Optimization of Statistical Single Subject Analysis of Brain FDG PET for the Prognosis of Mild Cognitive Impairment-to-Alzheimer's Disease Conversion

Increased [18F]Fluorodeoxyglucose Uptake in the Left Pallidum in Military Veterans with Blast-Related Mild Traumatic Brain Injury: Potential as an Imaging Biomarker and Mediation with Executive Dysfunction and Cognitive Impairment

Blast-related mild traumatic brain injury (blast-mTBI) can result in a spectrum of persistent symptoms leading to substantial functional impairment and reduced quality of life. Clinical evaluation and discernment from other conditions common to military service can be challenging and subject to patient recall bias and the limitations of available assessment measures. The need for objective biomarkers to facilitate accurate diagnosis, not just for symptom management and rehabilitation but for prognostication and disability compensation purposes is clear. Toward this end, we compared regional brain [18F]fluorodeoxyglucose-positron emission tomography ([18F]FDG-PET) intensity-scaled uptake measurements and motor, neuropsychological, and behavioral assessments in 79 combat Veterans with retrospectively recalled blast-mTBI with 41 control participants having no lifetime history of TBI. Using an agnostic and unbiased approach, we found significantly increased left pallidum [18F]FDG-uptake in Veterans with blast-mTBI versus control participants, p < 0.0001; q = 3.29 × 10-9 [Cohen's d, 1.38, 95% confidence interval (0.96, 1.79)]. The degree of left pallidum [18F]FDG-uptake correlated with the number of self-reported blast-mTBIs, r2 = 0.22; p < 0.0001. Greater [18F]FDG-uptake in the left pallidum provided excellent discrimination between Veterans with blast-mTBI and controls, with a receiver operator characteristic area under the curve of 0.859 (p < 0.0001) and likelihood ratio of 21.19 (threshold:SUVR ≥ 0.895). Deficits in executive function assessed using the Behavior Rating Inventory of Executive Function-Adult Global Executive Composite T-score were identified in Veterans with blast-mTBI compared with controls, p < 0.0001. Regression-based mediation analyses determined that in Veterans with blast-mTBI, increased [18F]FDG-uptake in the left pallidum-mediated executive function impairments, adjusted causal mediation estimate p = 0.021; total effect estimate, p = 0.039. Measures of working and prospective memory (Auditory Consonant Trigrams test and Memory for Intentions Test, respectively) were negatively correlated with left pallidum [18F]FDG-uptake, p < 0.0001, with mTBI as a covariate. Increased left pallidum [18F]FDG-uptake in Veterans with blast-mTBI compared with controls did not covary with dominant handedness or with motor activity assessed using the Unified Parkinson's Disease Rating Scale. Localized increased [18F]FDG-uptake in the left pallidum may reflect a compensatory response to functional deficits following blast-mTBI. Limited imaging resolution does not allow us to distinguish subregions of the pallidum; however, the significant correlation of our data with behavioral but not motor outcomes suggests involvement of the ventral pallidum, which is known to regulate motivation, behavior, and emotions through basal ganglia-thalamo-cortical circuits. Increased [18F]FDG-uptake in the left pallidum in blast-mTBI versus control participants was consistently identified using two different PET scanners, supporting the generalizability of this finding. Although confirmation of our results by single-subject-to-cohort analyses will be required before clinical deployment, this study provides proof of concept that [18F]FDG-PET bears promise as a readily available noninvasive biomarker for blast-mTBI. Further, our findings support a causative relationship between executive dysfunction and increased [18F]FDG-uptake in the left pallidum.

Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease

Previous studies have demonstrated resilience to AD-related neuropathology in a form of cognitive reserve (CR). In this study we investigated a relationship between CR and hypometabolic subtypes of AD, specifically the typical and the limbic-predominant subtypes. We analyzed data from 59 Aβ-positive cognitively normal (CN), 221 prodromal Alzheimer's disease (AD) and 174 AD dementia participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) from ADNI and ADNIGO/2 phases. For replication, we analyzed data from 5 Aβ-positive CN, 89 prodromal AD and 43 AD dementia participants from ADNI3. CR was estimated as standardized residuals in a model predicting cognition from temporoparietal grey matter volumes and covariates. Higher CR estimates predicted slower cognitive decline. Typical and limbic-predominant hypometabolic subtypes demonstrated similar baseline CR, but the results suggested a faster decline of CR in the typical subtype. These findings support the relationship between subtypes and CR, specifically longitudinal trajectories of CR. Results also underline the importance of longitudinal analyses in research on CR.

Individualized Summary Assessment of Detailed Neuropsychological Testing for the Etiological Diagnosis of Newly Detected Cognitive Impairment in Hospitalized Geriatric Patients

BACKGROUND

Neuropsychological testing (NPT) of geriatric inpatients can be affected by the acute illness and/or the hospitalization.

OBJECTIVE

To test individualized interpretation of detailed NPT for the differentiation between primary 'neurodegenerative' etiologies (predominantly Alzheimer's disease) and 'other' etiologies (including cerebrovascular disease) of newly detected cognitive impairment in geriatric inpatients without and with delirium in remission.

METHODS

96 geriatric inpatients (81.9±5.6 years, 64.6% females) with clinically uncertain cognitive impairment were included. 31.3% had delirium in remission that was not considered the primary cause of the cognitive impairment. Categorization of the most likely etiology as 'neurodegenerative' or 'other' was established retrospectively by a study neuropsychologist based on individualized summary assessment of detailed NPT compiled in a standardized vignette. The etiological diagnosis based on FDG-PET served as gold standard (54.2% 'neurodegenerative', 45.8% 'other').

RESULTS

Individualized summary assessment by the study neuropsychologist was correct in 80 patients (83.3%, 8 false positive, 8 false negative). The impact of delirium in remission was not significant (p = 0.237). Individualized summary assessment by an independent neuropsychologist resulted in more false positive cases (n = 22) at the same rate of false negative cases (n = 8). Automatic categorization with a decision tree model based on the most discriminative NPT scores was correct in 68 patients (70.8%, 14 false positive, 14 false negative).

CONCLUSION

Individualized summary assessment of detailed NPT in the context of relevant clinical information might be useful for the etiological diagnosis of newly detected cognitive impairment in hospitalized geriatric patients, also in patients with delirium in remission, but requires task-specific expertise.

Differential diagnosis of amnestic dementia patients based on an FDG-PET signature of autopsy-confirmed LATE-NC

INTRODUCTION

Limbic age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is common in advanced age and can underlie a clinical presentation mimicking Alzheimer's disease (AD). We studied whether an autopsy-derived fluorodeoxyglucose positron emission tomography (FDG-PET) signature of LATE-NC provides clinical utility for differential diagnosis of amnestic dementia patients.

METHODS

Ante mortem FDG-PET patterns from autopsy-confirmed LATE-NC (N = 7) and AD (N = 23) patients were used to stratify an independent cohort of clinically diagnosed AD dementia patients (N = 242) based on individual FDG-PET profiles.

RESULTS

Autopsy-confirmed LATE-NC and AD groups showed markedly distinct temporo-limbic and temporo-parietal FDG-PET patterns, respectively. Clinically diagnosed AD dementia patients showing a LATE-NC-like FDG-PET pattern (N = 25, 10%) were significantly older, showed less abnormal AD biomarker levels, lower APOE ε4, and higher TMEM106B risk allele load. Clinically, they exhibited a more memory-predominant profile and a generally slower disease course.

DISCUSSION

An autopsy-derived temporo-limbic FDG-PET signature identifies older amnestic patients whose clinical, genetic, and molecular biomarker features are consistent with underlying LATE-NC.

Stereotactical normalization with multiple templates representative of normal and Parkinson-typical reduction of striatal uptake improves the discriminative power of automatic semi-quantitative analysis in dopamine transporter SPECT

BACKGROUND

The specific binding ratio (SBR) of ¹²³I-FP-CIT in the putamen is widely used to support the interpretation of dopamine transporter (DAT) SPECT. Automatic methods for computation of the putamen SBR often include stereotactical normalization of the individual DAT-SPECT image to an anatomical standard space. This study compared using a single ¹²³I-FP-CIT template image as target for stereotactical normalization versus multiple templates representative of normal and different levels of Parkinson-typical reduction of striatal ¹²³I-FP-CIT uptake.

METHODS

1702 clinical ¹²³I-FP-CIT SPECT images were stereotactically normalized (affine) to the anatomical space of the Montreal Neurological Institute (MNI) with SPM12 either using a single custom-made ¹²³I-FP-CIT template representative of normal striatal uptake or using eight different templates representative of normal and different levels of Parkinson-typical reduction of striatal FP-CIT uptake with and without attenuation and scatter correction. In the latter case, SPM finds the linear combination of the multiple templates that best matches the patient's image. The putamen SBR was obtained using hottest voxels analysis in large unilateral regions-of-interest predefined in MNI space. The histogram of the putamen SBR in the whole sample was fitted by the sum of two Gaussians. The power to differentiate between reduced and normal SBR was estimated by the effect size of the distance between the two Gaussians computed as the differences between their mean values scaled to their pooled standard deviation.

RESULTS

The effect size of the distance between the two Gaussians was 3.83 with the single template versus 3.96 with multiple templates for stereotactical normalization.

CONCLUSIONS

Multiple templates representative of normal and different levels of Parkinson-typical reduction for stereotactical normalization of DAT-SPECT might provide improved separation between normal and reduced putamen SBR that could result in slightly improved power for the detection of nigrostriatal degeneration.

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

OBJECTIVE

We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans.

METHODS

In this study, we selected tau-PET scans from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments.

RESULTS

Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD.

CONCLUSIONS

Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.

Glucose Starvation Causes ptauS409 Increase in N2a Cells Through ATF3/PKAcα Signaling Pathway

In this work, we report that glucose starvation (GS) causes ptau^S409 increase, which may participate in GS-induced neurites retraction in neuro-2a (N2a) cells. Upon GS treatment, PKAcα was stimulated at mRNA and protein levels. Luciferase reporter gene assays indicated that GS regulated PKAcα expression through a core promoter (-345 to -95 bp upstream the transcription starting site) consisting of a cis-acting element of Activating Transcription Factor 3 (ATF3). Knockdown and over-expression experiments demonstrate that ATF3 transcriptionally regulated PKAcα expression. Moreover, GS stimulated ATF3 expression in a time-dependent manner. These findings reveal that glucose starvation induces ptau^S409 increase in N2a cells through an ATF3- PKAcα axis, which shed some light on the relationship between brain glucose metabolism and neurodegenerative diseases.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer's Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis

BACKGROUND

Early in-vivo diagnosis of Alzheimer's disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis.

METHODS

From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD.

RESULTS

A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity of Amyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher-order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%.

CONCLUSIONS

The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD.

Reversible increase in stress-associated neurobiological activity in the acute phase of Takotsubo syndrome; a brain 18F-FDG-PET study

INTRODUCTION

Takotsubo syndrome (TTS) is triggered mostly by physical and/or emotional stress that is processed in stress-associated brain regions, including the amygdala. However, it remains unclear whether such stress-induced brain activity is associated with TTS onset.

METHODS AND RESULTS

We acquired brain [¹⁸F]-2-fluoro-deoxy-d-glucose (¹⁸F-FDG) positron emission tomography in 4 TTS patients (44-82 yrs., 3 women) on days 2-4 (acute phase) and days 29-40 (recovery phase) after diagnosis of TTS was made by coronary angiography and left ventriculogram. The ¹⁸F-FDG uptake was measured globally and also in the pre-defined regions of interest of the bilateral amygdala on the common Montreal Neurological Institute space; all ¹⁸F-FDG images were normalized using automated image pre-processing. Amygdalar activity was calculated by dividing the ¹⁸F-FDG uptake of the amygdala by the global brain uptake. Left ventriculograms showed that apical ballooning was typical at diagnosis and was then relieved in the recovery phase. Amygdalar activity in the acute phase (0.872 ± 0.032) was higher than in the recovery phase (0.805 ± 0.037) (P = 0.013).

CONCLUSIONS

We report here 4 cases of TTS showing higher amygdalar activity in the acute phase as compared with the recovery phase, suggesting that increased stress-induced neurobiological activity is associated with TTS onset.

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance. Methods: 18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times. Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective. Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

Severity of Subjective Cognitive Complaints and Worries in Older Adults Are Associated With Cerebral Amyloid-β Load

Subjective cognitive decline (SCD) is considered an early risk stage for dementia due to Alzheimer's disease (AD) and the development of pathological brain changes, such as the aggregation of amyloid-beta (amyloid-β) plaques. This study evaluates the association between specific features of SCD and cerebral amyloid-β load measured by positron emission tomography (PET) with ¹⁸F-florbetaben in 40 cognitively normal older individuals. Global amyloid-β, as well as regional amyloid-β load for the frontal, temporal, parietal, and cingulate cortex, was quantified. Specific features of SCD, such as subjective cognitive complaints and worry, were assessed using the 39-item Everyday Cognition Scales and the 16-item Penn State Worry Questionnaire. Spearman's rank partial correlation analyses, adjusted for age and apolipoprotein E ε4 status, were conducted to test the associations between specific features of SCD and cerebral amyloid-β load. The severity of subjective cognitive complaints in everyday memory and organization was positively correlated with amyloid-β load in the frontal cortex. In addition, the severity of subjective cognitive complaints in everyday planning was positively correlated with amyloid-β load in the parietal cortex. Higher levels of worry were associated with higher amyloid-β load in the frontal cortex. After correction of the PET data for partial volume effects, these associations were reduced to trend level. In conclusion, the severity of subjective cognitive complaints and the level of trait worry were positively associated with cortical amyloid-β burden, particularly in the frontal and parietal cortex. Further studies are required to elucidate the direction of these associations in order to develop strategies to prevent amyloid deposition and cognitive decline.

Longitudinal Associations of Blood Phosphorylated Tau181 and Neurofilament Light Chain With Neurodegeneration in Alzheimer Disease

Question

What is the potential of blood-based biomarkers for predicting and monitoring the progression of Alzheimer disease neurodegeneration?

Findings

In this cohort study that included 1113 participants from the multicentric Alzheimer’s Disease Neuroimaging Initiative study, baseline and longitudinal increases of tau phosphorylated at threonine 181 (p-tau181) in blood plasma were associated with progressive, longitudinal neurodegeneration in brain regions characteristic for Alzheimer disease, as well as with cognitive decline, only among participants with elevated brain amyloid-β. Neurofilament light chain in plasma, however, was associated with disease progression independent of amyloid-β and plasma p-tau181.

Meaning

These findings suggest that plasma p-tau181, alone or combined with plasma neurofilament light chain, can be used as an accessible, minimally invasive biomarker to track Alzheimer disease progression.

Importance

Plasma phosphorylated tau at threonine 181 (p-tau181) has been proposed as an easily accessible biomarker for the detection of Alzheimer disease (AD) pathology, but its ability to monitor disease progression in AD remains unclear.

Objective

To study the potential of longitudinal plasma p-tau181 measures for assessing neurodegeneration progression and cognitive decline in AD in comparison to plasma neurofilament light chain (NfL), a disease-nonspecific marker of neuronal injury.

Design, Setting, and Participants

This longitudinal cohort study included data from the Alzheimer’s Disease Neuroimaging Initiative from February 1, 2007, to June 6, 2016. Follow-up blood sampling was performed for up to 8 years. Plasma p-tau181 measurements were performed in 2020. This was a multicentric observational study of 1113 participants, including cognitively unimpaired participants as well as patients with cognitive impairment (mild cognitive impairment and AD dementia). Participants were eligible for inclusion if they had available plasma p-tau181 and NfL measurements and at least 1 fluorine-18–labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) or structural magnetic resonance imaging scan performed at the same study visit. Exclusion criteria included any significant neurologic disorder other than suspected AD; presence of infection, infarction, or multiple lacunes as detected by magnetic resonance imaging; and any significant systemic condition that could lead to difficulty complying with the protocol.

Exposures

Plasma p-tau181 and NfL measured with single-molecule array technology.

Main Outcomes and Measures

Longitudinal imaging markers of neurodegeneration (FDG PET and structural magnetic resonance imaging) and cognitive test scores (Preclinical Alzheimer Cognitive Composite and Alzheimer Disease Assessment Scale–Cognitive Subscale with 13 tasks). Data were analyzed from June 20 to August 15, 2020.

Results

Of the 1113 participants (mean [SD] age, 74.0 [7.6] years; 600 men [53.9%]; 992 non-Hispanic White participants [89.1%]), a total of 378 individuals (34.0%) were cognitively unimpaired (CU) and 735 participants (66.0%) were cognitively impaired (CImp). Of the CImp group, 537 (73.1%) had mild cognitive impairment, and 198 (26.9%) had AD dementia. Longitudinal changes of plasma p-tau181 were associated with cognitive decline (CU: r = –0.24, P < .001; CImp: r = 0.34, P < .001) and a prospective decrease in glucose metabolism (CU: r = –0.05, P = .48; CImp: r = –0.27, P < .001) and gray matter volume (CU: r = –0.19, P < .001; CImp: r = –0.31, P < .001) in highly AD-characteristic brain regions. These associations were restricted to amyloid-β–positive individuals. Both plasma p-tau181 and NfL were independently associated with cognition and neurodegeneration in brain regions typically affected in AD. However, NfL was also associated with neurodegeneration in brain regions exceeding this AD-typical spatial pattern in amyloid-β–negative participants. Mediation analyses found that approximately 25% to 45% of plasma p-tau181 outcomes on cognition measures were mediated by the neuroimaging-derived markers of neurodegeneration, suggesting links between plasma p-tau181 and cognition independent of these measures.

Conclusions and Relevance

Study findings suggest that plasma p-tau181 was an accessible and scalable marker for predicting and monitoring neurodegeneration and cognitive decline and was, unlike plasma NfL, AD specific. The study findings suggest implications for the use of plasma biomarkers as measures to monitor AD progression in clinical practice and treatment trials.

Data-driven FDG-PET subtypes of Alzheimer's disease-related neurodegeneration

BACKGROUND

Previous research has described distinct subtypes of Alzheimer's disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.

METHODS

Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment ("prodromal AD") according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months.

RESULTS

Three main hypometabolic subtypes were identified: (i) "typical" (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) "limbic-predominant" (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare "cortical-predominant" subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline.

CONCLUSIONS

These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

Impairment of Everyday Spatial Navigation Abilities in Mild Cognitive Impairment Is Weakly Associated with Reduced Grey Matter Volume in the Medial Part of the Entorhinal Cortex

BACKGROUND

Research in rodents identified specific neuron populations encoding information for spatial navigation with particularly high density in the medial part of the entorhinal cortex (ERC), which may be homologous with Brodmann area 34 (BA34) in the human brain.

OBJECTIVE

The aim of this study was to test whether impaired spatial navigation frequently occurring in mild cognitive impairment (MCI) is specifically associated with neurodegeneration in BA34.

METHODS

The study included baseline data of MCI patients enrolled in the Alzheimer's Disease Neuroimaging Initiative with high-resolution structural MRI, brain FDG PET, and complete visuospatial ability scores of the Everyday Cognition test (VS-ECog) within 30 days of PET. A standard mask of BA34 predefined in MNI space was mapped to individual native space to determine grey matter volume and metabolic activity in BA34 on MRI and on (partial volume corrected) FDG PET, respectively. The association of the VS-ECog sum score with grey matter volume and metabolic activity in BA34, APOE4 carrier status, age, education, and global cognition (ADAS-cog-13 score) was tested by linear regression. BA28, which constitutes the lateral part of the ERC, was used as control region.

RESULTS

The eligibility criteria led to inclusion of 379 MCI subjects. The VS-ECog sum score was negatively correlated with grey matter volume in BA34 (β= -0.229, p = 0.022) and age (β= -0.124, p = 0.036), and was positively correlated with ADAS-cog-13 (β= 0.175, p = 0.003). None of the other predictor variables contributed significantly.

CONCLUSION

Impairment of spatial navigation in MCI is weakly associated with BA34 atrophy.

Functional Neural Changes after Low-Frequency Bilateral Globus Pallidus Internus Deep Brain Stimulation for Post-Hypoxic Cortical Myoclonus: Voxel-Based Subtraction Analysis of Serial Positron Emission

Post-hypoxic myoclonus (PHM) and Lance–Adams syndrome (LAS) are rare conditions following cardiopulmonary resuscitation. The aim of this study was to identify functional activity in the cerebral cortex after a hypoxic event and to investigate alterations that could be modulated by deep brain stimulation (DBS). A voxel-based subtraction analysis of serial positron emission tomography (PET) scans was performed in a 34-year-old woman with chronic medically refractory PHM that improved with bilateral globus pallidus internus (Gpi) DBS implanted three years after the hypoxic event. The patient required low-frequency stimulation to show myoclonus improvement. Using voxel-based statistical parametric mapping, we identified a decrease in glucose metabolism in the prefrontal lobe including the dorsolateral, orbito-, and inferior prefrontal cortex, which was suspected to be the origin of the myoclonus from postoperative PET/magnetic resonance imaging (MRI) after DBS. Based on the present study results, voxel-based subtraction of PET appears to be a useful approach for monitoring patients with PHM treated with DBS. Further investigation and continuous follow-up on the use of PET analysis and DBS treatment for patients with PHM are necessary to help understanding the pathophysiology of PHM, or LAS.

Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

BACKGROUND

Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease.

OBJECTIVE

This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI.

METHODS

The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET.

RESULTS

FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666).

CONCLUSION

Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment

OBJECTIVE

Glucose-based positron emission tomography (PET) imaging has been widely used to predict the progression of mild cognitive impairment (MCI) into Alzheimer's disease (AD) clinically. However, existing discriminant methods are unsubtle to reveal pathophysiological changes. Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately.

METHODS

In this study, we acquired fluorodeoxyglucose PET images and clinical assessments from 420 MCI patients with 36 months follow-up. Individual metabolic network based on connectome analysis was constructed, and the metabolic connectivity in this network was extracted as predictive features. Three different classification strategies were implemented to interrogate the predictive performance. To verify the effectivity of selected features, specific brain regions associated with MCI conversion were identified based on these features and compared with prior knowledge.

RESULTS

As a result, 4005 connectome features were obtained, and 153 in which were selected as efficient features. Our proposed feature extraction method had achieved 85.2% accuracy for MCI conversion prediction (sensitivity: 88.1%; specificity: 81.2%; and AUC: 0.933). The discriminative brain regions associated with MCI conversion were mainly located in the precentral gyrus, precuneus, lingual, and inferior frontal gyrus.

CONCLUSION

Overall, the results suggest that our proposed individual metabolic connectome method has great potential to predict whether MCI patients will progress to AD. The metabolic connectome may help to identify brain metabolic dysfunction and build a clinically applicable biomarker to predict the MCI progression.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.

Movement disorders after hypoxic brain injury following cardiac arrest in adults

BACKGROUND AND PURPOSE

Post-hypoxic movement disorders and chronic post-hypoxic myoclonus are rare complications after cardiac arrest in adults. Our study investigates the clinical spectrum, neuroimaging results, therapy and prognosis of these debilitating post-hypoxic sequelae.

METHODS

This retrospective study included 72 patients from the neurological intensive care unit at a university hospital, who were diagnosed with hypoxic-ischaemic encephalopathy after cardiac arrest between January 2007 and September 2018. Clinical records were screened for occurrence of post-hypoxic movement disorders and chronic post-hypoxic myoclonus. Affected patients were further analysed for applied neuroprognostic tests, administered therapy and treatment response, and the outcome of these movement disorders and neurological function.

RESULTS

Nineteen out of 72 screened patients exhibited post-hypoxic motor symptoms. Basal ganglia injury was the most likely neuroanatomical correlate of movement disorders as indicated by T1 hyperintensities and hypometabolism of this region in magnetic resonance imaging and positron emission tomography computed tomography. Levomepromazine and intrathecal baclofen showed first promising and mostly prompt responses to control these post-hypoxic movement disorders and even hyperkinetic storms. In contrast, chronic post-hypoxic myoclonus best responded to co-application of clonazepam, levetiracetam and primidone. Remission rates of post-hypoxic movement disorders and chronic post-hypoxic myoclonus were 58% and 50%, respectively. Affected patients seemed to present a rather good recovery of cognitive functions in contrast to the often more severe physical deficits.

CONCLUSIONS

Post-hypoxic movement disorders associated with pronounced basal ganglia dysfunction might be efficiently controlled by levomepromazine or intrathecal baclofen. Their occurrence might be an indicator for a more unfavourable, but often not devastating, neurological outcome.

Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images

Objective:

Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI.

Methods:

18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer’s Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results.

Result:

A total of 1103 ROIs were obtained. One hundred features were learned from ROIs using the DBN. The classification accuracy using linear, polynomial, and RBF kernels was 83.9%, 79.2%, and 86.6%, respectively. This method may be a powerful tool for personalized precision medicine in the population with prediction of early AD progression.

Decreased Glucose Utilization Contributes to Memory Impairment in Patients with Glufosinate Ammonium Intoxication

The symptoms of glufosinate ammonium (GLA) intoxication include gastrointestinal and neurologic symptoms, respiratory failure, and cardiovascular instability. Among these, neurologic symptoms including loss of consciousness, memory impairment, and seizure are characteristic of GLA poisoning. However, the mechanism of brain injury by GLA poisoning is still poorly understood. We investigated nine patients who had performed an F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scan because of memory impairment caused by GLA ingestion. FDG-PET images of patients with GLA intoxication were compared with 24 age- and sex-matched healthy controls to evaluate whether the patients had abnormal patterns of glucose metabolism in the brain. Decreased glucose metabolism was observed in the inferior frontal and temporal lobes of these patients with GLA intoxication when compared with 24 age- and sex-matched healthy controls. Three patients performed follow-up FDG-PET scans. However, it was shown that the results of the follow-up FDG-PET scans were determined to be inconclusive. Our study showed that memory impairment induced by GLA intoxication was associated with glucose hypometabolism in the inferior frontal and temporal lobes in the brain.

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score

18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG-PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross-validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0-3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.

18F-FDG PET for Prediction of Conversion to Alzheimer's Disease Dementia in People with Mild Cognitive Impairment: An Updated Systematic Review of Test Accuracy

Background:

A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of ¹⁸F-FDG PET in clinical practice in people with mild cognitive impairment (MCI).

Objectives:

To update the evidence and reassess the accuracy of ¹⁸F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer’s disease (AD) dementia at follow-up.

Methods:

A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies.

Results:

When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56–100%, specificity 24–100%). The most consistently high sensitivity and specificity values (approximately ≥80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies.

Conclusion:

Systematic and comprehensive assessment of studies of ¹⁸FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (≥5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of ¹⁸F-FDG PET in people with MCI is needed.

Region-specific association between basal blood insulin and cerebral glucose metabolism in older adults

Background

Although previous studies have suggested that insulin plays a role in brain function, it still remains unclear whether or not insulin has a region-specific association with neuronal and synaptic activity in the living human brain. We investigated the regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism (CMglu), a proxy for neuronal and synaptic activity, in older adults.

Method

A total of 234 nondiabetic, cognitively normal (CN) older adults underwent comprehensive clinical assessment, resting-state 18F-fluodeoxyglucose (FDG)-positron emission tomography (PET) and blood sampling to determine overnight fasting blood insulin and glucose levels, as well as apolipoprotein E (APOE) genotyping.

Results

An exploratory voxel-wise analysis of FDG-PET without a priori hypothesis demonstrated a positive association between basal blood insulin levels and resting-state CMglu in specific cerebral cortices and hippocampus, rather than in non-specific overall cerebral regions, even after controlling for the effects of APOE e4 carrier status, vascular risk factor score, body mass index, fasting blood glucose, and demographic variables. Particularly, a positive association of basal blood insulin with CMglu in the right posterior hippocampus and adjacent parahippocampal region as well as in the right inferior parietal region remained significant after multiple comparison correction. Conversely, no region showed negative association between basal blood insulin and CMglu.

Conclusions

Our finding suggests that basal fasting blood insulin may have association with neuronal and synaptic activity in specific cerebral regions, particularly in the hippocampal/parahippocampal and inferior parietal regions.

•

We investigated regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism.

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Significant clusters with positive associations were found mainly in the hippocampal and inferior parietal regions.

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Our finding suggests a region-specific association of basal blood insulin with resting-state cerebral glucose metabolism.

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Further studies to elucidate underlying mechanism and implication of this region-specific association will be necessary.

Classification of amyloid PET images using novel features for early diagnosis of Alzheimer's disease and mild cognitive impairment conversion

BACKGROUND

New PET tracers could have a substantial impact on the early diagnosis of Alzheimer's disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MRI but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology.

MATERIALS AND METHODS

In this study, one volumetric FD method and one volumetric SE method were applied to fluorine-18-flutemetamol and fluorine-18-florbetapir 3D amyloid images from 65 and 281 participants, respectively, including healthy volunteers, and patients with probable Alzheimer's disease (pAD) or mild cognitive impairment (MCI).

RESULTS

The group average FD of white matter surface and SE of white matter volume for healthy volunteers were higher than for pAD patients. Both FD and SE are effective in the identification of MCI patients who progress to pAD during the 2-year follow-up (ground truth). Finally, we developed a support vector machine multimodal classification framework using both PET and MRI features, which showed higher accuracy compared to traditional standard uptake value ratio or using PET alone. The classification accuracy for flutemetamol and florbetapir is 88.9 and 83.3%, respectively, for MCI progression, which is competitive with existing literature.

CONCLUSION

The results presented in this study demonstrate the potential of FD and SE methods for the analysis of brain PET scans in early AD diagnosis and in the prediction of MCI-AD conversion.

Prognosis of conversion of mild cognitive impairment to Alzheimer's dementia by voxel-wise Cox regression based on FDG PET data

Aim

The value of ¹⁸F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information.

Methods

FDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46–48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI.

Results

Conversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p < .01, false discovery rate (FDR) corrected). The clinical-only model (1) and the models based on normalized FDG uptake from Cox-ROI only (2) and ANCOVA-ROI only (3) all significantly predicted conversion to AD (Wald Test (WT): p < .001). The clinical model (1) was significantly improved by adding imaging information in model (4) (Akaike information criterion (AIC) relative likelihood (RL) (1) vs (4): RL < 0.018). There were no significant differences between models (2) and (3), as well as (4) and (5).

Conclusions

Voxel-wise Cox regression identifies conversion-related patterns of cerebral glucose metabolism, but is not superior to classical group contrasts in this regard. With imaging information from both FDG PET patterns, the prediction of conversion to AD was improved.

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Voxel-wise Cox regression identifies regions relevant for development AD.

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Hypometabolism of these regions poses a significant hazard for AD development.

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Inclusion of FDG PET data improves the accuracy of prognosis significantly.

Amyloid beta-positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity

Amyloid beta (Aβ) deposition and cognitive decline are key features of Alzheimer's disease. The relationship between Aβ status and changes in neuronal function over time, however, remains unclear. We evaluated the effect of baseline Aβ status on reference region spontaneous brain activity (SBA-rr) using resting-state functional magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in patients with mild cognitive impairment. Patients (N = 62, [43 Aβ-positive]) from the Alzheimer's Disease Neuroimaging Initiative were divided into Aβ-positive and Aβ-negative groups via prespecified cerebrospinal fluid Aβ42 or 18F-florbetapir positron emission tomography standardized uptake value ratio cutoffs measured at baseline. We analyzed interaction of biomarker-confirmed Aβ status with SBA-rr change over a 2-year period using mixed-effects modeling. SBA-rr differences between Aβ-positive and Aβ-negative subjects increased significantly over time within subsystems of the default and visual networks. Changes exhibit an interaction with memory performance over time but were independent of glucose metabolism. Results reinforce the value of resting-state functional magnetic resonance imaging in evaluating Alzheimer''s disease progression and suggest spontaneous neuronal activity changes are concomitant with cognitive decline.

Principal Components Analysis of Brain Metabolism Predicts Development of Alzheimer Dementia

The value of ¹⁸F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate. We used a principal components analysis (PCA) to identify a metabolic AD conversion-related pattern (ADCRP) and investigated the prognostic value of the resulting pattern expression score (PES). Methods: ¹⁸F-FDG PET scans of 544 MCI patients were obtained from the Alzheimer Disease Neuroimaging Initiative database and analyzed. We implemented voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose cerebral metabolic patterns associated with conversion from MCI to AD. By Cox proportional hazards regression, we examined the prognostic value of candidate predictors. Also, we constructed prognostic models with clinical, imaging, and clinical and imaging variables in combination. Results: PCA revealed an ADCRP that involved regions with relative decreases in metabolism (temporoparietal, frontal, posterior cingulate, and precuneus cortices) and relative increases in metabolism (sensorimotor and occipital cortices, cerebellum, and left putamen). Among the predictor variables age, sex, Functional Activities Questionnaire, Mini-Mental State Examination, apolipoprotein E, PES, and normalized ¹⁸F-FDG uptake (regions with significant hypo- and hypermetabolism in patients with conversion vs. those without conversion), PES was the best independent predictor of conversion (hazard ratio, 1.77, per z score increase; 95% CI, 1.24-2.52; P < 0.001). Moreover, adding PES to the model including the clinical variables significantly increased its prognostic value. Conclusion: The ADCRP expression score was a valid predictor of conversion. A combination of clinical variables and PES yielded a higher accuracy than each single tool in predicting conversion from MCI to AD, underlining the incremental utility of ¹⁸F-FDG PET.

Prediction of Alzheimer's Dementia in Patients with Amnestic Mild Cognitive Impairment in Clinical Routine: Incremental Value of Biomarkers of Neurodegeneration and Brain Amyloidosis Added Stepwise to Cognitive Status

The aim of this study was to evaluate the incremental benefit of biomarkers for prediction of Alzheimer's disease dementia (ADD) in patients with mild cognitive impairment (MCI) when added stepwise in the order of their collection in clinical routine. The model started with cognitive status characterized by the ADAS-13 score. Hippocampus volume (HV), cerebrospinal fluid (CSF) phospho-tau (pTau), and the FDG t-sum score in an AD meta-region-of-interest were compared as neurodegeneration markers. CSF-Aβ1-42 was used as amyloidosis marker. The incremental prognostic benefit from these markers was assessed by stepwise Kaplan-Meier survival analysis in 402 ADNI MCI subjects. Predefined cutoffs were used to dichotomize patients as 'negative' or 'positive' for AD characteristic alteration with respect to each marker. Among the neurodegeneration markers, CSF-pTau provided the best incremental risk stratification when added to ADAS-13. FDG PET outperformed HV only in MCI subjects with relatively preserved cognition. Adding CSF-Aβ provided further risk stratification in pTau-positive subjects, independent of their cognitive status. Stepwise integration of biomarkers allows stepwise refinement of risk estimates for MCI-to-ADD progression. Incremental benefit strongly depends on the patient's status according to the preceding diagnostic steps. The stepwise Kaplan-Meier curves might be useful to optimize diagnostic workflow in individual patients.

Metabolic and Electrophysiological Changes Associated to Clinical Improvement in Two Severely Traumatized Subjects Treated With EMDR-A Pilot Study

Neuroimaging represents a powerful tool to investigate the neurobiological correlates of Eye Movements Desensitization and Reprocessing (EMDR). The impact of EMDR on cortical and sub-cortical brain regions has been proven by several investigations demonstrating a clear association between symptoms disappearance and changes in cortical structure and functionality. The aim of this study was to assess by electroencephalography (EEG) and for the first time by positron emission tomography (PET) the changes occurring after EMDR therapy in two cases of psychological trauma following brain concussion and comatose state due to traffic accident. A 28 and a 29 years old men underwent extensive neuropsychological examination, which investigated: (i) categorical and phonological verbal fluency; (ii) episodic verbal memory; (iii) executive functions; (iv) visuospatial abilities; (v) attention and working memory as well as clinical assessment by means of psychopathological tests (CAPS, IES, BDI, SCL90R, and DES). They were then treated by eight sessions of EMDR. During the first session EEG monitoring was continuously performed and ¹⁸F-FDG PET scans, depicting brain metabolism, were acquired at rest within a week (T0). After the last session, in which the two clients were considered to be symptoms-free, neuropsychological, clinical, and PET assessment were repeated (T1). PET data were semi-quantitatively compared to a group of 18 normal controls, as for EEG the preferential cortical activations were disclosed by thresholding the individual z-score to a p < 0.05. There was a significant improvement in clinical condition for both clients associated with a significant decrease in CAPS scores. IES and BDI were found to be pathological at T0 and improved at T1 in only one subject. Visuo-constructive abilities and abstract reasoning improved after EMDR in both subjects. As for EEG, the most striking changes occurred in fronto-temporal-parietal cortex in subject 1 while subject 2 showed only minor changes. PET showed more pronounced metabolism in orbito-frontal and prefrontal cortex at T1 as compared to T0 in both subjects. In conclusion both clients had a clear clinical improvement in PTSD symptoms associated with metabolic and electrophysiological changes in limbic and associative cortex, respectively, highlighting the value of EMDR also in such extreme pathological conditions.

Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all individuals clinically diagnosed with MCI progress to AD. A fraction of subjects with MCI either progress to non-AD dementia or remain stable at the MCI stage without progressing to dementia. Although a curative treatment of AD is currently unavailable, it is extremely important to correctly identify the individuals in the MCI phase that will go on to develop AD so that they may benefit from a curative treatment when one becomes available in the near future. At the same time, it would be highly desirable to also correctly identify those in the MCI phase that do not have AD pathology so they may be spared from unnecessary pharmocologic interventions that, at best, may provide them no benefit, and at worse, could further harm them with adverse side-effects. Additionally, it may be easier and simpler to identify the cause of the cognitive impairment in these non-AD cases, and hence proper identification of prodromal AD will be of benefit to these individuals as well. Fluorodeoxy glucose positron emission tomography (FDG-PET) captures the metabolic activity of the brain, and this imaging modality has been reported to identify changes related to AD prior to the onset of structural changes. Prior work on designing classifier using FDG-PET imaging has been promising. Since deep-learning has recently emerged as a powerful tool to mine features and use them for accurate labeling of the group membership of given images, we propose a novel deep-learning framework using FDG-PET metabolism imaging to identify subjects at the MCI stage with presymptomatic AD and discriminate them from other subjects with MCI (non-AD / non-progressive). Our multiscale deep neural network obtained 82.51% accuracy of classification just using measures from a single modality (FDG-PET metabolism data) outperforming other comparable FDG-PET classifiers published in the recent literature.

Feasibility of 15O-water PET studies of auditory system activation during general anesthesia in children

Background

¹⁵O-Water positron emission tomography (PET) enables functional imaging of the auditory system during stimulation via a promontory electrode or cochlear implant, which is not possible using functional magnetic resonance imaging (fMRI). Although PET has been introduced in this context decades ago, its feasibility when performed during general anesthesia has not yet been explored. However, due to a shift to earlier (and bilateral) auditory implantation, the need to study children during general anesthesia appeared, since they are not able to cooperate during scanning. Therefore, we evaluated retrospectively results of individual SPM (statistical parametric mapping) analysis of ¹⁵O-water PET in 17 children studied during general anesthesia and compared them to those in 9 adults studied while awake. Specifically, the influence of scan duration, smoothing filter kernel employed during preprocessing, and cut-off value used for statistical inferences were evaluated. Frequencies, peak heights, and extents of activations in auditory and extra-auditory brain regions (AR and eAR) were registered.

Results

It was possible to demonstrate activations in auditory brain regions during general anesthesia; however, the frequency and markedness of positive findings were dependent on some of the abovementioned influence factors. Scan duration (60 vs. 90 s) had no significant influence on peak height of auditory cortex activations. To achieve a similar frequency and extent of AR activations during general anesthesia compared to waking state, a lower cut-off for statistical inferences (p < 0.05 or p < 0.01 vs. p < 0.001) had to be applied. However, this lower cut-off was frequently associated with unexpected, “artificial” activations in eAR. These activations in eAR could be slightly reduced by the use of a stronger smoothing filter kernel during preprocessing of the data (e.g., [30 mm]³).

Conclusions

Our data indicate that it is feasible to detect auditory cortex activations in ¹⁵O-water PET during general anesthesia. Combined with the improved signal to noise ratios of modern PET scanners, this suggests reasonable prospects for further evaluation of the method for clinical use in auditory implant users. Adapted parameters for data analysis seem to be helpful to improve the proportion of signals in AR versus eAR.

Combination of Structural MRI and FDG-PET of the Brain Improves Diagnostic Accuracy in Newly Manifested Cognitive Impairment in Geriatric Inpatients

BACKGROUND

The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease.

OBJECTIVE

To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients.

METHODS

Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis.

RESULTS

Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001).

CONCLUSION

These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.

Application of Concordance Probability Estimate to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease

Subjects with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). Identifying MCI subjects who have high progression risk to AD is important in clinical management. Existing risk prediction models of AD among MCI subjects generally use either the AUC or Harrell's C-statistic to evaluate predictive accuracy. AUC is aimed at binary outcome and Harrell's C-statistic depends on the unknown censoring distribution. Gönen & Heller's K-index, also known as concordance probability estimate (CPE), is another measure of overall predictive accuracy for Cox proportional hazards (PH) models, which does not depend on censoring distribution. As a comprehensive example, using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we built a Cox PH model to predict the conversion from MCI to AD where the prognostic accuracy was evaluated using K-index.

Early identification of MCI converting to AD: a FDG PET study

PURPOSE

Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer's disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not.

METHODS

FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy.

RESULTS

The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients.

CONCLUSION

In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

δ scores predict mild cognitive impairment and Alzheimer's disease conversions from nondemented states

INTRODUCTION

We tested the latent variable "δ" (for "dementia")'s ability to predict conversion to "mild cognitive impairment" (MCI) and Alzheimer's disease (AD).

METHODS

An ethnicity equivalent d homolog ("dEQ") was constructed in n = 1113 Mexican- American (MA) and n = 1958 non-Hispanic white (NHW) participants in the Texas Alzheimer's Research and Care Consortium. "Normal Controls" (NC) (n = 1276) and MCI cases (n = 611) were followed annually for up to 6 years [m = 4.7(0.6)].

RESULTS

22.0% (n = 281) of NC converted to "MCI" or "AD". 17.3%( n = 106) of MCI converted to "AD." Independently of covariates, each quintile increase in the dEQ scores of NC increased the odds of conversion by 52%. Each quintile increase in the dEQ scores of MCI cases increased the odds of conversion to AD almost three-fold.

DISCUSSION

Baseline δ scores predict MCI and AD conversions from nondemented states in MA and NHW.

Glucose deficit triggers tau pathology and synaptic dysfunction in a tauopathy mouse model

Clinical investigations have highlighted a biological link between reduced brain glucose metabolism and Alzheimer's disease (AD). Previous studies showed that glucose deprivation may influence amyloid beta formation in vivo but no data are available on the effect that this condition might have on tau protein metabolism. In the current paper, we investigated the effect of glucose deficit on tau phosphorylation, memory and learning, and synaptic function in a transgenic mouse model of tauopathy, the h-tau mice. Compared with controls, h-tau mice with brain glucose deficit showed significant memory impairments, reduction of synaptic long-term potentiation, increased tau phosphorylation, which was mediated by the activation of P38 MAPK Kinase pathway. We believe our studies demonstrate for the first time that reduced glucose availability in the central nervous system directly triggers behavioral deficits by promoting the development of tau neuropathology and synaptic dysfunction. Since restoring brain glucose levels and metabolism could afford the opportunity to positively influence the entire AD phenotype, this approach should be considered as a novel and viable therapy for preventing and/or halting the disease progression.

Preserved brain metabolic activity at the age of 96 years

Loss of brain tissue becomes notable to cerebral magnetic resonance imaging (MRI) at age 30 years, and progresses more rapidly from mid 60s. The incidence of dementia increases exponentially with age, and is all too frequent in the oldest old (≥ 90 years of age), the fastest growing age group in many countries. However, brain pathology and cognitive decline are not inevitable, even at extremely old age (den Dunnen et al., 2008).