Neuroimaging in dementia

The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.

PURPOSE

To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.

MATERIAL AND METHODS

A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where V A is weighted volume of frontal and temporal lobes and V P of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.

RESULTS

A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).

CONCLUSION

The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.

Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.

Evaluation of MR Visual Rating Scales in Major Forms of Dementia

Objective  The aim of the study is to visually rate major forms of dementia using global cortical atrophy (GCA), medial temporal lobe atrophy (MTA), and Fazeka’s scales and Koedam’s score using magnetic resonance imaging (MRI). The purpose is to correlate the visual rating scales (VRS) with severity of dementia.

Materials and Methods  Thirty patients fulfilling DSM 5 (Diagnostic and Statistical Manual of Mental Disorders) criteria for Alzheimer’s dementia (AD), vascular dementia (VaD), and frontotemporal dementia (FTD) underwent MRI brain. Cortical atrophy, medial temporal, and parietal lobe atrophy were assessed using GCA and MTA scales and Koedam’s score, respectively. White matter hyperintensities were assessed using Fazeka’s scale. Correlation between VRS and mini-mental state exam (MMSE) scores was done using Pearson correlation coefficient.

Results  70% of patients had Grade 2 GCA. More patients with AD had higher MTA scores as compared with others with 57% of AD patients showing abnormal for age MTA scores. Fazeka’s scale was abnormal for age in 58.33% of VaD and 57% AD patients. Majority (75%) showing severe parietal atrophy (Grade 3 Koedam’s score) were AD patients. Disproportionate frontal lobe atrophy was seen in all four (100%) FTD patients. Significant negative correlation was seen between MMSE and GCA scores of all patients ( p -value = 0.003) as well as between MTA and MMSE scores in AD patients ( p -value = 0.00095).

Conclusion  Visual rating of MTA is a reliable method for detecting AD and correlates strongly with memory scores. Atrophy of specific regions is seen more commonly in some conditions, for instance, where MTA and parietal atrophy are specific for AD while asymmetric frontal lobe and temporal pole atrophy favor FTD.

Pattern Recognition to Objectively Differentiate the Etiology of Cognitive Decline: Analysis of the Impact of Stroke and Alzheimer's Disease

BACKGROUND

Undetected Alzheimer's disease (AD) and stroke neuropathology is believed to account for a large proportion of decline in cognitive performance that is attributed to normal aging. This study examined the amount of variance in age-related cognitive change that is accounted for by AD and stroke using a novel pattern recognition protocol.

METHOD

Secondary analyses of data collected for the Health and Retirement Study (N = 17,579) were used to objectively characterize patterns of cognitive decline associated with AD and stroke. The rate of decline in episodic memory and orientation was the outcome of interest, while algorithms indicative of AD and stroke pathology were the predictors of interest.

RESULTS

The average age of the sample was 67.54 ± 10.45 years at baseline, and they completed, on average, 14.20 ± 3.56 years of follow-up. After adjusting for demographics, AD and stroke accounted for approximately half of age-associated decline in cognition (51.07-55.6% for orientation and episodic memory, respectively) and explained variance attributed to random slopes in longitudinal multilevel models.

DISCUSSION

The results of this study suggested that approximately half of the cognitive decline usually attributed to normal aging are more characteristic of AD and stroke.

Systemic hepcidin quantified with LC-MS/MS in dementia in association with disease pathology and severity and with structural changes in the brain

Hepcidin is a peptide hormone regulating iron metabolism, the dyshomeostasis of which has been implicated in dementia. Yet, data on hepcidin status in dementia are scanty, limited to Alzheimer's disease (AD) and inconsistent due to methodological problems with its determination using immunoassays and/or lack of homogeneity of evaluated groups. Hepcidin association with vascular dementia (VaD) remains unknown. We proposed a mass spectrometry method of hepcidin quantification in sera and aimed at determining hepcidin systemic status in patients with dementia of AD, VaD, or mixed (MD) pathology, with reference to the degree of cognitive loss and structural changes in the brain as well as at evaluating the diagnostic potential of hepcidin as a biomarker. We found that hepcidin concentrations were significantly elevated in VaD and insignificantly so in AD or MD and that they positively correlated with the Clinical Dementia Rating and inversely with the Mini Mental State Examination. Hepcidin tended to be more pronouncedly elevated in patients with advanced cortical atrophy and white matter lesions. It displayed a biphasic relationship with the Hachinski Ischemic Scale and a good accuracy as dementia but not differential marker. Taken together, our results demonstrated that dementia of vascular and not neurodegenerative pathology is associated with significant elevation of systemic hepcidin. Hepcidin elevation reflects the degree of cognitive loss as well as the severity of structural changes in the brain. If confirmed in a prospective study, hepcidin quantification may hold promise as a diagnostic marker; its accuracy as a differential marker of VaD is insufficient.

Pattern Recognition to Identify Stroke in the Cognitive Profile: Secondary Analyses of a Prospective Cohort Study

Background

Stroke can produce subtle changes in the brain that may produce symptoms that are too small to lead to a diagnosis. Noting that a lack of diagnosis may bias research estimates, the current study sought to examine the utility of pattern recognition relying on serial assessments of cognition to objectively identify stroke-like patterns of cognitive decline (pattern-detected stroke, p-stroke).

Methods

Secondary data analysis was conducted using participants with no reported history of stroke in the Health and Retirement Study, a large (n = 16,113) epidemiological study of cognitive aging among respondents aged 50 years and older that measured episodic memory consistently biennially between 1996 and 2014. Analyses were limited to participants with at least 4 serial measures of episodic memory. Occurrence and date of p-stroke events were identified utilizing pattern recognition to identify stepwise declines in cognition consistent with stroke. Descriptive statistics included the percentage of the population with p-stroke, the mean change in episodic memory resulting in stroke-positive testing, and the mean time between p-stroke and first major diagnosed stroke. Statistical analyses comparing cases of p-stroke with reported major stroke relied on the area under the receiver-operating curve (AUC). Longitudinal modeling was utilized to examine rates of change in those with/without major stroke after adjusting for demographics.

Results

The pattern recognition protocol identified 7,499 p-strokes that went unreported. On average, individuals with p-stroke declined in episodic memory by 1.986 (SD = 0.023) words at the inferred time of stroke. The resulting pattern recognition protocol was able to identify self-­reported major stroke (AUC = 0.58, 95% CI = 0.57-0.59, p < 0.001). In those with a reported major stroke, p-stroke events were detectable on average 4.963 (4.650–5.275) years (p < 0.001) before diagnosis was first reported. The incidence of p-stroke was 40.23/1,000 (95% CI = 39.40–41.08) person-years. After adjusting for sex, age was associated with the incidence of p-stroke and major stroke at similar rates.

Conclusions

This is the first study to propose utilizing pattern recognition to identify the incidence and timing of p-stroke. Further work is warranted examining the clinical utility of pattern recognition in identifying p-stroke in longitudinal cognitive profiles.

Diagnostic Value of Diffusion Tensor Imaging and Positron Emission Tomography in Early Stages of Frontotemporal Dementia

BACKGROUND

Due to suboptimal sensitivity and specificity of structural and molecular neuroimaging tools, the diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging.

OBJECTIVE

Investigation of the sensitivity of diffusion tensor imaging (DTI) and fluorodeoxyglucose positron emission tomography (FDG-PET) to detect cerebral alterations in early stages of bvFTD despite inconspicuous conventional MRI.

METHODS

Thirty patients with early stages of bvFTD underwent a detailed neuropsychological examination, cerebral 3T MRI with DTI analysis, and FDG-PET. After 12 months of follow-up, all patients finally fulfilled the diagnosis of bvFTD. Individual FDG-PET data analyses showed that 20 patients exhibited a "typical" pattern for bvFTD with bifrontal and/or temporal hypometabolism (bvFTD/PET+), and that 10 patients showed a "non-typical"/normal pattern (bvFTD/PET-). DTI data were compared with 42 healthy controls in an individual and voxel-based group analysis. To examine the clinical relevance of the findings, associations between pathologically altered voxels of DTI or FDG-PET results and behavioral symptoms were estimated by linear regression analyses.

RESULTS

DTI voxel-based group analyses revealed microstructural degeneration in bifrontal and bitemporal areas in bvFTD/PET+ and bvFTD/PET- groups. However, when comparing the sensitivity of individual DTI data analysis with FDG-PET, DTI appeared to be less sensitive. Neuropsychological symptoms were considerably related to neurodegeneration within frontotemporal areas identified by DTI and FDG-PET.

CONCLUSION

DTI seems to be an interesting tool for detection of functionally relevant neurodegenerative alterations in early stages of bvFTD, even in bvFTD/PET- patients. However, at a single subject level, it seems to be less sensitive than FDG-PET. Thus, improvement of individual DTI analysis is necessary.

Structural and metabolic cerebral alterations between elderly bipolar disorder and behavioural variant frontotemporal dementia: A combined MRI-PET study

BACKGROUND

Elderly bipolar disorder (BD) and behavioural variant of frontotemporal dementia (bvFTD) may exhibit similar symptoms and both disorders are characterized by selective abnormalities in cortical and subcortical regions that are associated with cognitive and emotional impairments. We aimed to investigate common and distinct neural substrates of BD and bvFTD by coupling, for the first time, magnetic resonance imaging (MRI) and positron emission tomography (PET) techniques.

METHODS

3-Tesla MRI and 18 fluorodeoxyglucose-PET scans were acquired for 16 elderly BD patients, 23 bvFTD patients with mild cognitive impairments and 68 healthy controls (48 for PET and 20 for MRI analyses).

RESULTS

BD and bvFTD patients exhibit a different localization of grey matter reductions in the lateral prefrontal cortex, with the first group showing grey matter decrease in the ventrolateral prefrontal cortex and the latter group showing grey matter reductions in the dorsolateral prefrontal cortex as well as unique grey matter and metabolic alterations within the orbitofrontal cortex. The bvFTD group also displayed unique volumetric shrinkage in regions within the temporo-parietal network together with greater metabolic impairments within the temporal cortex and more extensive volumetric and metabolic abnormalities within the limbic lobe. Finally, while the BD group showed greater grey matter volumes in caudate nucleus, bvFTD subjects displayed lower metabolism.

CONCLUSION

This MRI-PET study explored, for the first time to the best of our knowledge, structural and functional abnormalities in bvFTD and elderly BD patients, with the final aim of identifying the specific biological signature of these disorders, which might have important implications not only in prevention but also in differential diagnosis and treatment.

Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia

Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls—HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.

A Cross-Validation of FDG- and Amyloid-PET Biomarkers in Mild Cognitive Impairment for the Risk Prediction to Dementia due to Alzheimer's Disease in a Clinical Setting

Assessments of brain glucose metabolism (18F-FDG-PET) and cerebral amyloid burden (11C-PiB-PET) in mild cognitive impairment (MCI) have shown highly variable performances when adopted to predict progression to dementia due to Alzheimer's disease (ADD). This study investigates, in a clinical setting, the separate and combined values of 18F-FDG-PET and 11C-PiB-PET in ADD conversion prediction with optimized data analysis procedures. Respectively, we investigate the accuracy of an optimized SPM analysis for 18F-FDG-PET and of standardized uptake value ratio semiquantification for 11C-PiB-PET in predicting ADD conversion in 30 MCI subjects (age 63.57±7.78 years). Fourteen subjects converted to ADD during the follow-up (median 26.5 months, inter-quartile range 30 months). Receiver operating characteristic analyses showed an area under the curve (AUC) of 0.89 and of 0.81 for, respectively, 18F-FDG-PET and 11C-PiB-PET. 18F-FDG-PET, compared to 11C-PiB-PET, showed higher specificity (1.00 versus 0.62, respectively), but lower sensitivity (0.79 versus 1.00). Combining the biomarkers improved classification accuracy (AUC = 0.96). During the follow-up time, all the MCI subjects positive for both PET biomarkers converted to ADD, whereas all the subjects negative for both remained stable. The difference in survival distributions was confirmed by a log-rank test (p = 0.002). These results indicate a very high accuracy in predicting MCI to ADD conversion of both 18F-FDG-PET and 11C-PiB-PET imaging, the former showing optimal performance based on the SPM optimized parametric assessment. Measures of brain glucose metabolism and amyloid load represent extremely powerful diagnostic and prognostic biomarkers with complementary roles in prodromal dementia phase, particularly when tailored to individual cases in clinical settings.

Neuroimaging in vascular cognitive impairment: a state-of-the-art review

Imaging is critical in the diagnosis and treatment of dementia, particularly in vascular cognitive impairment, due to the visualization of ischemic and hemorrhagic injury of gray and white matter. Magnetic resonance imaging (MRI) and positron emission tomography (PET) provide structural and functional information. Clinical MRI is both generally available and versatile - T2-weighted images show infarcts, FLAIR shows white matter changes and lacunar infarcts, and susceptibility-weighted images reveal microbleeds. Diffusion MRI adds another dimension by showing graded damage to white matter, making it more sensitive to white matter injury than FLAIR. Regions of neuroinflammatory disruption of the blood-brain barrier with increased permeability can be quantified and visualized with dynamic contrast-enhanced MRI. PET shows metabolism of glucose and accumulation of amyloid and tau, which is useful in showing abnormal metabolism in Alzheimer's disease. Combining MRI and PET allows identification of patients with mixed dementia, with MRI showing white matter injury and PET demonstrating regional impairment of glucose metabolism and deposition of amyloid. Excellent anatomical detail can be observed with 7.0-Tesla MRI. Imaging is the optimal method to follow the effect of treatments since changes in MRI scans are seen prior to those in cognition. This review describes the role of various imaging modalities in the diagnosis and treatment of vascular cognitive impairment.

Prion Diseases

PURPOSE OF REVIEW

This article presents an update on the clinical aspects of human prion disease, including the wide spectrum of their presentations.

RECENT FINDINGS

Prion diseases, a group of disorders caused by abnormally shaped proteins called prions, occur in sporadic (Jakob-Creutzfeldt disease), genetic (genetic Jakob-Creutzfeldt disease, Gerstmann-Sträussler-Scheinker syndrome, and fatal familial insomnia), and acquired (kuru, variant Jakob-Creutzfeldt disease, and iatrogenic Jakob-Creutzfeldt disease) forms. This article presents updated information on the clinical features and diagnostic methods for human prion diseases. New antemortem potential diagnostic tests based on amplifying prions in order to detect them are showing very high specificity. Understanding of the diversity of possible presentations of human prion diseases continues to evolve, with some genetic forms progressing slowly over decades, beginning with dysautonomia and neuropathy and progressing to a frontal-executive dementia with pathology of combined prionopathy and tauopathy. Unfortunately, to date, all human prion disease clinical trials have failed to show survival benefit. A very rare polymorphism in the prion protein gene recently has been identified that appears to protect against prion disease; this finding, in addition to providing greater understanding of the prionlike mechanisms of neurodegenerative disorders, might lead to potential treatments.

SUMMARY

Sporadic Jakob-Creutzfeldt disease is the most common form of human prion disease. Genetic prion diseases, resulting from mutations in the prion-related protein gene (PRNP), are classified based on the mutation, clinical phenotype, and neuropathologic features and can be difficult to diagnose because of their varied presentations. Perhaps most relevant to this Continuum issue on neuroinfectious diseases, acquired prion diseases are caused by accidental transmission to humans, but fortunately, they are the least common form and are becoming rarer as awareness of transmission risk has led to implementation of measures to prevent such occurrences.

The use of MRI, CT and lumbar puncture in dementia diagnostics: data from the SveDem Registry

BACKGROUND/AIMS

The use of structural brain imaging [computed tomography (CT)/magnetic resonance imaging (MRI)] and the analysis of cerebrospinal fluid biomarkers are included in the guidelines for the diagnosis of dementia. The influence of variables such as age, gender and disease severity on the use of MRI, CT and lumbar puncture (LP) for the differential diagnosis of dementia and the consonance with the recommendations of the Swedish national guidelines were investigated.

METHODS

From the National Swedish Dementia Registry (SveDem), 17,057 newly diagnosed dementia patients were included in our study, with the majority from specialist care units (90%).

RESULTS

In the diagnostic workup, a CT was performed in 87%, MRI in 16% and LP in 40% of the cases. Age (p < 0.001) and cognitive status (p < 0.001) significantly influenced the use of MRI, CT or LP. Older patients with severe dementia were often investigated with CT. LP and MRI were used more often when less common dementia disorders were suspected.

CONCLUSION

Our findings indicate that age, severity of cognitive impairment and the type of dementia disorder suspected are determinants for the choice of CT, MRI or LP. The majority of the dementia workups in specialist care units follow the recommendations of the Swedish national guidelines where CT is performed as a basic workup, and MRI and LP are chosen when extended workup is needed. .

Apolipoprotein E-dependent load of white matter hyperintensities in Alzheimer's disease: a voxel-based lesion mapping study

Introduction

White matter (WM) magnetic resonance imaging (MRI) hyperintensities are common in Alzheimer’s disease (AD), but their pathophysiological relevance and relationship to genetic factors are unclear. In the present study, we investigated potential apolipoprotein E (APOE)-dependent effects on the extent and cognitive impact of WM hyperintensities in patients with AD.

Methods

WM hyperintensity volume on fluid-attenuated inversion recovery images of 201 patients with AD (128 carriers and 73 non-carriers of the APOE ε4 risk allele) was determined globally as well as regionally with voxel-based lesion mapping. Clinical, neuropsychological and MRI data were collected from prospective multicenter trials conducted by the German Dementia Competence Network.

Results

WM hyperintensity volume was significantly greater in non-carriers of the APOE ε4 allele. Lesion distribution was similar among ε4 carriers and non-carriers. Only ε4 non-carriers showed a correlation between lesion volume and cognitive performance.

Conclusion

The current findings indicate an increased prevalence of WM hyperintensities in non-carriers compared with carriers of the APOE ε4 allele among patients with AD. This is consistent with a possibly more pronounced contribution of heterogeneous vascular risk factors to WM damage and cognitive impairment in patients with AD without APOE ε4-mediated risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-015-0111-8) contains supplementary material, which is available to authorized users.

Differential diagnosis of white matter lesions: Nonvascular causes-Part II

The knowledge of characteristic lesion patterns is important in daily practice imaging, as the radiologist increasingly is required to provide precise differential diagnosis despite unspecific clinical symptoms like cognitive impairment and missed elaborated neurological workup. This part II dealing with nonvascular white matter changes of proven cause and diagnostic significance aimed to assist the evaluation of diseases exhibiting lesions exclusively or predominantly located in the white matter. The etiologies commented on are classified as follows: (a) toxic-metabolic, (b) leukodystrophies and mitochondriopathies, (c) infectious, (d) neoplastic, and (e) immune mediated. The respective mode of lesion formation is characterized, and typical radiological findings are displayed. More or less symmetrical lesion patterns on the one hand as well as focal and multifocal ones on the other are to be analyzed with reference to clinical data and knowledge of predilection sites characterizing major disease categories. Complementing spinal cord imaging may be useful not only in acute and relapsing demyelinating diseases but in certain leukodystrophies as well. In neuromyelitis optica (NMO), the detection of a specific antibody and some recently published observations may lead to a new understanding of certain deep white matter lesions occasionally complicating systemic autoimmune disease.

Rapidly progressive dementia: prion diseases and other rapid dementias

Dementias that occur over weeks to months require a different differential than the slowly progressive dementias that occur over years. Because of the rapidity of decline in cognition, the evaluation of the patient is usually urgent and requires an extensive workup with multiple tests running in parallel. Creutzfeldt-Jakob disease, perhaps the prototypic rapidly progressive dementia (RPD), is often the first diagnosis many neurologists consider when faced with a patient with rapid cognitive decline. Many conditions other than prion disease, however, can present with RPD. Other etiologies include autoimmune conditions, cancers, uncommon presentations of other more common neurodegenerative dementias, and others. This chapter discusses some of the major etiologies for RPDs and offers an algorithm for diagnosis. Some topics, such as other neurodegenerative dementias and autoimmune dementias, are covered in other chapters in this issue, and will be mentioned here only briefly.

Correlating brain blood oxygenation level dependent (BOLD) fractal dimension mapping with magnetic resonance spectroscopy (MRS) in Alzheimer's disease

OBJECTIVES

To correlate temporal fractal structure of resting state blood oxygen level dependent (rsBOLD) functional magnetic resonance imaging (fMRI) with in vivo proton magnetic resonance spectroscopy ((1)H-MRS), in Alzheimer's disease (AD) and healthy age-matched normal controls (NC).

MATERIALS AND METHODS

High temporal resolution (4 Hz) rsBOLD signal and single voxel (left putamen) magnetic resonance spectroscopy data was acquired in 33 AD patients and 13 NC. The rsBOLD data was analyzed using two types of fractal dimension (FD) analysis based on relative dispersion and frequency power spectrum. Comparisons in FD were performed between AD and NC, and FD measures were correlated with (1)H-MRS findings.

RESULTS

Temporal fractal analysis of rsBOLD, was able to differentiate AD from NC subjects (P = 0.03). Low FD correlated with markers of AD severity including decreased concentrations of N-acetyl aspartate (R = 0.44, P = 0.015) and increased myoinositol (mI) (R = -0.45, P = 0.012).

CONCLUSION

Based on these results we suggest fractal analysis of rsBOLD could provide an early marker of AD.

Structural MRI

Clinical neuroimaging is increasingly being used in the diagnosis of neurodegenerative diseases and has become one of the most important paraclinical tools in the diagnosis of dementia. According to current guidelines, neuroimaging, preferably magnetic resonance imaging (MRI), should be performed at least once during the diagnostic work-up of patients with suspected or definite dementia. MRI is helpful in identifying or excluding potentially treatable causes of dementia; however, these account only for a small proportion of dementias. In addition, MRI is able to support the clinical diagnosis in a memory clinic setting by identifying certain patterns of atrophy and vascular damage. Visual rating scales are well-established methods in the clinical routine for the assessment and quantification of regional/global cortical atrophy, hippocampal atrophy and vascular damage. In addition, MRI is able to detect certain aspects of pathology associated with dementia, such as cerebral microbleeds which are related to cerebral amyloid angiopathy and Alzheimer pathology. This review paper aims to give an overview of the application of structural MRI in the diagnostic procedure for memory clinic patients in terms of excluding and supporting the diagnosis of various diseases associated with dementia.

[Perfusion brain SPECT and Alzheimer disease]

Alzheimer Disease (AD) is the most frequent cause of degenerative dementia. There is an asymptomatic phase of the disease. Brain single photon computed tomography (SPECT) is a simple way to investigate the cerebral blood flow. Alzheimer's disease is characterized by hypoperfusion in the medial temporal, associative posterior parietal cortex and frontal cortex. Brain SPECT could also have an interest in the early detection of amnesic mild cognitive impairment (MCI) patients with a high risk of conversion to AD. Indeed, the hypoperfusion of the associative parietal cortex in MCI patients is considered predictive of a rapid conversion to AD. Different scintigraphic patterns of neurodegenerative dementias could be used for their differential diagnosis.

Imaging in neurodegenerative disorders

This article discusses imaging findings of the cortical and subcortical neurodegenerative diseases. Spinocerebellar ataxia and neurodegenerative diseases secondary to vascular insults (multiinfarction dementia, Binswanger's disease, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, and amyloid angiopathy) are beyond the scope of this discussion.