Brain network markers of abnormal cerebral glucose metabolism and blood flow in Parkinson's disease

A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson's disease

Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both network-based statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison with the control group. Evaluation of mean connectivity between all regions of interest (ROIs) within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual and striatal networks and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and inter-network synchrony may support cognitive function in the face of PD-related network disruptions.

"Advanced" Parkinson's disease: A review

There is no consensus driven definition of "advanced" Parkinson's disease (APD) currently. APD has been described in terms of emergence of specific clinical features and clinical milestones of the disease e.g., motor fluctuations, time to increasing falls, emergence of cognitive decline, etc. The pathological burden of disease has been used to characterize various stages of the disease. Imaging markers have been associated with various motor and nonmotor symptoms of advancing disease. In this review, we present an overview of clinical, pathologic, and imaging markers of APD. We also propose a model of disease definition involving longitudinal assessments of these markers as well as quality of life metrics to better understand and predict disease progression in those with Parkinson's disease.

Positive relation between dopamine neuron degeneration and metabolic connectivity disruption in the MPTP plus probenecid mouse model of Parkinson's disease

The clinical manifestation of Parkinson's disease (PD) appears when neurodegeneration is already advanced, compromising the efficacy of disease-modifying treatment approaches. Biomarkers to identify the early stages of PD are therefore of paramount importance for the advancement of the therapy of PD. In the present study, by using a mouse model of PD obtained by subchronic treatment with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the clearance inhibitor probenecid (MPTPp), we identified prodromal markers of PD by combining in vivo positron emission tomography (PET) imaging and ex vivo immunohistochemistry. Longitudinal PET imaging of the dopamine transporter (DAT) by [18F]-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ([18F]-FP-CIT), and brain glucose metabolism by 2-deoxy-2-[18F]-fluoroglucose ([18F]-FDG) were performed before MPTPp treatment and after 1, 3, and 10 MPTPp administrations, in order to assess relation between dopamine neuron integrity and brain connectivity. The results show that in vivo [18F]-FP-CIT in the dorsal striatum was not modified after the first administration of MPTPp, tended to decrease after 3 administrations, and significantly decreased after 10 MPTPp administrations. Post-mortem immunohistochemical analyses of DAT and tyrosine hydroxylase (TH) in the striatum showed a positive correlation with [18F]-FP-CIT, confirming the validity of repeated MPTPp-treated mice as a model that can reproduce the progressive pathological changes in the early phases of PD. Analysis of [18F]-FDG uptake in several brain areas connected to the striatum showed that metabolic connectivity was progressively disrupted, starting from the first MPTPp administration, and that significant connections between cortical and subcortical regions were lost after 10 MPTPp administrations, suggesting an association between dopamine neuron degeneration and connectivity disruption in this PD model. The results of this study provide a relevant model, where new drugs that can alleviate neurodegeneration in PD could be evaluated preclinically.

A bibliometric analysis of neuroimaging biomarkers in Parkinson disease based on Web of Science

BACKGROUND

This study aimed to analyze and summarize the research hotspots and trends in neuroimaging biomarkers (NMBM) in Parkinson disease (PD) based on the Web of Science core collection database and provide new references for future studies.

METHODS

Literature regarding NMBM in PD from 1998 to 2022 was analyzed using the Web of Science core collection database. We utilized CiteSpace software (6.1R2) for bibliometric analyses of countries/institutions/authors, keywords, keyword bursts, references, and their clusters.

RESULTS

A total of 339 studies were identified with a continually increasing annual trend. The most productive country and collaboration was the United States. The top research hotspot is PD cognitive disorder. NMBM and artificial intelligence medical imaging have been applied in the clinical diagnosis, differential diagnosis, treatment, and prognosis of PD. The trends in this field include research on T1 weighted structure magnetic resonance imaging in accordance with voxel-based morphometry, PD cognitive disorder, and neuroimaging features of Lewy body dementia and Alzheimer disease.

CONCLUSION

The development of NMBM in PD will be effectively promoted by drawing on international research hotspots and cutting-edge technologies, emphasizing international collaboration and institutional cooperation at the national level, and strengthening interdisciplinary research.

Imaging of sleep disorders in pre-Parkinsonian syndromes

PURPOSE OF REVIEW

Neuroimaging has been advanced in the last years and enabled clinicians to evaluate sleep disorders, especially isolated rapid eye movement sleep disorder (iRBD), which can be seen in alpha-synucleinopathies. iRBD is the best prodromal clinical marker for phenoconversion to these neurodegenerative diseases. This review aims to provide an update on advanced neuroimaging biomarkers in iRBD.

RECENT FINDINGS

Advanced structural MRI techniques, such as diffusion tensor imaging and functional MRI, neuromelanin-sensitive MRI, and scintigraphic neuroimaging such as cholinergic PET, dopamine transporter imaging - single-photon emission computerized tomography, perfusional single-photon emission computerized tomography, and cardiac metaiodobenzylguanidine can provide diagnostic and prognostic imaging biomarkers for iRBD, in isolation and more robustly when combined.

SUMMARY

New advanced neuroimaging can provide imaging biomarkers and aid in the appropriate clinical assessment and future therapeutic trials.

Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease

Objective

The diagnosis of Parkinson's disease (PD) remains challenging. Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has revealed the metabolic abnormalities associated with PD at systemic levels, the underlying rich-club organization of the metabolic connectome in these patients remains largely unknown.

Materials and Methods

The data of 49 PD patients and 49 well-matched healthy controls (HCs) were retrieved and assessed. An individual metabolic connectome based on the standard uptake value (SUV) was built using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method to compare the rich-club properties between PD patients and HC.

Results

Our results showed the rich-club organization of metabolic networks (normalized rich-club coefficients > 1) in the PD and HC group were within a range of thresholds. Further, patients with PD demonstrated lower strength and degree in rich-club connections compared with HCs (strength: HCs = 55.70 ± 8.52, PDs = 52.03 ± 10.49, p = 0.028; degree: HCs = 56.55 ± 8.60, PDs = 52.85 ± 10.62, p = 0.029), but difference between their feeder and local connections was not significant.

Conclusion

Individual metabolic networks combined with rich club analysis indicated that PD patients had decreased rich club connections but similar feeder and local connections compared with HCs, indicating rich club connections as a promising marker for early diagnosis of PD.

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease

Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as ¹⁸F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (¹⁸F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and ¹⁸F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.

Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders

Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.

Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session

Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.

Functional Alteration of Cerebello-Cerebral Coupling in an Experimental Mouse Model of Parkinson's Disease

In Parkinson's disease, the degeneration of the midbrain dopaminergic neurons is consistently associated with modified metabolic activity in the cerebellum. Here we examined the functional reorganization taking place in the cerebello-cerebral circuit in a murine model of Parkinson's disease with 6-OHDA lesion of midbrain dopaminergic neurons. Cerebellar optogenetic stimulations evoked similar movements in control and lesioned mice, suggesting a normal coupling of cerebellum to the motor effectors after the lesion. In freely moving animals, the firing rate in the primary motor cortex was decreased after the lesion, while cerebellar nuclei neurons showed an increased firing rate. This increase may result from reduced inhibitory Purkinje cells inputs, since a population of slow and irregular Purkinje cells was observed in the cerebellar hemispheres of lesioned animals. Moreover, cerebellar stimulations generated smaller electrocortical responses in the motor cortex of lesioned animals suggesting a weaker cerebello-cerebral coupling. Overall these results indicate the presence of functional changes in the cerebello-cerebral circuit, but their ability to correct cortical dysfunction may be limited due to functional uncoupling between the cerebellum and cerebral cortex.

Brain Functional and Structural Signatures in Parkinson's Disease

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). ¹⁸F-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

HIV-1 infection alters energy metabolism in the brain: Contributions to HIV-associated neurocognitive disorders

The brain is particularly sensitive to changes in energy supply. Defects in glucose utilization and mitochondrial dysfunction are hallmarks of nearly all neurodegenerative diseases and are also associated with the cognitive decline that occurs as the brain ages. Chronic neuroinflammation driven by glial activation is commonly implicated as a contributing factor to neurodegeneration and cognitive impairment. Human immunodeficiency virus-1 (HIV-1) disrupts normal brain homeostasis and leads to a spectrum of HIV-associated neurocognitive disorders (HAND). HIV-1 activates stress responses in the brain and triggers a state of chronic neuroinflammation. Growing evidence suggests that inflammatory processes and bioenergetics are interconnected in the propagation of neuronal dysfunction. Clinical studies of people living with HIV and basic research support the notion that HIV-1 creates an environment in the CNS that interrupts normal metabolic processes at the cellular level to collectively alter whole brain metabolism. In this review, we highlight reports of abnormal brain metabolism from clinical studies and animal models of HIV-1. We also describe diverse CNS cell-specific changes in bioenergetics associated with HIV-1. Moreover, we propose that attention should be given to adjunctive therapies that combat sources of metabolic dysfunction as a mean to improve and/or prevent neurocognitive impairments.

Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions

Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.

The Role of Glucagon-Like Peptide 1 (GLP1) in Type 3 Diabetes: GLP-1 Controls Insulin Resistance, Neuroinflammation and Neurogenesis in the Brain

Alzheimer's disease (AD), characterized by the aggregation of amyloid-β (Aβ) protein and neuroinflammation, is the most common neurodegenerative disease globally. Previous studies have reported that some AD patients show impaired glucose utilization in brain, leading to cognitive decline. Recently, diabetes-induced dementia has been called "type 3 diabetes", based on features in common with those of type 2 diabetes and the progression of AD. Impaired glucose uptake and insulin resistance in the brain are important issues in type 3 diabetes, because these problems ultimately aggravate memory dysfunction in the brain. Glucagon-like peptide 1 (GLP-1) has been known to act as a critical controller of the glucose metabolism. Several studies have demonstrated that GLP-1 alleviates learning and memory dysfunction by enhancing the regulation of glucose in the AD brain. However, the specific actions of GLP-1 in the AD brain are not fully understood. Here, we review evidences related to the role of GLP-1 in type 3 diabetes.

Nonmotor Symptoms and Natural History of Parkinson's Disease: Evidence From Cognitive Dysfunction and Role of Noninvasive Interventions

Parkinson's disease (PD) is a common neurodegenerative disorder, characterized by motor and nonmotor symptoms (NMS). Several subsequent studies substantiate the great functional burden related to NMS, their progression, and negative effect on quality of life in PD. Additional evidence indicates interesting relationships between striatal dopaminergic function and NMS. The basal ganglia are implicated in the modulation and integration of sensory information and pain, bladder function is under control of both inhibitory (D1) and facilitatory (D2) dopaminergic inputs, finally reduced dopaminergic activity in the mesocortical and mesolimbic pathways is involved in the development of several NMS including mood, motivational, and cognitive alterations. Some NMS fluctuate in response to dopaminergic treatment and are relieved by dopamine replacement therapy, other are insensitive to current therapeutic strategies. The relation among the overall disease complications, perhaps the most important for PD patients and family members' well-being and functionality is dementia that affects most PD patients over the course of disease. Specific pharmacological treatment is lacking, and alternative approaches have been implemented to improve everyday functionality and quality of life. The state of the art suggests that cognitive rehabilitation in PD is possible and may either increase performance or preserve cognitive level over the time. However, it is also evident that cognitive abnormalities in PD are heterogeneous and we still do not have biomarkers to detect early patients at risk for dementia. Cognitive dysfunction is one the most prevalent NMS and is a clinically and functionally important disease milestone. Given the available clinical and imaging evidence it is possible to use cognition to model NMS progression and design nonpharmacological interventions. In this chapter we will address the use of cognitive rehabilitation and noninvasive brain stimulation techniques to modulate cognitive performance and rescue connectivity in affected brain circuitry.

Neurovascular-neuroenergetic coupling axis in the brain: master regulation by nitric oxide and consequences in aging and neurodegeneration

The strict energetic demands of the brain require that nutrient supply and usage be fine-tuned in accordance with the specific temporal and spatial patterns of ever-changing levels of neuronal activity. This is achieved by adjusting local cerebral blood flow (CBF) as a function of activity level - neurovascular coupling - and by changing how energy substrates are metabolized and shuttled amongst astrocytes and neurons - neuroenergetic coupling. Both activity-dependent increase of CBF and O₂ and glucose utilization by active neural cells are inextricably linked, establishing a functional metabolic axis in the brain, the neurovascular-neuroenergetic coupling axis. This axis incorporates and links previously independent processes that need to be coordinated in the normal brain. We here review evidence supporting the role of neuronal-derived nitric oxide (^•NO) as the master regulator of this axis. Nitric oxide is produced in tight association with glutamatergic activation and, diffusing several cell diameters, may interact with different molecular targets within each cell type. Hemeproteins such as soluble guanylate cyclase, cytochrome c oxidase and hemoglobin, with which ^•NO reacts at relatively fast rates, are but a few of the key in determinants of the regulatory role of ^•NO in the neurovascular-neuroenergetic coupling axis. Accordingly, critical literature supporting this concept is discussed. Moreover, in view of the controversy regarding the regulation of catabolism of different neural cells, we further discuss key aspects of the pathways through which ^•NO specifically up-regulates glycolysis in astrocytes, supporting lactate shuttling to neurons for oxidative breakdown. From a biomedical viewpoint, derailment of neurovascular-neuroenergetic axis is precociously linked to aberrant brain aging, cognitive impairment and neurodegeneration. Thus, we summarize current knowledge of how both neurovascular and neuroenergetic coupling are compromised in aging, traumatic brain injury, epilepsy and age-associated neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease, suggesting that a shift in cellular redox balance may contribute to divert ^•NO bioactivity from regulation to dysfunction.

Brain glucose metabolism: Role of Wnt signaling in the metabolic impairment in Alzheimer's disease

The brain is an organ that has a high demand for glucose. In the brain, glucose is predominantly used in energy production, with almost 70% of the energy used by neurons. The importance of the energy requirement in neurons is clearly demonstrated by the fact that all neurodegenerative disorders exhibit a critical metabolic impairment that includes decreased glucose uptake/utilization and decreased mitochondrial activity, with a consequent diminution in ATP production. In fact, in Alzheimer's disease, the measurement of the general metabolic rate of the brain has been reported to be an accurate tool for diagnosis. Additionally, the administration of metabolic activators such as insulin/glucagon-like peptide 1 can improve memory/learning performance. Despite the importance of energy metabolism in the brain, little is known about the cellular pathways involved in the regulation of this process. Several reports postulate a role for Wnt signaling as a general metabolic regulator. Thus, in the present review, we discuss the antecedents that support the relationship between Wnt signaling and energy metabolism in the Alzheimer's disease.

The Unwanted Cell Migration in the Brain: Glioma Metastasis

Cell migration is identified as a highly orchestrated process. It is a fundamental and essential phenomenon underlying tissue morphogenesis, wound healing, and immune response. Under dysregulation, it contributes to cancer metastasis. Brain is considered to be the most complex organ in human body containing many types of neural cells with astrocytes playing crucial roles in monitoring both physiological and pathological functions. Astrocytoma originates from astrocytes and its most malignant type is glioblastoma multiforme (WHO Grade IV astrocytoma), which is capable to infiltrate widely into the neighboring brain tissues making a complete resection of tumors impossible. Very recently, we have reviewed the mechanisms for astrocytes in migration. Given the fact that astrocytoma shares many histological features with astrocytes, we therefore attempt to review the mechanisms for glioma cells in migration and compare them to normal astrocytes, hoping to obtain a better insight into the dysregulation of migratory mechanisms contributing to their metastasis in the brain.

Cerebral vasomotor reactivity in neurodegenerative diseases

Small-caliber cerebral vessels change their diameters in response to alterations of key metabolite concentrations such as carbon dioxide or oxygen. This phenomenon, termed the cerebral vasomotor reactivity (CVMR), is the basis for blood flow regulation in the brain in accordance with its metabolic status. Typically, CVMR is determined as the amount of change in cerebral blood flow in response to a vasodilating stimulus, which can be measured by various neuroimaging methods or by transcranial Doppler. It has been shown that CVMR is impaired in cerebrovascular diseases, but there is also evidence of a similar dysfunction in neurodegenerative disorders. Here, we review studies that have investigated CVMR in the common neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and multiple sclerosis. Moreover, we discuss potential neurodegenerative mechanisms responsible for the impairment of CVMR.

Metabolic Topology of Neurodegenerative Disorders: Influence of Cognitive and Motor Deficits

Parkinson disease with and without dementia (PDD and PD, respectively), dementia with Lewy bodies (DLB), and Alzheimer dementia (AD) traditionally have been viewed as distinct clinical and pathologic entities. However, intriguing overlaps in biochemical, clinical, and imaging findings question the concept of distinct entities and suggest a continuous spectrum in which individual patients express PD-typical patterns and AD-typical patterns to a variable degree.

METHODS

Following this concept, we built a topological map based on regional patterns of the cerebral metabolic rate of glucose as measured with (18)F-FDG PET to rank and localize single subjects' disease status according to PD-typical (PD vs. controls) and AD-typical (AD vs. controls) pattern expression in patients clinically characterized as PD, PDD, DLB, amnestic mild cognitive impairment, and AD.

RESULTS

The topology generally confirmed an indivisible spectrum of disease manifestation according to 2 separable expression patterns. The expression values derived from the first pattern were highly correlated with individual cognitive, but not motor, disability. The opposite was found for the corresponding expression values of the second pattern.

CONCLUSION

The metabolic imaging analysis supports the notion that there is a continuous spectrum of neurodegeneration between AD and PD. Furthermore, PDD and DLB may in fact represent 1 overlapping disease entity, characterized by the presence of mixed neuropathology and only different by the time course.