, n = 17). Cognitive domain (attention/working memory, executive function, language, memory, visuospatial function) z scores were calculated. DTI scalar values, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were established for 5 callosal segments on a midsagittal plane, single slice using a topographically derived parcellation method. Scalar values were compared among participant groups. Regression analyses were performed on cognitive domain z scores and DTI metrics.

RESULTS

Participants with PD showed increased AD values in the anterior 3 callosal segments compared to healthy controls. Participants with PDD had significantly increased AD, MD, and RD in the anterior 2 segments compared to participants with PD-NC and most anterior segment compared to participants with PD-MCI. FA values did not differ significantly between participants with PD and participants with HC or among PD cognitive groups. The strongest associations for the DTI metrics and cognitive performance occurred in the most anterior and most posterior callosal segments, and also reflected fronto-striatal and posterior cortical type cognitive deficits, respectively.

CONCLUSIONS

Microstructural white matter abnormalities of the corpus callosum, as measured by DTI, may contribute to PD cognitive impairment by disrupting information transfer across interhemispheric and callosal-cortical projections.

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

Using MRI, a characteristic pattern of grey matter (GM) atrophy has been described in the early stages of Alzheimer's disease (AD); GM patterns at different stages of Parkinson's disease (PD) have been inconclusive. Few studies have directly compared structural changes in groups with mild cognitive impairment (MCI) caused by different pathologies (AD, PD). We used several analytical methods to determine GM changes at different stages of both PD and AD. We also evaluated associations between GM changes and cognitive measurements. Altogether 144 subjects were evaluated: PD with normal cognition (PD-NC; n = 23), PD with MCI (PD-MCI; n = 24), amnestic MCI (aMCI; n = 27), AD (n = 12), and age-matched healthy controls (HC; n = 58). All subjects underwent structural MRI and cognitive examination. GM volumes were analysed using two different techniques: voxel-based morphometry (VBM) and source-based morphometry (SBM), which is a multivariate method. In addition, cortical thickness (CT) was evaluated to assess between-group differences in GM. The cognitive domain z-scores were correlated with GM changes in individual patient groups. GM atrophy in the anterior and posterior cingulate, as measured by VBM, in the temporo-fronto-parietal component, as measured by SBM, and in the posterior cortical regions as well as in the anterior cingulate and frontal region, as measured by CT, differentiated aMCI from HC. Major hippocampal and temporal lobe atrophy (VBM, SBM) and to some extent occipital atrophy (SBM) differentiated AD from aMCI and from HC. Correlations with cognitive deficits were present only in the AD group. PD-MCI showed greater GM atrophy than PD-NC in the orbitofrontal regions (VBM), which was related to memory z-scores, and in the left superior parietal lobule (CT); more widespread limbic and fronto-parieto-occipital neocortical atrophy (all methods) differentiated this group from HC. Only CT revealed subtle GM atrophy in the anterior cingulate, precuneus, and temporal neocortex in PD-NC as compared to HC. None of the methods differentiated PD-MCI from aMCI. Both MCI groups showed distinct limbic and fronto-temporo-parietal neocortical atrophy compared to HC with no specific between-group differences. AD subjects displayed a typical pattern of major temporal lobe atrophy which was associated with deficits in all cognitive domains. VBM and CT were more sensitive than SBM in identifying frontal and posterior cortical atrophy in PD-MCI as compared to PD-NC. Our data support the notion that the results of studies using different analytical methods cannot be compared directly. Only CT measures revealed some subtle differences between HC and PD-NC.

Longitudinal white matter microstructural change in Parkinson's disease

Postmortem studies of Parkinson's disease (PD) suggest that Lewy body pathology accumulates in a predictable topographical sequence, beginning in the olfactory bulb, followed by caudal brainstem, substantia nigra, limbic cortex, and neocortex. Diffusion-weighted imaging (DWI) is sensitive, if not specific, to early disease-related white matter (WM) change in a variety of traumatic and degenerative brain diseases. Although numerous cross-sectional studies have reported DWI differences in cerebral WM in PD, only a few longitudinal studies have investigated whether DWI change exceeds that of normal aging or coincides with regional Lewy body accumulation. This study mapped regional differences in the rate of DWI-based microstructural change between 29 PD patients and 43 age-matched controls over 18 months. Iterative within- and between-subject tensor-based registration was completed on motion- and eddy current-corrected DWI images, then baseline versus follow-up difference maps of fractional anisotropy, mean, radial, and axial diffusivity were analyzed in the Biological Parametric Mapping toolbox for MATLAB. This analysis showed that PD patients had a greater decline in WM integrity in the rostral brainstem, caudal subcortical WM, and cerebellar peduncles, compared with controls. In addition, patients with unilateral clinical signs at baseline experienced a greater rate of WM change over the 18-month study than patients with bilateral signs. These findings suggest that rate of WM microstructural change in PD exceeds that of normal aging and is maximal during early stage disease. In addition, the neuroanatomic locations (rostral brainstem and subcortical WM) of accelerated WM change fit with current theories of topographic disease progression.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Nonmotor Subtyping in Parkinson's Disease

Nonmotor symptoms are integral to Parkinson's disease. Several subtypes dominated by specific nonmotor symptoms have emerged. In this chapter, the rationale behind nonmotor subtyping and currently proposed nonmotor subgroups within Parkinson's disease based on data-driven cluster analysis and clinical observations will be summarized. Furthermore, the concept of seven clinical nonmotor subtypes will be discussed in detail including the clinical presentation, potential biomarkers, and the clinical relevance. In future, nonmotor subtypes will possibly play a major role within the aim to achieve personalized medicine.

Cognition in Parkinson's Disease

Cognitive decline is now recognized as a common nonmotor symptom of Parkinson's disease, and it has been the subject of increasing research in recent decades. Cognitive deficits in Parkinson's disease can be distinguished as dopaminergically mediated executive dysfunction seen in the milder stages vs a global dementia syndrome that can occur with disease progression. The neural basis of these deficits has been explored from the perspective of multimodal imaging techniques to measure the structural, functional, and metabolic correlates of cognitive decline in Parkinson's disease. Increasingly, changes in neurotransmitter systems beyond dopamine, including the noradrenergic, serotonergic, and cholinergic systems, are being recognized for their contribution to cognitive decline. The impact of certain genetic variations on cognitive function has also been established, including links between cognitive decline and polymorphisms affecting COMT, MAPT, APOE, and GBA genotypes. Although therapeutic options for cognitive decline are still far less established than for motor systems, both pharmacological and nonpharmacological strategies are continuing to develop.

Lewy Body Disorders

Dementia syndromes associated with Lewy bodies are subdivided into dementia with Lewy bodies (DLB), an underdiagnosed cause of dementia in the elderly, and Parkinson disease with dementia (PDD), cognitive impairment appearing in people diagnosed with Parkinson disease. Their neuropathologic substrates are the widespread distribution of aggregates of the protein α-synuclein in neurons in cortical brain regions, accompanied by variable Alzheimer pathology. Clinical features of DLB and PDD include distinctive changes in cognition, behavior, movement, sleep, and autonomic function. Diagnostic criteria for DLB and PDD incorporate these features. Current treatment options for DLB and PDD are symptomatic.

Corpus callosal atrophy and associations with cognitive impairment in Parkinson disease

OBJECTIVE

To investigate atrophy of the corpus callosum on MRI in Parkinson disease (PD) and its relationship to cognitive impairment.

METHODS

One hundred patients with PD and 24 healthy control participants underwent clinical and neuropsychological evaluations and structural MRI brain scans. Participants with PD were classified as cognitively normal (PD-NC; n = 28), having mild cognitive impairment (PD-MCI; n = 47), or having dementia (PDD; n = 25) by Movement Disorder Society criteria. Cognitive domain (attention/working memory, executive function, memory, language, visuospatial function) z scores were calculated. With the use of FreeSurfer image processing, volumes for total corpus callosum and its subsections (anterior, midanterior, central, midposterior, posterior) were computed and normalized by total intracranial volume. Callosal volumes were compared between participants with PD and controls and among PD cognitive groups, covarying for age, sex, and PD duration and with multiple comparison corrections. Regression analyses were performed to evaluate relationships between callosal volumes and performance in cognitive domains.

RESULTS

Participants with PD had reduced corpus callosum volumes in midanterior and central regions compared to healthy controls. Participants with PDD demonstrated decreased callosal volumes involving multiple subsections spanning anterior to posterior compared to participants with PD-MCI and PD-NC. Regional callosal atrophy predicted cognitive domain performance such that central volumes were associated with the attention/working memory domain; midposterior volumes with executive function, language, and memory domains; and posterior volumes with memory and visuospatial domains.

CONCLUSIONS

Notable volume loss occurs in the corpus callosum in PD, with specific neuroanatomic distributions in PDD and relationships of regional atrophy to different cognitive domains. Callosal volume loss may contribute to clinical manifestations of PD cognitive impairment.

Classification of brain disease in magnetic resonance images using two-stage local feature fusion

BACKGROUND

Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale.

METHODS

Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to.

RESULTS

We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block.

DISCUSSION

This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.

Insulin Resistance and Neurodegeneration: Progress Towards the Development of New Therapeutics for Alzheimer's Disease

Alzheimer's disease (AD) should be regarded as a degenerative metabolic disease caused by brain insulin resistance and deficiency, and overlapping with the molecular, biochemical, pathophysiological, and metabolic dysfunctions in diabetes mellitus, non-alcoholic fatty liver disease, and metabolic syndrome. Although most of the diagnostic and therapeutic approaches over the past several decades have focused on amyloid-beta (Aβ42) and aberrantly phosphorylated tau, which could be caused by consequences of brain insulin resistance, the broader array of pathologies including white matter atrophy with loss of myelinated fibrils and leukoaraiosis, non-Aβ42 microvascular disease, dysregulated lipid metabolism, mitochondrial dysfunction, astrocytic gliosis, neuro-inflammation, and loss of synapses vis-à-vis growth of dystrophic neurites, is not readily accounted for by Aβ42 accumulations, but could be explained by dysregulated insulin/IGF-1 signaling with attendant impairments in signal transduction and gene expression. This review covers the diverse range of brain abnormalities in AD and discusses how insulins, incretins, and insulin sensitizers could be utilized to treat at different stages of neurodegeneration.

Advanced magnetic resonance imaging of neurodegenerative diseases

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

Central Pain Processing in Early-Stage Parkinson's Disease: A Laser Pain fMRI Study

Background & Objective

Pain is a common non-motor symptom in Parkinson’s disease. As dopaminergic dysfunction is suggested to affect intrinsic nociceptive processing, this study was designed to characterize laser-induced pain processing in early-stage Parkinson’s disease patients in the dopaminergic OFF state, using a multimodal experimental approach at behavioral, autonomic, imaging levels.

Methods

13 right-handed early-stage Parkinson’s disease patients without cognitive or sensory impairment were investigated OFF medication, along with 13 age-matched healthy control subjects. Measurements included warmth perception thresholds, heat pain thresholds, and central pain processing with event-related functional magnetic resonance imaging (erfMRI) during laser-induced pain stimulation at lower (E = 440 mJ) and higher (E = 640 mJ) target energies. Additionally, electrodermal activity was characterized during delivery of 60 randomized pain stimuli ranging from 440 mJ to 640 mJ, along with evaluation of subjective pain ratings on a visual analogue scale.

Results

No significant differences in warmth perception thresholds, heat pain thresholds, electrodermal activity and subjective pain ratings were found between Parkinson’s disease patients and controls, and erfMRI revealed a generally comparable activation pattern induced by laser-pain stimuli in brain areas belonging to the central pain matrix. However, relatively reduced deactivation was found in Parkinson’s disease patients in posterior regions of the default mode network, notably the precuneus and the posterior cingulate cortex.

Conclusion

Our data during pain processing extend previous findings suggesting default mode network dysfunction in Parkinson’s disease. On the other hand, they argue against a genuine pain-specific processing abnormality in early-stage Parkinson’s disease. Future studies are now required using similar multimodal experimental designs to examine pain processing in more advanced stages of Parkinson’s disease.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.

Combined Diffusion Tensor Imaging and Arterial Spin Labeling as Markers of Early Parkinson's disease

This study aimed to identify a PD-specific MRI pattern using combined diffusion tensor imaging (DTI) and arterial spin labeling (ASL) to discriminate patients with early PD from healthy subjects and evaluate disease status. Twenty-one early and 22 mid-late PD patients, and 22 healthy, age/gender-matched controls underwent 3-T MRI with apparent diffusion coefficient (ADC), fractional anisotropy (FA), fiber number (FN) and cerebral blood flow (CBF) measurements. We found that compared with healthy subjects, there was a profound reduction in FN passing through the SN in PD. FA in the SN and CBF in the caudate nucleus were inversely correlated with motor dysfunction. A negative correlation was observed between FA in the hippocampus (Hip) and the NMSS-Mood score, whereas CBF in the Hip and the prefrontal cortex(PFC) correlated with declined cognition. Stratified five-fold cross-validation identified FA in the SN(FA-SN_Av), CBF in the PFC(CBF-PFC_Av) and FA in the parietal white matter(FA-PWM_Av), and the combination of these measurements offered relatively high accuracy (AUC 0.975, 90% sensitivity and 100% specificity) in distinguishing those with early PD from healthy subjects. We demonstrate that the decreased FNs through SN in combination with changes in FA-SN_Av, CBF-PFC_Av and FA-PWM_Av values might serve as potential markers of early-stage PD.

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

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

Comparative Analysis of Lipid Extracts and Imaging Mass Spectrometry for Evaluating Cerebral White Matter Biochemical Pathology in an Experimental Second-Hand Cigarette Smoke Exposure Model

Background

White matter injury and degeneration are common features of developmental and aging-associated diseases, yet their pathobiological bases are poorly understood. However, recent advances in Matrix-Assisted Laser Desorption Ionization (MALDI) instruments and chemistry have provided critical tools for myelin-lipid analytical research.

Design

This study characterizes Cigarette Smoke (CS) exposure effects on frontal lobe lipid ion profiles in adult male A/J mice that had been exposed to air for 8 weeks (A8), CS for 4 (CS4) or 8 weeks (CS8), or CS8 followed by 2 weeks recovery (CS8+R). MALDI data acquired by analysis of lipid extracts plated onto a ground steel target (high through-put) were compared with Imaging Mass Spectrometry (IMS).

Results

MALDI-time-of-flight (TOF) detected 120 lipid ions with m/z’s of 600 to 1300 (phospholipids and sulfatides) in samples plated onto the steel target or analyzed by IMS, but just 25 ions (18%) were detected by both methods. IMS more effectively detected ions in the highest m/z range, whereas the extracts had abundant middle-range m/z ions. The experimental groups were better discriminated by PCA and R-generated heat map hierarchical clustering of IMS data than lipid extract data. On the other hand, both methods clearly delineated the CS4, CS8 and CS8+R experimental groups from control.

Conclusions

MALDI analysis of brain lipid extracts plated onto a ground steel target for high through-put studies, or imaged directly in tissue can be used to assess biochemical pathology of white matter neurodegeneration and responses to treatment.

Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases

Magnetic resonance imaging (MRI) is one of the core elements within the differential diagnostic work-up of patients with neurodegenerative diseases such as dementia syndromes, Parkinsonian syndromes, and motor neuron diseases. Currently, computerized MRI analyses are not routinely used for individual diagnosis; however, they have improved the anatomical understanding of pathomorphological alterations in various neurodegenerative diseases by quantitative comparisons between patients and controls at the group level. For multiparametric MRI protocols, including T1-weighted MRI, diffusion-weighted imaging, and intrinsic functional connectivity MRI, the potential as a surrogate marker is a subject of investigation. The additional value of MRI with respect to diagnosis at the individual level and for future disease-modifying multicentre trials remains to be defined. Here, we give an overview of recent applications of multiparametric MRI to patients with various neurodegenerative diseases. Starting from applications at the group level, continuous progress of a transfer to individual diagnostic classification is ongoing.

Cerebellar atrophy in Parkinson's disease and its implication for network connectivity

Pathophysiological and atrophic changes in the cerebellum are documented in Parkinson's disease. Without compensatory activity, such abnormalities could potentially have more widespread effects on both motor and non-motor symptoms. We examined how atrophic change in the cerebellum impacts functional connectivity patterns within the cerebellum and between cerebellar-cortical networks in 42 patients with Parkinson's disease and 29 control subjects. Voxel-based morphometry confirmed grey matter loss across the motor and cognitive cerebellar territories in the patient cohort. The extent of cerebellar atrophy correlated with decreased resting-state connectivity between the cerebellum and large-scale cortical networks, including the sensorimotor, dorsal attention and default networks, but with increased connectivity between the cerebellum and frontoparietal networks. The severity of patients' motor impairment was predicted by a combination of cerebellar atrophy and decreased cerebellar-sensorimotor connectivity. These findings demonstrate that cerebellar atrophy is related to both increases and decreases in cerebellar-cortical connectivity in Parkinson's disease, identifying potential cerebellar driven functional changes associated with sensorimotor deficits. A post hoc analysis exploring the effect of atrophy in the subthalamic nucleus, a cerebellar input source, confirmed that a significant negative relationship between grey matter volume and intrinsic cerebellar connectivity seen in controls was absent in the patients. This suggests that the modulatory relationship of the subthalamic nucleus on intracerebellar connectivity is lost in Parkinson's disease, which may contribute to pathological activation within the cerebellum. The results confirm significant changes in cerebellar network activity in Parkinson's disease and reveal that such changes occur in association with atrophy of the cerebellum.

Hippocampal subfield atrophy in relation to cerebrospinal fluid biomarkers and cognition in early Parkinson's disease: a cross-sectional study

Cognition is often affected early in Parkinson’s disease (PD). Lewy body and amyloid β (Aβ) pathology and cortical atrophy may be involved. The aim of this study was to examine whether medial temporal lobe structural changes may be linked to cerebrospinal fluid (CSF) biomarker levels and cognition in early PD. PD patients had smaller volumes of total hippocampus, presubiculum, subiculum, CA2–3, CA4-DG, and hippocampal tail compared with normal controls (NCs). In the PD group, lower CSF Aβ38 and 42 were significant predictors for thinner perirhinal cortex. Lower Aβ42 and smaller presubiculum and subiculum predicted poorer verbal learning and delayed verbal recall. Smaller total hippocampus, presubiculum and subiculum predicted poorer visuospatial copying. Lower Aβ38 and 40 and thinner perirhinal cortex predicted poorer delayed visual reproduction. In conclusion, smaller volumes of hippocampal subfields and subhippocampal cortex thickness linked to lower CSF Aβ levels may contribute to cognitive impairment in early PD. Thirty-three early PD patients (13 without, 5 with subjective, and 15 with mild cognitive impairment) and NC had 3 T magnetic resonance imaging (MRI) scans. The MRI scans were post processed for volumes of hippocampal subfields and entorhinal and perirhinal cortical thickness. Lumbar puncture for CSF biomarkers Aβ38, 40, 42, total tau, phosphorylated tau (Innogenetics), and total α-synuclein (Meso Scale Diagnostics) were performed. Multiple regression analyses were used for between-group comparisons of the MRI measurements in the NC and PD groups and for assessment of CSF biomarkers and neuropsychological tests in relation to morphometry in the PD group.

Cigarette Smoke-Induced Alterations in Frontal White Matter Lipid Profiles Demonstrated by MALDI-Imaging Mass Spectrometry: Relevance to Alzheimer's Disease

BACKGROUND

Meta-analysis has shown that smokers have significantly increased risks for Alzheimer's disease (AD), and neuroimaging studies showed that smoking alters white matter (WM) structural integrity.

OBJECTIVE

Herein, we characterize the effects of cigarette smoke (CS) exposures and withdrawal on WM myelin lipid composition using matrix assisted laser desorption and ionization-imaging mass spectrometry (MALDI-IMS).

METHODS

Young adult male A/J mice were exposed to air (8 weeks; A8), CS (4 or 8 weeks; CS4, CS8), or CS8 followed by 2 weeks recovery (CS8 + R). Frontal lobe WM was examined for indices of lipid and protein oxidation and lipid profile alterations by MALDI-IMS. Lipid ions were identified by MS/MS with the LIPID MAPS prediction tools database. Inter-group comparisons were made using principal component analysis and R-generated heatmap.

RESULTS

CS increased lipid and protein adducts such that higher levels were present in CS8 compared with CS4 samples. CS8 + R reversed CS8 effects and normalized the levels of oxidative stress. MALDI-IMS demonstrated striking CS-associated alterations in WM lipid profiles characterized by either reductions or increases in phospholipids (phosphatidylinositol, phosphatidylserine, phosphatidylcholine, or phosphatidylethanolamine) and sphingolipids (sulfatides), and partial reversal of CS's inhibitory effects with recovery. The heatmap hierarchical dendrogram and PCA distinguished CS exposure, duration, and withdrawal effects on WM lipid profiles.

CONCLUSION

CS-mediated WM degeneration is associated with lipid peroxidation, protein oxidative injury, and alterations in myelin lipid composition, including shifts in phospholipids and sphingolipids needed for membrane integrity, plasticity, and intracellular signaling. Future goals are to delineate WM abnormalities in AD using MALDI-IMS, and couple the findings with MRI-mass spectroscopy to improve in vivo diagnostics and early detection of brain biochemical responses to treatment.

Tobacco Smoke-Induced Brain White Matter Myelin Dysfunction: Potential Co-Factor Role of Smoking in Neurodegeneration

BACKGROUND

Meta-analysis studies showed that smokers have increased risk for developing Alzheimer's disease (AD) compared with non-smokers, and neuroimaging studies revealed that smoking damages white matter structural integrity.

OBJECTIVE

The present study characterizes the effects of side-stream (second hand) cigarette smoke (CS) exposures on the expression of genes that regulate oligodendrocyte myelin-synthesis, maturation, and maintenance and neuroglial functions.

METHODS

Adult male A/J mice were exposed to air (8 weeks; A8), CS (4 or 8 weeks; CS4, CS8), or CS8 followed by 2 weeks recovery (CS8 + R). The frontal lobes were used for histology and qRT-PCR analysis.

RESULTS

Luxol fast blue, Hematoxylin and Eosin stained histological sections revealed CS-associated reductions in myelin staining intensity and narrowing of the corpus callosum. CS exposures broadly decreased mRNA levels of immature and mature oligodendrocyte myelin-associated, neuroglial, and oligodendrocyte-related transcription factors. These effects were more prominent in the CS8 compared with CS4 group, suggesting that molecular abnormalities linked to white matter atrophy and myelin loss worsen with duration of CS exposure. Recovery normalized or upregulated less than 25% of the suppressed genes; in most cases, inhibition of gene expression was either sustained or exacerbated.

CONCLUSION

CS exposures broadly inhibit expression of genes needed for myelin synthesis and maintenance. These adverse effects often were not reversed by short-term CS withdrawal. The results support the hypothesis that smoking contributes to white matter degeneration, and therefore could be a key risk factor for a number of neurodegenerative diseases, including AD.