Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET

Association of Insulin Resistance With Cerebral Glucose Uptake in Late Middle-Aged Adults at Risk for Alzheimer Disease

IMPORTANCE

Converging evidence suggests that Alzheimer disease (AD) involves insulin signaling impairment. Patients with AD and individuals at risk for AD show reduced glucose metabolism, as indexed by fludeoxyglucose F 18-labeled positron emission tomography (FDG-PET).

OBJECTIVES

To determine whether insulin resistance predicts AD-like global and regional glucose metabolism deficits in late middle-aged participants at risk for AD and to examine whether insulin resistance-predicted variation in regional glucose metabolism is associated with worse cognitive performance.

DESIGN, SETTING, AND PARTICIPANTS

This population-based, cross-sectional study included 150 cognitively normal, late middle-aged (mean [SD] age, 60.7 [5.8] years) adults from the Wisconsin Registry for Alzheimer's Prevention (WRAP) study, a general community sample enriched for AD parental history. Participants underwent cognitive testing, fasting blood draw, and FDG-PET at baseline. We used the homeostatic model assessment of peripheral insulin resistance (HOMA-IR). Regression analysis tested the statistical effect of HOMA-IR on global glucose metabolism. We used a voxelwise analysis to determine whether HOMA-IR predicted regional glucose metabolism. Finally, predicted variation in regional glucose metabolism was regressed against cognitive factors. Covariates included age, sex, body mass index, apolipoprotein E ε4 genotype, AD parental history status, and a reference region used to normalize regional uptake.

MAIN OUTCOMES AND MEASURES

Regional glucose uptake determined using FDG-PET and neuropsychological factors.

RESULTS

Higher HOMA-IR was associated with lower global glucose metabolism (β = -0.29; P < .01) and lower regional glucose metabolism across large portions of the frontal, lateral parietal, lateral temporal, and medial temporal lobes (P < .05, familywise error corrected). The association was especially robust in the left medial temporal lobe (R2 = 0.178). Lower glucose metabolism in the left medial temporal lobe predicted by HOMA-IR was significantly related to worse performance on the immediate memory (β = 0.317; t148 = 4.08; P < .001) and delayed memory (β = 0.305; t148 = 3.895; P < .001) factor scores.

CONCLUSIONS AND RELEVANCE

Our results show that insulin resistance, a prevalent and increasingly common condition in developed countries, is associated with significantly lower regional cerebral glucose metabolism, which in turn may predict worse memory performance. Midlife may be a critical period for initiating treatments to lower peripheral insulin resistance to maintain neural metabolism and cognitive function.

On Statistical Analysis of Neuroimages with Imperfect Registration

A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases. To do so, an important first step is to register (or co-register) all of the image data into a common coordinate system. This permits meaningful comparison of the intensities at each voxel across groups (e.g., diseased versus healthy) to evaluate the effects of the disease and/or use machine learning algorithms in a subsequent step. But errors in the underlying registration make this problematic, they either decrease the statistical power or make the follow-up inference tasks less effective/accurate. In this paper, we derive a novel algorithm which offers immunity to local errors in the underlying deformation field obtained from registration procedures. By deriving a deformation invariant representation of the image, the downstream analysis can be made more robust as if one had access to a (hypothetical) far superior registration procedure. Our algorithm is based on recent work on scattering transform. Using this as a starting point, we show how results from harmonic analysis (especially, non-Euclidean wavelets) yields strategies for designing deformation and additive noise invariant representations of large 3-D brain image volumes. We present a set of results on synthetic and real brain images where we achieve robust statistical analysis even in the presence of substantial deformation errors; here, standard analysis procedures significantly under-perform and fail to identify the true signal.

Amyloid-independent functional neural correlates of episodic memory in amnestic mild cognitive impairment

PURPOSE

Although amnestic mild cognitive impairment (aMCI) could have various biological characteristics, little attention has been given to the nature of episodic memory decline in aMCI with pathophysiologies other than Alzheimer's disease (AD), i.e., aMCI with low beta-amyloid (Aβ) burden. This study aimed to identify the functional neural basis of episodic memory impairment in aMCI with Aβ burden negative (aMCI-Aβ-) and to compare these results with aMCI with Aβ burden positive (aMCI-Aβ+).

METHODS

Individuals with aMCI (n = 498) were selected from the Alzheimer's Disease Neuroimaging Initiative database. Based on the mean florbetapir standard uptake value ratio, participants were classified as aMCI-Aβ- or aMCI-Aβ+. Correlations between memory scores and regional cerebral glucose metabolism (rCMglc) were analyzed separately for the two subgroups using a multiple regression model.

RESULTS

For aMCI-Aβ-, significant positive correlations between memory and rCMglc were found in the bilateral claustrum, right thalamus, left anterior cingulate cortex, left insula, and right posterior cingulate. For aMCI-Aβ+, significant positive correlations between memory and rCMglc were found in the temporoparietal areas. These correlation patterns remained unchanged when clinical severity was added as a covariate

CONCLUSION

Our findings indicate that memory impairment in aMCI-Aβ- is related to multimodal integrative processing and the attentional control system, whereas memory impairment in aMCI-Aβ+ is related to the typical brain memory systems and AD signature. These results suggest that although the two subgroups are clinically in the same category as aMCI, the memory impairment process depends on completely different functional brain regions according to their Aβ burden level.

Visual Versus Fully Automated Analyses of 18F-FDG and Amyloid PET for Prediction of Dementia Due to Alzheimer Disease in Mild Cognitive Impairment

Biomarkers of Alzheimer disease (AD) can be imaged in vivo and can be used for diagnostic and prognostic purposes in people with cognitive decline and dementia. Indicators of amyloid deposition such as (11)C-Pittsburgh compound B ((11)C-PiB) PET are primarily used to identify or rule out brain diseases that are associated with amyloid pathology but have also been deployed to forecast the clinical course. Indicators of neuronal metabolism including (18)F-FDG PET demonstrate the localization and severity of neuronal dysfunction and are valuable for differential diagnosis and for predicting the progression from mild cognitive impairment (MCI) to dementia. It is a matter of debate whether to analyze these images visually or using automated techniques. Therefore, we compared the usefulness of both imaging methods and both analyzing strategies to predict dementia due to AD.

METHODS

In MCI participants, a baseline examination, including clinical and imaging assessments, and a clinical follow-up examination after a planned interval of 24 mo were performed.

RESULTS

Of 28 MCI patients, 9 developed dementia due to AD, 2 developed frontotemporal dementia, and 1 developed moderate dementia of unknown etiology. The positive and negative predictive values and the accuracy of visual and fully automated analyses of (11)C-PiB for the prediction of progression to dementia due to AD were 0.50, 1.00, and 0.68, respectively, for the visual and 0.53, 1.00, and 0.71, respectively, for the automated analyses. Positive predictive value, negative predictive value, and accuracy of fully automated analyses of (18)F-FDG PET were 0.37, 0.78, and 0.50, respectively. Results of visual analyses were highly variable between raters but were superior to automated analyses.

CONCLUSION

Both (18)F-FDG and (11)C-PiB imaging appear to be of limited use for predicting the progression from MCI to dementia due to AD in short-term follow-up, irrespective of the strategy of analysis. On the other hand, amyloid PET is extremely useful to rule out underlying AD. The findings of the present study favor a fully automated method of analysis for (11)C-PiB assessments and a visual analysis by experts for (18)F-FDG assessments.

Multi-modality sparse representation-based classification for Alzheimer's disease and mild cognitive impairment

BACKGROUND AND OBJECTIVE

The discrimination of Alzheimer's disease (AD) and its prodromal stage known as mild cognitive impairment (MCI) from normal control (NC) is important for patients' timely treatment. The simultaneous use of multi-modality data has been demonstrated to be helpful for more accurate identification. The current study focused on extending a multi-modality algorithm and evaluating the method by identifying AD/MCI.

METHODS

In this study, sparse representation-based classification (SRC), a well-developed method in pattern recognition and machine learning, was extended to a multi-modality classification framework named as weighted multi-modality SRC (wmSRC). Data including three modalities of volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET from the Alzheimer's disease Neuroimaging Initiative database were adopted for AD/MCI classification (113 AD patients, 110 MCI patients and 117 NC subjects).

RESULTS

Adopting wmSRC, the classification accuracy achieved 94.8% for AD vs. NC, 74.5% for MCI vs. NC, and 77.8% for progressive MCI vs. stable MCI, superior to or comparable with the results of some other state-of-the-art models in recent multi-modality researches.

CONCLUSIONS

The wmSRC method is a promising tool for classification with multi-modality data. It could be effective for identifying diseases from NC with neuroimaging data, which could be helpful for the timely diagnosis and treatment of diseases.

Right Limbic FDG-PET Hypometabolism Correlates with Emotion Recognition and Attribution in Probable Behavioral Variant of Frontotemporal Dementia Patients

The behavioural variant of frontotemporal dementia (bvFTD) is a rare disease mainly affecting the social brain. FDG-PET fronto-temporal hypometabolism is a supportive feature for the diagnosis. It may also provide specific functional metabolic signatures for altered socio-emotional processing. In this study, we evaluated the emotion recognition and attribution deficits and FDG-PET cerebral metabolic patterns at the group and individual levels in a sample of sporadic bvFTD patients, exploring the cognitive-functional correlations. Seventeen probable mild bvFTD patients (10 male and 7 female; age 67.8±9.9) were administered standardized and validated version of social cognition tasks assessing the recognition of basic emotions and the attribution of emotions and intentions (i.e., Ekman 60-Faces test-Ek60F and Story-based Empathy task-SET). FDG-PET was analysed using an optimized voxel-based SPM method at the single-subject and group levels. Severe deficits of emotion recognition and processing characterized the bvFTD condition. At the group level, metabolic dysfunction in the right amygdala, temporal pole, and middle cingulate cortex was highly correlated to the emotional recognition and attribution performances. At the single-subject level, however, heterogeneous impairments of social cognition tasks emerged, and different metabolic patterns, involving limbic structures and prefrontal cortices, were also observed. The derangement of a right limbic network is associated with altered socio-emotional processing in bvFTD patients, but different hypometabolic FDG-PET patterns and heterogeneous performances on social tasks at an individual level exist.

Improving (18)F-Fluoro-D-Glucose-Positron Emission Tomography/Computed Tomography Imaging in Alzheimer's Disease Studies

The goal was to improve Alzheimer's 2-deoxy-2-¹⁸F-fluoro-D-glucose (¹⁸F FDG)-positron emission tomography (PET)/computed tomography (CT) imaging through application of a novel, hybrid Fourier-wavelet windowed Fourier transform (WFT) restoration technique, in order to provide earlier and more accurate clinical results. General Electric Medical Systems downward-looking sonar PET/CT 16 slice system was used to acquire studies. Patient data were acquired according the Alzheimer's disease Neuroimaging Initiative (ADNI) protocol. Here, we implemented Fourier-wavelet regularized restoration, with a Butterworth low-pass filter, order n = 6 and a cut-off frequency f = 0.35 cycles/pixel and wavelet (Daubechies, order 2) noise suppression. The original (PET-O) and restored (PET-R) ADNI subject PET images were compared using the Alzheimer's discrimination analysis by dedicated software. Forty-two PET/CT scans were used in the study. They were performed on eleven ADNI subjects at intervals of approximately 6 months. The final clinical diagnosis was used as a gold standard. For three subjects, the final clinical diagnosis was mild cognitive impairment and those 13 PET/CT studies were not included in the final comparison, as the result was considered as inconclusive. Using the reminding 29 PET/CT studies (23 AD and 6 normal), the sensitivity and specificity of the PET-O and PET-R were calculated. The sensitivity was 0.65 and 0.96 for PET-O and PET-R, respectively, and the specificity was 0.67 and 0.50 for PET-O and PET-R. The accuracy was 0.66 and 0.86 for PET-O and PET-R, respectively. The results of the study demonstrated that the accuracy of three-dimensional brain F-18 FDG PET images was significantly improved by Fourier-wavelet restoration filtering.

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.

Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease

BACKGROUND

The choice of biomarkers for early detection of Alzheimer's disease (AD) is important for improving the accuracy of imaging-based prediction of conversion from mild cognitive impairment (MCI) to AD. The primary goal of this study was to assess the effects of imaging modalities and brain atlases on prediction. We also investigated the influence of support vector machine recursive feature elimination (SVM-RFE) on predictive performance.

METHODS

Eighty individuals with amnestic MCI [40 developed AD within 3 years] underwent structural magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans at baseline. Using Automated Anatomical Labeling (AAL) and LONI Probabilistic Brain Atlas (LPBA40), we extracted features representing gray matter density and relative cerebral metabolic rate for glucose in each region of interest from the baseline MRI and FDG-PET data, respectively. We used linear SVM ensemble with bagging and computed the area under the receiver operating characteristic curve (AUC) as a measure of classification performance. We performed multiple SVM-RFE to compute feature ranking. We performed analysis of variance on the mean AUCs for eight feature sets.

RESULTS

The interactions between atlas and modality choices were significant. The main effect of SVM-RFE was significant, but the interactions with the other factors were not significant.

COMPARISON WITH EXISTING METHOD

Multimodal features were found to be better than unimodal features to predict AD. FDG-PET was found to be better than MRI.

CONCLUSIONS

Imaging modalities and brain atlases interact with each other and affect prediction. SVM-RFE can improve the predictive accuracy when using atlas-based features.

Synaptic change in the posterior cingulate gyrus in the progression of Alzheimer's disease

Mild cognitive impairment (MCI) is considered to be an early stage in the progression of Alzheimer's disease (AD) providing an opportunity to investigate brain pathogenesis prior to the onset of dementia. Neuroimaging studies have identified the posterior cingulate gyrus (PostC) as a cortical region affected early in the onset of AD. This association cortex is involved in a variety of different cognitive tasks and is intimately connected with the hippocampal/entorhinal cortex region, a component of the medial temporal memory circuit that displays early AD pathology. We quantified the total number of synapses in lamina 3 of the PostC using unbiased stereology coupled with electron microscopy from short postmortem autopsy tissue harvested from cases at different stage of AD progression. Individuals in the early stages of AD showed a significant decline in synaptic numbers compared to individuals with no cognitive impairment (NCI). Subjects with MCI exhibited synaptic numbers that were between the AD and NCI cohorts. Adjacent tissue was evaluated for changes in both pre and postsynaptic proteins levels. Individuals with MCI demonstrated a significant loss in presynaptic markers synapsin-1 and synaptophysin and postsynaptic markers PSD-95 and SAP-97. Levels of [3H]PiB binding was significantly increased in MCI and AD and correlated strongly with levels of synaptic proteins. All synaptic markers showed a significant association with Mini-Mental Status Examination scores. These results support the idea that the PostC synaptic function is affected during the prodromal stage of the disease and may underlie some of the early clinical sequelae associated with AD.

Complementarity of visual and voxel-based FDG-PET analysis to detect MCI-like hypometabolic pattern in elderly patients with hypertension and isolated memory complaints

BACKGROUND

18F-FDG PET can be used to aid in the diagnosis of Alzheimer's disease (AD) and clarify the diagnosis and prognosis of patients with mild cognitive impairment (MCI).

PURPOSE

To compare the results of a quantitative analysis of FDG-PET brain images to a standard visual analysis (SVA) with regards to the detection of MCI-like hypometabolic pattern in elderly patients with hypertension and subjective, isolated memory complaints.

MATERIAL AND METHODS

FDG-PET brain was performed in 71 patients (mean age, 76.4 ± 5.1 years; women, 53.5%). Images were analyzed for the presence of an MCI-like hypometabolic pattern using an SVA by 2 physicians and a voxel-based statistical procedure (statistical parametric mapping [SPM]) that compared each patient's images to normal reference samples from 19 elderly individuals obtained using the same PET camera. The reliability of these analyses was evaluated according to neuropsychological assessment results, including the Grober & Buschke Free and Cued Selective Reminding Test, and a combined analysis by a neuropsychologist.

RESULTS

An MCI-like hypometabolic pattern was documented in 5 patients (7%) by SVA and 7 patients (10%) by SPM analysis; however, only 2 of these patients were selected by both methods. The group characteristics of the 7 patients identified by the quantitative method were consistent with the MCI pattern, which included a higher rate of abnormal GB-FCSRT in Free Recall (57% vs. 9%, p < 0.05) or in Total Recall (29% vs. 8%, p < 0.05) when compared with other patients. In contrast, the group identified by SVA did not exhibit these characteristics.

CONCLUSION

A combined visual and quantitative analysis improves the diagnostic accuracy to detect an MCI-like hypometabolic pattern in elderly patients with hypertension and subjective, isolated memory complaints.

Reduced N-acetylaspartate to creatine ratio in the posterior cingulate correlates with cognition in Alzheimer's disease following four months of rivastigmine treatment

AIM

To determine whether 4 months of rivastigmine treatment would result in metabolic changes and whether metabolic changes correlate with changes in cognition in people with Alzheimer's disease (AD).

METHODS

Magnetic resonance spectra were acquired from the posterior cingulate cortex of subjects with AD at 3 T. Magnetic resonance imaging scans and cognitive tests were performed before and 4 months after the beginning of the treatment. Metabolite concentrations were quantified and used to calculate the metabolite ratios.

RESULTS

On average, the N-acetylaspartate/creatine (NAA/Cr) ratio decreased by 12.7% following 4 months of rivastigmine treatment, but changes in the NAA/Cr ratio correlated positively with changes in Mini-Mental State Examination scores.

CONCLUSION

This positive correlation between changes in NAA/Cr and changes in cognitive performance suggests that the NAA/Cr ratio could be an objective indicator of a response to rivastigmine treatment.

Arterial Spin-Labeling Parameters Influence Signal Variability and Estimated Regional Relative Cerebral Blood Flow in Normal Aging and Mild Cognitive Impairment: FAIR versus PICORE Techniques

BACKGROUND AND PURPOSE

Arterial spin-labeling is a noninvasive method to map cerebral blood flow, which might be useful for early diagnosis of neurodegenerative diseases. We directly compared 2 arterial spin-labeling techniques in healthy elderly controls and individuals with mild cognitive impairment.

MATERIALS AND METHODS

This prospective study was approved by the local ethics committee and included 198 consecutive healthy controls (mean age, 73.65 ± 4.02 years) and 43 subjects with mild cognitive impairment (mean age, 73.38 ± 5.85 years). Two pulsed arterial spin-labeling sequences were performed at 3T: proximal inversion with a control for off-resonance effects (PICORE) and flow-sensitive alternating inversion recovery technique (FAIR). Relative cerebral blood flow maps were calculated by using commercial software and standard parameters. Data analysis included spatial normalization of gray matter-corrected relative CBF maps, whole-brain average, and voxelwise comparison of both arterial spin-labeling sequences.

RESULTS

Overall, FAIR yielded higher relative CBF values compared with PICORE (controls, 32.7 ± 7.1 versus 30.0 ± 13.1 mL/min/100 g, P = .05; mild cognitive impairment, 29.8 ± 5.4 versus 26.2 ± 8.6 mL/min/100 g, P < .05; all, 32.2 ± 6.8 versus 29.3 ± 12.3 mL/min/100 g, P < .05). FAIR had lower variability (controls, 36.2% versus 68.8%, P < .00001; mild cognitive impairment, 18.9% versus 22.9%, P < .0001; all, 34.4% versus 64.9% P < .00001). The detailed voxelwise analysis revealed a higher signal for FAIR, notably in both convexities, while PICORE had higher signal predominantly in deep cerebral regions.

CONCLUSIONS

Overall, FAIR had higher estimated relative CBF and lower interindividual variability than PICORE. In more detail, there were regional differences between both arterial spin-labeling sequences. In summary, these results highlight the need to calibrate arterial spin-labeling sequences.

Recommendations for the use of PET imaging biomarkers in the diagnosis of neurodegenerative conditions associated with dementia: SEMNIM and SEN consensus

The new diagnostic criteria for Alzheimer's disease (AD) acknowledges the interest given to biomarkers to improve the specificity in subjects with dementia and to facilitate an early diagnosis of the pathophysiological process of AD in the prodromal or pre-dementia stage. The current availability of PET imaging biomarkers of synaptic dysfunction (PET-FDG) and beta amyloid deposition using amyloid-PET provides clinicians with the opportunity to apply the new criteria and improve diagnostic accuracy in their clinical practice. Therefore, it seems essential for the scientific societies involved to use the new clinical diagnostic support tools to establish clear, evidence-based and agreed set of recommendations for their appropriate use. The present work includes a systematic review of the literature on the utility of FDG-PET and amyloid-PET for the diagnosis of AD and related neurodegenerative diseases that occur with dementia. Thus, we propose a series of recommendations agreed on by the Spanish Society of Nuclear Medicine and Spanish Society of Neurology as a consensus statement on the appropriate use of PET imaging biomarkers.

Update on the core and developing cerebrospinal fluid biomarkers for Alzheimer disease

Alzheimer disease (AD) is a complex neurodegenerative disorder, whose prevalence will dramatically rise by 2050. Despite numerous clinical trials investigating this disease, there is still no effective treatment. Many trials showed negative or inconclusive results, possibly because they recruited only patients with severe disease, who had not undergone disease-modifying therapies in preclinical stages of AD before severe degeneration occurred. Detection of AD in asymptomatic at risk individuals (and a few presymptomatic individuals who carry an autosomal dominant monogenic AD mutation) remains impractical in many of clinical situations and is possible only with reliable biomarkers. In addition to early diagnosis of AD, biomarkers should serve for monitoring disease progression and response to therapy. To date, the most promising biomarkers are cerebrospinal fluid (CSF) and neuroimaging biomarkers. Core CSF biomarkers (amyloid β_1-42, total tau, and phosphorylated tau) showed a high diagnostic accuracy but were still unreliable for preclinical detection of AD. Hence, there is an urgent need for detection and validation of novel CSF biomarkers that would enable early diagnosis of AD in asymptomatic individuals. This article reviews recent research advances on biomarkers for AD, focusing mainly on the CSF biomarkers. In addition to core CSF biomarkers, the potential usefulness of novel CSF biomarkers is discussed.

Gray & white matter tissue contrast differentiates Mild Cognitive Impairment converters from non-converters

The clinical relevance of gray/white matter contrast ratio (GWR) in mild cognitive impairment (MCI) remains unknown. This study examined baseline GWR and 3-year follow-up diagnostic status in MCI. Alzheimer's Disease Neuroimaging Initiative MCI participants with baseline 1.5 T MRI and 3-year follow-up clinical data were included. Participants were categorized into two groups based on 3-year follow-up diagnoses: 1) non-converters (n = 69, 75 ± 7, 26 % female), and 2) converters (i.e., dementia at follow-up; n = 69, 75 ± 7, 30 % female) who were matched on baseline age and Mini-Mental State Examination scores. Groups were compared on FreeSurfer generated baseline GWR from structural images in which higher values represent greater tissue contrast. A general linear model, adjusting for APOE-status, scanner type, hippocampal volume, and cortical thickness, revealed that converters evidenced lower GWR values than non-converters (i.e., more degradation in tissue contrast; p = 0.03). Individuals with MCI who convert to dementia have lower baseline GWR values than individuals who remain diagnostically stable over a 3-year period, statistically independent of cortical thickness or hippocampal volume.

Resilience of precuneus neurotrophic signaling pathways despite amyloid pathology in prodromal Alzheimer's disease

BACKGROUND

Reduction of precuneus choline acetyltransferase activity co-occurs with greater beta-amyloid (Aβ) in Alzheimer's disease (AD). Whether this cholinergic deficit is associated with alteration in nerve growth factor (NGF) signaling and its relation to Aβ plaque and neurofibrillary tangle (NFT) pathology during disease onset is unknown.

METHODS

Precuneus NGF upstream and downstream signaling levels relative to Aβ and NFT pathology were evaluated using biochemistry and histochemistry in 62 subjects with a premortem diagnosis of non-cognitively impaired (NCI; n = 23), mild cognitive impairment (MCI; n = 21), and mild to moderate AD (n = 18).

RESULTS

Immunoblots revealed increased levels of proNGF in AD subjects but not MCI subjects, whereas cognate receptors were unchanged. There were no significant differences in protein level for the downstream survival kinase-signaling proteins Erk and phospho-Erk among groups. Apoptotic phospho-JNK, phospho-JNK/JNK ratio, and Bcl-2 were significantly elevated in AD subjects. Soluble Aβ1-42 and fibrillar Aβ measured by [(3)H] Pittsburgh compound-B ([(3)H]PiB) binding were significantly higher in AD subjects compared with MCI and NCI subjects. The density of plaques showed a trend to increase, but only 6-CN-PiB-positive plaques reached significance in AD subjects. AT8-positive, TOC-1-positive, and Tau C3-positive NFT densities were unchanged, whereas only AT8-positive neuropil thread density was statistically higher in AD subjects. A negative correlation was found between proNGF, phospho-JNK, and Bcl-2 levels and phospho-JNK/JNK ratio and cognition, whereas proNGF correlated positively with 6-CN-PiB-positive plaques during disease progression.

CONCLUSIONS

Data indicate that precuneus neurotrophin pathways are resilient to amyloid toxicity during the onset of AD.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

Brain: normal variations and benign findings in fluorodeoxyglucose-PET/computed tomography imaging

Brain 18F-fluorodeoxyglucose (18F-FDG) PET allows the in vivo study of cerebral glucose metabolism, reflecting neuronal and synaptic activity. 18F-FDG-PET has been extensively used to detect metabolic alterations in several neurologic diseases compared with normal aging. However, healthy subjects have variants of 18F-FDG distribution, especially as associated with aging. This article focuses on 18F-FDG-PET findings in so-called normal brain aging, and in particular on metabolic differences occurring with aging and as a function of people’s gender. The effect of different substances, medications, and therapy procedures are discussed, as well as common artifacts.

Functional neuroanatomy of auditory scene analysis in Alzheimer's disease

Auditory scene analysis is a demanding computational process that is performed automatically and efficiently by the healthy brain but vulnerable to the neurodegenerative pathology of Alzheimer's disease. Here we assessed the functional neuroanatomy of auditory scene analysis in Alzheimer's disease using the well-known 'cocktail party effect' as a model paradigm whereby stored templates for auditory objects (e.g., hearing one's spoken name) are used to segregate auditory 'foreground' and 'background'. Patients with typical amnestic Alzheimer's disease (n = 13) and age-matched healthy individuals (n = 17) underwent functional 3T-MRI using a sparse acquisition protocol with passive listening to auditory stimulus conditions comprising the participant's own name interleaved with or superimposed on multi-talker babble, and spectrally rotated (unrecognisable) analogues of these conditions. Name identification (conditions containing the participant's own name contrasted with spectrally rotated analogues) produced extensive bilateral activation involving superior temporal cortex in both the AD and healthy control groups, with no significant differences between groups. Auditory object segregation (conditions with interleaved name sounds contrasted with superimposed name sounds) produced activation of right posterior superior temporal cortex in both groups, again with no differences between groups. However, the cocktail party effect (interaction of own name identification with auditory object segregation processing) produced activation of right supramarginal gyrus in the AD group that was significantly enhanced compared with the healthy control group. The findings delineate an altered functional neuroanatomical profile of auditory scene analysis in Alzheimer's disease that may constitute a novel computational signature of this neurodegenerative pathology.

Early Alzheimer's disease neuropathology detected by proton MR spectroscopy

Proton magnetic resonance spectroscopy ((1)H-MRS) is sensitive to early neurodegenerative processes associated with Alzheimer's disease (AD). Although (1)H-MRS metabolite ratios of N-acetyl aspartate (NAA)/creatine (Cr), NAA/myoinositol (mI), and mI/Cr measured in the posterior cingulate gyrus reveal evidence of disease progression in AD, pathologic underpinnings of the (1)H-MRS metabolite changes in AD are unknown. Pathologically diagnosed human cases ranging from no likelihood to high likelihood AD (n = 41, 16 females and 25 males) who underwent antemortem (1)H-MRS of the posterior cingulate gyrus at 3 tesla were included in this study. Immunohistochemical evaluation was performed on the posterior cingulate gyrus using antibodies to synaptic vesicles, hyperphosphorylated tau (pTau), neurofibrillary tangle conformational-epitope (cNFT), amyloid-β, astrocytes, and microglia. The slides were digitally analyzed using Aperio software, which allows neuropathologic quantification in the posterior cingulate gray matter. MRS and pathology associations were adjusted for time from scan to death. Significant associations across AD and control subjects were found between reduced synaptic immunoreactivity and both NAA/Cr and NAA/mI in the posterior cingulate gyrus. Higher pTau burden was associated with lower NAA/Cr and NAA/mI. Higher amyloid-β burden was associated with elevated mI/Cr and lower NAA/mI ratios, but not with NAA/Cr. (1)H-MRS metabolite levels reveal early neurodegenerative changes associated with AD pathology. Our findings support the hypothesis that a decrease in NAA/Cr is associated with loss of synapses and early pTau pathology, but not with amyloid-β or later accumulation of cNFT pathology in the posterior cingulate gyrus. In addition, elevation of mI/Cr is associated with the occurrence of amyloid-β plaques in AD.

Mild cognitive impairment with suspected nonamyloid pathology (SNAP): Prediction of progression

OBJECTIVES

The aim of this study was to investigate predictors of progressive cognitive deterioration in patients with suspected non-Alzheimer disease pathology (SNAP) and mild cognitive impairment (MCI).

METHODS

We measured markers of amyloid pathology (CSF β-amyloid 42) and neurodegeneration (hippocampal volume on MRI and cortical metabolism on [(18)F]-fluorodeoxyglucose-PET) in 201 patients with MCI clinically followed for up to 6 years to detect progressive cognitive deterioration. We categorized patients with MCI as A+/A- and N+/N- based on presence/absence of amyloid pathology and neurodegeneration. SNAPs were A-N+ cases.

RESULTS

The proportion of progressors was 11% (8/41), 34% (14/41), 56% (19/34), and 71% (60/85) in A-N-, A+N-, SNAP, and A+N+, respectively; the proportion of APOE ε4 carriers was 29%, 70%, 31%, and 71%, respectively, with the SNAP group featuring a significantly different proportion than both A+N- and A+N+ groups (p ≤ 0.005). Hypometabolism in SNAP patients was comparable to A+N+ patients (p = 0.154), while hippocampal atrophy was more severe in SNAP patients (p = 0.002). Compared with A-N-, SNAP and A+N+ patients had significant risk of progressive cognitive deterioration (hazard ratio = 2.7 and 3.8, p = 0.016 and p < 0.001), while A+N- patients did not (hazard ratio = 1.13, p = 0.771). In A+N- and A+N+ groups, none of the biomarkers predicted time to progression. In the SNAP group, lower time to progression was correlated with greater hypometabolism (r = 0.42, p = 0.073).

CONCLUSIONS

Our findings support the notion that patients with SNAP MCI feature a specific risk progression profile.

¹⁸F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-FDFG uptake by brain tissue as measured by positron emission tomography (PET) is a well-established method for assessment of brain function in people with dementia. Certain findings on brain PET scans can potentially predict the decline of mild cognitive Impairment (MCI) to Alzheimer's disease dementia or other dementias.

OBJECTIVES

To determine the diagnostic accuracy of the ¹⁸F-FDG PET index test for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease dementia or other forms of dementia at follow-up.

SEARCH METHODS

We searched the Cochrane Register of Diagnostic Test Accuracy Studies, MEDLINE, EMBASE, Science Citation Index, PsycINFO, BIOSIS previews, LILACS, MEDION, (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases to January 2013. We checked the reference lists of any relevant studies and systematic reviews for additional studies.

SELECTION CRITERIA

We included studies that evaluated the diagnostic accuracy of ¹⁸F-FDG PET to determine the conversion from MCI to Alzheimer's disease dementia or to other forms of dementia, i.e. any or all of vascular dementia, dementia with Lewy bodies, and fronto-temporal dementia. These studies necessarily employ delayed verification of conversion to dementia and are sometimes labelled as 'delayed verification cross-sectional studies'.

DATA COLLECTION AND ANALYSIS

Two blinded review authors independently extracted data, resolving disagreement by discussion, with the option to involve a third review author as arbiter if necessary. We extracted and summarised graphically the data for two-by-two tables. We conducted exploratory analyses by plotting estimates of sensitivity and specificity from each study on forest plots and in receiver operating characteristic (ROC) space. When studies had mixed thresholds, we derived estimates of sensitivity and likelihood ratios at fixed values (lower quartile, median and upper quartile) of specificity from the hierarchical summary ROC (HSROC) models.

MAIN RESULTS

We included 14 studies (421 participants) in the analysis. The sensitivities for conversion from MCI to Alzheimer's disease dementia were between 25% and 100% while the specificities were between 15% and 100%. From the summary ROC curve we fitted we estimated that the sensitivity was 76% (95% confidence interval (CI): 53.8 to 89.7) at the included study median specificity of 82%. This equates to a positive likelihood ratio of 4.03 (95% CI: 2.97 to 5.47), and a negative likelihood ratio of 0.34 (95% CI: 0.15 to 0.75). Three studies recruited participants from the same Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort but only the largest ADNI study (Herholz 2011) is included in the meta-analysis. In order to demonstrate whether the choice of ADNI study or discriminating brain region (Chételat 2003) or reader assessment (Pardo 2010) make a difference to the pooled estimate, we performed five additional analyses. At the median specificity of 82%, the estimated sensitivity was between 74% and 76%. There was no impact on our findings. In addition to evaluating Alzheimer's disease dementia, five studies evaluated the accuracy of ¹⁸F-FDG PET for all types of dementia. The sensitivities were between 46% and 95% while the specificities were between 29% and 100%; however, we did not conduct a meta-analysis because of too few studies, and those studies which we had found recruited small numbers of participants. Our findings are based on studies with poor reporting, and the majority of included studies had an unclear risk of bias, mainly for the reference standard and participant selection domains. According to the assessment of Index test domain, more than 50% of studies were of poor methodological quality.

AUTHORS' CONCLUSIONS

It is difficult to determine to what extent the findings from the meta-analysis can be applied to clinical practice. Given the considerable variability of specificity values and lack of defined thresholds for determination of test positivity in the included studies, the current evidence does not support the routine use of ¹⁸F-FDG PET scans in clinical practice in people with MCI. The ¹⁸F-FDG PET scan is a high-cost investigation, and it is therefore important to clearly demonstrate its accuracy and to standardise the process of ¹⁸F-FDG PET diagnostic modality prior to its being widely used. Future studies with more uniform approaches to thresholds, analysis and study conduct may provide a more homogeneous estimate than the one available from the included studies we have identified.

White matter disruption at the prodromal stage of Alzheimer's disease: relationships with hippocampal atrophy and episodic memory performance

White matter tract alterations have been consistently described in Alzheimer's disease (AD). In particular, limbic fronto-temporal connections, which are critical to episodic memory function, may degenerate early in the course of the disease. However the relation between white matter tract degeneration, hippocampal atrophy and episodic memory impairment at the earliest stages of AD is still unclear. In this magnetic resonance imaging study, white matter integrity and hippocampal volumes were evaluated in patients with amnestic mild cognitive impairment due to AD (Albert et al., 2011) (n = 22) and healthy controls (n = 15). Performance in various episodic memory tasks was also evaluated in each participant. Relative to controls, patients showed a significant reduction of white matter fractional anisotropy (FA) and increase of radial diffusivity (RD) in the bilateral uncinate fasciculus, parahippocampal cingulum and fornix. Within the patient group, significant intra-hemispheric correlations were notably found between hippocampal grey matter volume and FA in the uncinate fasciculus, suggesting a relationship between atrophy and disconnection of the hippocampus. Moreover, episodic recognition scores were related with uncinate fasciculus FA across patients. These results indicate that fronto-hippocampal connectivity is reduced from the earliest pre-demential stages of AD. Disruption of fronto-hippocampal connections may occur progressively, in parallel with hippocampal atrophy, and may specifically contribute to early initial impairment in episodic memory.

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Limbic fronto-temporal connections (cingulum, uncinate fasciculus and fornix) are altered from the prodromal stage of AD.

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In prodromal AD patients, intra-hemispheric correlations were found between uncinate fasciculus FA and hippocampal atrophy.

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In prodromal AD patients, uncinate fasciculus FA was correlated with scores on episodic recognition.

The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET

Although amyloid imaging with PiB-PET ([C-11]Pittsburgh Compound-B positron emission tomography), and now with F-18-labeled tracers, has produced remarkably consistent qualitative findings across a large number of centers, there has been considerable variability in the exact numbers reported as quantitative outcome measures of tracer retention. In some cases this is as trivial as the choice of units, in some cases it is scanner dependent, and of course, different tracers yield different numbers. Our working group was formed to standardize quantitative amyloid imaging measures by scaling the outcome of each particular analysis method or tracer to a 0 to 100 scale, anchored by young controls (≤ 45 years) and typical Alzheimer's disease patients. The units of this scale have been named "Centiloids." Basically, we describe a "standard" method of analyzing PiB PET data and then a method for scaling any "nonstandard" method of PiB PET analysis (or any other tracer) to the Centiloid scale.

Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia

[¹⁸F]FDG-PET imaging has been recognized as a crucial diagnostic marker in Mild Cognitive Impairment (MCI), supporting the presence or the exclusion of Alzheimer's Disease (AD) pathology. A clinical heterogeneity, however, underlies MCI definition. In this study, we aimed to evaluate the predictive role of single-subject voxel-based maps of [¹⁸F]FDG distribution generated through statistical parametric mapping (SPM) in the progression to different dementia subtypes in a sample of 45 MCI. Their scans were compared to a large normal reference dataset developed and validated for comparison at single-subject level. Additionally, Aβ42 and Tau CSF values were available in 34 MCI subjects. Clinical follow-up (mean 28.5 ± 7.8 months) assessed subsequent progression to AD or non-AD dementias. The SPM analysis showed: 1) normal brain metabolism in 14 MCI cases, none of them progressing to dementia; 2) the typical temporo-parietal pattern suggestive for prodromal AD in 15 cases, 11 of them progressing to AD; 3) brain hypometabolism suggestive of frontotemporal lobar degeneration (FTLD) subtypes in 7 and dementia with Lewy bodies (DLB) in 2 subjects (all fulfilled FTLD or DLB clinical criteria at follow-up); and 4) 7 MCI cases showed a selective unilateral or bilateral temporo-medial hypometabolism without the typical AD pattern, and they all remained stable. In our sample, objective voxel-based analysis of [¹⁸F]FDG-PET scans showed high predictive prognostic value, by identifying either normal brain metabolism or hypometabolic patterns suggestive of different underlying pathologies, as confirmed by progression at follow-up. These data support the potential usefulness of this SPM [¹⁸F]FDG PET analysis in the early dementia diagnosis and for improving subject selection in clinical trials based on MCI definition.

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We used an optimized voxel-based single-subject [¹⁸F]FDG-PET analysis

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We showed different hypometabolic patterns (AD and non-AD) underlying MCI condition

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Heterogeneous PET profiles predicted progression into specific dementia subtypes.

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Statistical analyses showed high positive and negative post-test probability values.

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CSF findings agreed with [¹⁸F]FDG-PET imaging in single cases.

Autopsy as Gold Standard in FDG-PET Studies in Dementia

Positron emission tomography (PET) imaging with F18-fluorodeoxyglucose (FDG) is increasingly used as an adjunct to clinical evaluation in the diagnosis of dementia. Considering that most FDG-PET studies in dementia use clinical diagnosis as gold standard and that clinical diagnosis is approximately 80% sensitive or accurate, we aim to review the evidence-based data on the diagnostic accuracy of brain FDG-PET in dementia when cerebral autopsy is used as gold standard. We searched the PubMed and Medline databases for dementia-related articles that correlate histopathological diagnosis at autopsy with FDG-PET imaging and found 47 articles among which there were only 5 studies of 20 patients or more. We were able to conclude that sensitivity and specificity of FDG-PET for Alzheimer's disease are good, but more studies using histopathological diagnosis at autopsy as gold standard are needed in order to evaluate what FDG-PET truly adds to premortem diagnostic accuracy in dementia.

Multimodality imaging of Alzheimer disease and other neurodegenerative dementias

Neurodegenerative diseases, such as Alzheimer disease, result in cognitive decline and dementia and are a leading cause of mortality in the growing elderly population. These progressive diseases typically have an insidious onset, with overlapping clinical features early in the disease course that make diagnosis challenging. The neurodegenerative diseases are associated with characteristic, although not completely understood, changes in the brain: abnormal protein deposition, synaptic dysfunction, neuronal injury, and neuronal death. Neuroimaging biomarkers-principally regional atrophy on structural MR imaging, patterns of hypometabolism on (18)F-FDG PET, and detection of cerebral amyloid plaque on amyloid PET--are able to evaluate the patterns of these abnormalities in the brain to improve early diagnosis and help predict the disease course. These techniques have unique strengths and synergies in multimodality evaluation of the patient with cognitive decline or dementia. This review discusses the key imaging biomarkers from MR imaging, (18)F-FDG PET, and amyloid PET; the imaging features of the most common neurodegenerative dementias; the role of various neuroimaging studies in differential diagnosis and prognosis; and some promising imaging techniques under development.

Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study

An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine (SVM) and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.

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¹⁸F-FDG-PET/CT analysis of metabolic differences between MCI converting to AD and HC

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Large and very well controlled cohorts from EADC-Consortium were investigated.

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Data were analyzed by a friendly-to-use Matlab-based script and Support Vector Machine.

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Excellent discrimination between MCI and HC (sensitivity 92%; specificity 91%)

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Highest accuracy reported so far in MCI and promising implementation in clinical routine

Using Copula distributions to support more accurate imaging-based diagnostic classifiers for neuropsychiatric disorders

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increase the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone.

The added value of 18-fluorodeoxyglucose-positron emission tomography in the diagnosis of the behavioral variant of frontotemporal dementia

Characteristic frontotemporal abnormalities on structural or functional neuroimaging are mandatory for a diagnosis of probable behavioral variant of frontotemporal dementia (bvFTD) according to the new criteria. 18-Fluorodeoxyglucose-positron emission tomography (18F-FDG-PET) imaging is commonly reserved for patients with suspected bvFTD without characteristic structural neuroimaging results. We studied the diagnostic value of 18F-FDG-PET in these patients.

METHODS

The 18F-FDG-PET was performed in 52 patients with suspected bvFTD but lacking characteristic structural neuroimaging results. The clinical diagnosis of bvFTD in the presence of functional decline (bvFTD/fd+) after a follow-up period of 2 years was used as a golden standard.

RESULTS

The sensitivity of 18F-FDG-PET for bvFTD/fd+ was 47% at a specificity of 92%. The differential diagnosis comprised alternative neurodegenerative and psychiatric disorders and a benign phenocopy of bvFTD.

CONCLUSIONS

The 18F-FDG-PET is able to identify nearly half of the patients with bvFTD who remain undetected by magnetic resonance imaging. In our selected group, high specificity enables exclusion of psychiatric and other neurodegenerative disorders.

Nonamyloid PET biomarkers and Alzheimer's disease: current and future perspectives

ABSTRACT 

Recent advances in neurobiology and PET have helped redefine Alzheimer's disease (AD) as a dynamic pathophysiological process, clinically characterized by preclinical, mild cognitive impairment due to AD and dementia stages. Though a majority of PET studies conducted within these populations have to date focused on β-amyloid, various ‘nonamyloid’ radiopharmaceuticals exist for evaluating neurodegeneration, neuroinflammation and perturbations in neurotransmission across the spectrum of AD. Importantly, findings using such tracers have been shown to correlate with various clinical, cognitive and behavioral measures. In the context of a growing shift toward early diagnosis and symptomatic and disease-modifying clinical trials, nonamyloid PET radiotracers will prove of use, and, potentially, contribute to improved therapeutic prospects for AD.

Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting

Diagnostic accuracy in FDG-PET imaging highly depends on the operating procedures. In this clinical study on dementia, we compared the diagnostic accuracy at a single-subject level of a) Clinical Scenarios, b) Standard FDG Images and c) Statistical Parametrical (SPM) Maps generated via a new optimized SPM procedure. We evaluated the added value of FDG-PET, either Standard FDG Images or SPM Maps, to Clinical Scenarios. In 88 patients with neurodegenerative diseases (Alzheimer's Disease—AD, Frontotemporal Lobar Degeneration—FTLD, Dementia with Lewy bodies—DLB and Mild Cognitive Impairment—MCI), 9 neuroimaging experts made a forced diagnostic decision on the basis of the evaluation of the three types of information. There was also the possibility of a decision of normality on the FDG-PET images. The clinical diagnosis confirmed at a long-term follow-up was used as the gold standard. SPM Maps showed higher sensitivity and specificity (96% and 84%), and better diagnostic positive (6.8) and negative (0.05) likelihood ratios compared to Clinical Scenarios and Standard FDG Images. SPM Maps increased diagnostic accuracy for differential diagnosis (AD vs. FTD; beta 1.414, p = 0.019). The AUC of the ROC curve was 0.67 for SPM Maps, 0.57 for Clinical Scenarios and 0.50 for Standard FDG Images. In the MCI group, SPM Maps showed the highest predictive prognostic value (mean LOC = 2.46), by identifying either normal brain metabolism (exclusionary role) or hypometabolic patterns typical of different neurodegenerative conditions.

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Brain FDG-PET was evaluated with a new optimized SPM procedure in dementias.

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We compared the diagnostic accuracy of clinical information, visual and SPM FDG-PET.

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SPM had the best sensitivity (96%), specificity (84%) and positive and negative LR.

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In an MCI subgroup, SPM had the highest predictive prognostic value.

Evaluation of neuroprotective effect of glucagon-like peptide 1 analogs using neuroimaging

There is increasing evidence to suggest that glucagon-like peptide 1 (GLP1) analogs are neuroprotective in animal models. In transgenic mice, both insulin and GLP1 analogs reduced inflammation, increased stem cell proliferation, reduced apoptosis, and increased dendritic growth. Furthermore, insulin desensitization was also observed in these animals, and reduced glucose uptake in the brain, as shown on FDG-PET imaging. In this review we discussed the role of PET and MRI in evaluating the effect of GLP1 analogs in disease progression in both Alzheimer's and Parkinson's disease. We have also discussed the potential novel PET markers that will allow us to understand the mechanism by which GLP1 exerts its effects.

Memory loss in Alzheimer's disease

Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD) and by their caretakers. Working memory and long-term declarative memory are affected early during the course of the disease. The individual pattern of impaired memory functions correlates with parameters of structural or functional brain integrity. AD pathology interferes with the formation of memories from the molecular level to the framework of neural networks. The investigation of AD memory loss helps to identify the involved neural structures, such as the default mode network, the influence of epigenetic and genetic factors, such as ApoE4 status, and evolutionary aspects of human cognition. Clinically, the analysis of memory assists the definition of AD subtypes, disease grading, and prognostic predictions. Despite new AD criteria that allow the earlier diagnosis of the disease by inclusion of biomarkers derived from cerebrospinal fluid or hippocampal volume analysis, neuropsychological testing remains at the core of AD diagnosis.

The use of biomarkers for the etiologic diagnosis of MCI in Europe: an EADC survey

We investigated the use of Alzheimer's disease (AD) biomarkers in European Alzheimer's Disease Consortium centers and assessed their perceived usefulness for the etiologic diagnosis of mild cognitive impairment (MCI). We surveyed availability, frequency of use, and confidence in diagnostic usefulness of markers of brain amyloidosis (amyloid positron emission tomography [PET], cerebrospinal fluid [CSF] Aβ42) and neurodegeneration (medial temporal atrophy [MTA] on MR, fluorodeoxyglucose positron emission tomography [FDG-PET], CSF tau). The most frequently used biomarker is visually rated MTA (75% of the 37 responders reported using it "always/frequently") followed by CSF markers (22%), FDG-PET (16%), and amyloid-PET (3%). Only 45% of responders perceive MTA as contributing to diagnostic confidence, where the contribution was rated as "moderate". Seventy-nine percent of responders felt "very/extremely" comfortable delivering a diagnosis of MCI due to AD when both amyloid and neuronal injury biomarkers were abnormal (P < .02 versus any individual biomarker). Responders largely agreed that a combination of amyloidosis and neuronal injury biomarkers was a strongly indicative AD signature.