Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization

A multi-site 99mTc-HMPAO SPECT study of cerebral blood flow in a community sample of patients with major depression

Prior regional Cerebral Blood Flow (rCBF) studies in Major Depressive Disorder (MDD) have been limited by small, highly selective, non-representative samples that have yielded variable and poorly replicated findings. The aim of this study was to compare rCBF measures in a large, more representative community sample of adults with MDD and healthy control participants. This is a cross-sectional, retrospective multi-site cohort study in which clinical data from 338 patients 18-65 years of age with a primary diagnosis of MDD were retrieved from a central database for 8 privately owned, private-pay outpatient psychiatric centers across the United States. Two 99mTc-HMPAO SPECT brain scans, one at rest and one during performance of a continuous performance task, were acquired as a routine component of their initial clinical evaluation. In total, 103 healthy controls, 18-65 years old and recruited from the community were also assessed and scanned. Depressed patients had significantly higher rCBF in frontal, anterior cingulate, and association cortices, and in basal ganglia, thalamus, and cerebellum, after accounting for significantly higher overall CBF. Depression severity associated positively with rCBF in the basal ganglia, hippocampus, cerebellum, and posterior white matter. Elevated rCBF was especially prominent in women and older patients. Elevated rCBF likely represents pathogenic hypermetabolism in MDD, with its magnitude in direct proportion to depression severity. It is brain-wide, with disproportionate increases in cortical and subcortical attentional networks. Hypermetabolism may be a reasonable target for novel therapeutics in MDD.

Genetic-based patient stratification in Alzheimer's disease

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Developing a novel dual-injection FDG-PET imaging methodology to study the functional neuroanatomy of gait

Gait is an excellent indicator of physical, emotional, and mental health. Previous studies have shown that gait impairments in ageing are common, but the neural basis of these impairments are unclear. Existing methodologies are suboptimal and novel paradigms capable of capturing neural activation related to real walking are needed. In this study, we used a hybrid PET/MR system and measured glucose metabolism related to both walking and standing with a dual-injection paradigm in a single study session. For this study, 15 healthy older adults (10 females, age range: 60.5-70.7 years) with normal cognition were recruited from the community. Each participant received an intravenous injection of [18F]-2-fluoro-2-deoxyglucose (FDG) before engaging in two distinct tasks, a static postural control task (standing) and a walking task. After each task, participants were imaged. To discern independent neural functions related to walking compared to standing, we applied a bespoke dose correction to remove the residual 18F signal of the first scan (PETSTAND) from the second scan (PETWALK) and proportional scaling to the global mean, cerebellum, or white matter (WM). Whole-brain differences in walking-elicited neural activity measured with FDG-PET were assessed using a one-sample t-test. In this study, we show that a dual-injection paradigm in healthy older adults is feasible with biologically valid findings. Our results with a dose correction and scaling to the global mean showed that walking, compared to standing, increased glucose consumption in the cuneus (Z = 7.03), the temporal gyrus (Z = 6.91) and the orbital frontal cortex (Z = 6.71). Subcortically, we observed increased glucose metabolism in the supraspinal locomotor network including the thalamus (Z = 6.55), cerebellar vermis and the brainstem (pedunculopontine/mesencephalic locomotor region). Exploratory analyses using proportional scaling to the cerebellum and WM returned similar findings. Here, we have established the feasibility and tolerability of a novel method capable of capturing neural activations related to actual walking and extended previous knowledge including the recruitment of brain regions involved in sensory processing. Our paradigm could be used to explore pathological alterations in various gait disorders.

The relationship between brain glucose metabolism and insulin resistance in subjects with normal cognition - a study based on 18F-FDG PET

OBJECTIVE

Insulin resistance can increase the risk of cognitive dysfunction and dementia. Our purpose is to use 18F-FDG PET imaging to explore the effect of insulin resistance on brain glucose metabolism in cognitively normal subjects.

METHODS

A total of 189 cognitively normal subjects who underwent PET examinations were enrolled. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to evaluate the presence of insulin resistance. Multivariate linear regression and generalized additive models were used to analyze the association between HOMA-IR and glucose metabolism in the whole brain and evaluate the effects of various covariates. The SPM12 software was used to evaluate the regional effect of insulin resistance on brain glucose metabolism.

RESULTS

After being fully adjusted for confounding factors, HOMA-IR showed an approximately linear negative correlation with brain glucose metabolism (β = -0.219, T = -3.331, P = 0.021). Compared with normal subjects, insulin-resistant subjects had reduced glucose metabolism in bilateral middle temporal gyrus, bilateral middle frontal gyrus, right precentral gyrus, right inferior frontal gyrus, right cuneiform lobe and bilateral cerebellar regions. In cognitively normal subjects, systemic insulin resistance has a significant effect on brain glucose metabolism.

CONCLUSIONS

18F-FDG brain PET imaging could be helpful for the early diagnosis and treatment of changes in brain glucose metabolism caused by insulin resistance.

Self-normalized Classification of Parkinson's Disease DaTscan Images

Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson's disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson's Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4.

Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization

Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMR_glc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization.

Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized.

To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses.

Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals.

Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.

Evaluation of Age and Sex-Related Metabolic Changes in Healthy Subjects: An Italian Brain 18F-FDG PET Study

Background: 18F-fluorodeoxyglucose (18F-FDG) positron-emission-tomography (PET) allows detection of cerebral metabolic alterations in neurological diseases vs. normal aging. We assess age- and sex-related brain metabolic changes in healthy subjects, exploring impact of activity normalization methods. Methods: brain scans of Italian Association of Nuclear Medicine normative database (151 subjects, 67 Males, 84 Females, aged 20–84) were selected. Global mean, white matter, and pons activity were explored as normalization reference. We performed voxel-based and ROI analyses using SPM12 and IBM-SPSS software. Results: SPM proved a negative correlation between age and brain glucose metabolism involving frontal lobes, anterior-cingulate and insular cortices bilaterally. Narrower clusters were detected in lateral parietal lobes, precuneus, temporal pole and medial areas bilaterally. Normalizing on pons activity, we found a more significant negative correlation and no positive one. ROIs analysis confirmed SPM results. Moreover, a significant age × sex interaction effect was revealed, with worse metabolic reduction in posterior-cingulate cortices in females than males, especially in post-menopausal age. Conclusions: this study demonstrated an age-related metabolic reduction in frontal lobes and in some parieto-temporal areas more evident in females. Results suggested pons as the most appropriate normalization reference. Knowledge of age- and sex-related cerebral metabolic changes is critical to correctly interpreting brain 18F-FDG PET imaging.

Mapping covariance in brain FDG uptake to structural connectivity

PURPOSE

Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDG_cov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDG_cov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDG_cov and structural connectivity networks.

METHODS

We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDG_cov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking.

RESULTS

For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDG_cov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections.

CONCLUSION

Structural connectivity via white matter fiber tracts is a relevant substrate of FDG_cov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDG_cov connections.

A Characterization of the Effects of Minocycline Treatment During Adolescence on Structural, Metabolic, and Oxidative Stress Parameters in a Maternal Immune Stimulation Model of Neurodevelopmental Brain Disorders

BACKGROUND

Minocycline (MIN) is a tetracycline with antioxidant, anti-inflammatory, and neuroprotective properties. Given the likely involvement of inflammation and oxidative stress (IOS) in schizophrenia, MIN has been proposed as a potential adjuvant treatment in this pathology. We tested an early therapeutic window, during adolescence, as prevention of the schizophrenia-related deficits in the maternal immune stimulation (MIS) animal model.

METHODS

On gestational day 15, Poly I:C or vehicle was injected in pregnant Wistar rats. A total 93 male offspring received MIN (30 mg/kg) or saline from postnatal day (PND) 35-49. At PND70, rats were submitted to the prepulse inhibition test. FDG-PET and T2-weighted MRI brain studies were performed at adulthood. IOS markers were evaluated in frozen brain tissue.

RESULTS

MIN treatment did not prevent prepulse inhibition test behavioral deficits in MIS offspring. However, MIN prevented morphometric abnormalities in the third ventricle but not in the hippocampus. Additionally, MIN reduced brain metabolism in cerebellum and increased it in nucleus accumbens. Finally, MIN reduced the expression of iNOS (prefrontal cortex, caudate-putamen) and increased the levels of KEAP1 (prefrontal cortex), HO1 and NQO1 (amygdala, hippocampus), and HO1 (caudate-putamen).

CONCLUSIONS

MIN treatment during adolescence partially counteracts volumetric abnormalities and IOS deficits in the MIS model, likely via iNOS and Nrf2-ARE pathways, also increasing the expression of cytoprotective enzymes. However, MIN treatment during this peripubertal stage does not prevent sensorimotor gating deficits. Therefore, even though it does not prevent all the MIS-derived abnormalities evaluated, our results suggest the potential utility of early treatment with MIN in other schizophrenia domains.

Exploratory study of the long-term footprint of deep brain stimulation on brain metabolism and neuroplasticity in an animal model of obesity

Deep brain stimulation (DBS) is a powerful neurostimulation therapy proposed for the treatment of several neuropsychiatric disorders. However, DBS mechanism of action remains unclear, being its effects on brain dynamics of particular interest. Specifically, DBS reversibility is a major point of debate. Preclinical studies in obesity showed that the stimulation of the lateral hypothalamus (LH) and nucleus accumbens (NAcc), brain centers involved in satiety and reward circuits, are able to modulate the activity of brain structures impaired in this pathology. Nevertheless, the long-term persistence of this modulation after DBS withdrawal was unexplored. Here we examine the in vivo presence of such changes 1 month after LH- and NAcc-DBS, along with differences in synaptic plasticity, following an exploratory approach. Thus, both stimulated and non-stimulated animals with electrodes in the NAcc showed a common pattern of brain metabolism modulation, presumably derived from the electrodes' presence. In contrast, animals stimulated in the LH showed a relative metabolic invariance, and a reduction of neuroplasticity molecules, evidencing long-lasting neural changes. Our findings suggest that the reversibility or persistence of DBS modulation in the long-term depends on the selected DBS target. Therefore, the DBS footprint would be influenced by the stability achieved in the neural network involved during the stimulation.

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.

Topography of 11C-Pittsburgh compound B uptake in Alzheimer's disease: a voxel-based investigation of cortical and white matter regions

Objective:

To compare results of positron emission tomography (PET) with carbon-11-labeled Pittsburgh compound B (¹¹C-PIB) obtained with cerebellar or global brain uptake for voxel intensity normalization, describe the cortical sites with highest tracer uptake in subjects with mild Alzheimer’s disease (AD), and explore possible group differences in ¹¹C-PIB binding to white matter.

Methods:

¹¹C-PIB PET scans were acquired from subjects with AD (n=17) and healthy elderly controls (n=19). Voxel-based analysis was performed with statistical parametric mapping (SPM).

Results:

Cerebellar normalization showed higher ¹¹C-PIB uptake in the AD group relative to controls throughout the cerebral cortex, involving the lateral temporal, orbitofrontal, and superior parietal cortices. With global uptake normalization, greatest cortical binding was detected in the orbitofrontal cortex; decreased ¹¹C-PIB uptake in white matter was found in the posterior hippocampal region, corpus callosum, pons, and internal capsule.

Conclusion:

The present case-control voxelwise ¹¹C-PIB PET comparison highlighted the regional distribution of amyloid deposition in the cerebral cortex of mildly demented AD patients. Tracer uptake was highest in the orbitofrontal cortex. Decreased ¹¹C-PIB uptake in white-matter regions in this patient population may be a marker of white-matter damage in AD.

Impact of Global Mean Normalization on Regional Glucose Metabolism in the Human Brain

Because the human brain consumes a disproportionate fraction of the resting body's energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMR_glc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMR_glc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMR_glc alterations compared to control were detected in all conditions except in congenitally blind where regional CMR_glc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMR_glc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMR_glc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMR_glc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMR_glc data in health and disease.

Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution

¹⁸F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson's disease (PD) that allows us to examine postsynaptic dopamine D_2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of ¹⁸F-DMFP-PET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in ¹⁸F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess ¹⁸F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD. First, the data were segmented using an algorithm based on Hidden Markov Random Field. As a result, each neuroimage was divided into 4 maps according to the intensity and the neighborhood of the voxels. The maps were then individually normalized so that the shape of their histograms could be modeled by a Gaussian distribution with equal parameters for all the neuroimages. This approach was evaluated using a dataset with neuroimaging data from 87 parkinsonian patients. After these preprocessing steps, a Support Vector Machine classifier was used to separate idiopathic and non-idiopathic PD. Data preprocessed by the proposed method provided higher accuracy results than the ones preprocessed with previous approaches.

Differential Patterns of Subcortical Activity Evoked by Glial GLT-1 Blockade in Prelimbic and Infralimbic Cortex: Relationship to Antidepressant-Like Effects in Rats

BACKGROUND

Glutamatergic neurotransmission has emerged as a novel target in antidepressant drug development, with a critical role of the ventral anterior cingulate cortex. We recently reported that blockade of the astrocytic glutamate transporter GLT-1 with dihydrokainic acid in infralimbic cortex (rodent equivalent of ventral anterior cingulate cortex), but not in the adjacent prelimbic cortex, evoked robust antidepressant-like effects through α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation and increased serotonin release.

METHODS

2-deoxy-2-[18F]-fluoro-D-glucose-positron emission tomography and computed tomography in 36 male Wistar rats microinfused bilaterally in prelimbic cortex or infralimbic cortex with dihydrokainic acid or vehicle.

RESULTS

Dihydrokainic acid microinfusion in infralimbic cortex and prelimbic cortex evoked dramatically different regional patterns of subcortical activity. In infralimbic cortex, dihydrokainic acid selectively affected midbrain areas, whereas in prelimbic cortex it affected the basal ganglia, the thalamus, and both superior and inferior colliculi.

CONCLUSIONS

These results highlight the differential connectivity of infralimbic and prelimbic cortex with subcortical brain regions and support the involvement of infralimbic cortex-midbrain pathway in the antidepressant-like effects of dihydrokainic acid.

Cerebral Blood Flow and Aβ-Amyloid Estimates by WARM Analysis of [11C]PiB Uptake Distinguish among and between Neurodegenerative Disorders and Aging

Background: We report results of the novel Washout Allometric Reference Method (WARM) that uses estimates of cerebral blood flow and amyloid load from the same [¹¹C]Pittsburgh Compound B ([¹¹C]PiB) retention maps in brain to distinguish between patients with different forms dementia, including Alzheimer's disease, and healthy volunteers. The method introduces two approaches to the identification of brain pathology related to amyloid accumulation, (1) a novel analysis of amyloid binding based on the late washout of the tracer from brain tissue, and (2) the simultaneous estimation of absolute cerebral blood flow indices (sCBF) from the early accumulation of the tracer in brain tissue. Objective: We tested the hypothesis that a change of cerebral blood flow is correlated with the degree of tracer [¹¹C]PiB retention, reflecting dendritic spine pathology and consequent inhibition of brain energy metabolism and reduction of blood flow by neurovascular coupling in neurodegenerative disorders, including Alzheimer's disease. Methods: Previously reported images of [¹¹C]PiB retention in brain of 29 subjects with cognitive impairment or dementia [16 Alzheimer's Disease (AD), eight subjects with dementia with Lewy bodies (DLB), five patients with frontotemporal lobar degeneration (FTLD), five patients with mild cognitive impairment, and 29 age-matched healthy control subjects (HC)], underwent analysis of PiB delivery and retention by means of WARM for quantitation of [¹¹C]PiB's binding potentials (BP_ND) and correlated surrogate cerebral blood flow (sCBF) estimates, based on the [¹¹C]PiB images, compared to estimates by conventional Standard Uptake Value Ratio (SUVR) of [¹¹C]PiB retention with cerebellum gray matter as reference. Receiver Operating Characteristics (ROC) revealed the power of discrimination among estimates. Results: For AD, the discriminatory power of [¹¹C]PiB binding potential (BP_ND) by WARM exceeded the power of SUVR that in turn exceeded the power of sCBF estimates. Differences of [¹¹C]PiB binding and sCBF measures between AD and HC both were highly significant (p < 0.001). For all the dementia groups as a whole, sCBF estimates revealed the greatest discrimination between the patient and HC groups. WARM resolves a major issue of amyloid load quantification with [¹¹C]PiB in human brain by determining absolute sCBF and amyloid load measures from the same images. The two parameter approach provides key discriminary information in AD for which [¹¹C]PiB traditionally is used, as well as for the distinct flow deficits in FTLD, and the marked parietal and occipital lobe flow deficits in DLB. Conclusion: We conclude that WARM yields estimates of two important variables that together discriminate among patients with dementia, including AD, and healthy volunteers, with ROC that are superior to conventional methods of analysis. The distinction between estimates of flow and amyloid load from the same dynamic emission tomograms provides valuable pathogenetic information.

Longitudinal Cerebral Perfusion Changes in Parkinson's Disease with Subjective Cognitive Impairment

Background and Purpose

Although subjective cognitive impairment (SCI) is often accompanied by Parkinson's disease (PD) and may predict the development of mild cognitive impairment or dementia, longitudinal brain perfusion changes in PD patients with SCI remain to be elucidated. The current prospective study examined cerebral perfusion changes in PD patients with SCI using technetium-99m hexamethylpropylene amine oxime single photon emission computed tomography (SPECT).

Methods

Among 53 PD patients at baseline, 30 patients were classified into the PD with SCI group and 23 patients were assigned to the PD without SCI group. The mean follow-up interval was 2.3±0.9 years. The Mini-Mental State Examination, Clinical Dementia Rating, and Global Deterioration Scale were used to assess impairments in cognitive function. Brain SPECT images were acquired at baseline and follow-up.

Results

Significant differences between the two groups were not found for demographic variables, PD severity, or cognitive function at either baseline or follow-up. At baseline, the PD with SCI group showed decreased perfusion in the left angular gyrus compared to the PD without SCI group. Longitudinal analysis revealed widespread perfusion reductions primarily in the bilateral temporo-parieto-occipital areas and cerebellum in the PD with SCI group. Relative to the PD without SCI group, an excessive decrease of perfusion was found in the left middle frontal gyrus of the PD with SCI patients.

Conclusions

Our findings suggest that perfusion deficits in the middle frontal area may play an important role in the pathophysiology of SCI in PD.

Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers

A variety of imaging, neuropsychological, and genetic biomarkers have been suggested as potential biomarkers for the identification of mild cognitive impairment (MCI) in patients who later develop Alzheimer's disease (AD). Here, we systematically evaluated the most promising combinations of these biomarkers regarding discrimination between stable and converter MCI and reflection of disease staging. Alzheimer's Disease Neuroimaging Initiative data of AD (n = 144), controls (n = 112), stable (n = 265) and converter (n = 177) MCI, for which apolipoprotein E status, neuropsychological evaluation, and structural, glucose, and amyloid imaging were available, were included in this study. Naïve Bayes classifiers were built on AD and controls data for all possible combinations of these biomarkers, with and without stratification by amyloid status. All classifiers were then applied to the MCI cohorts. We obtained an accuracy of 76% for discrimination between converter and stable MCI with glucose positron emission tomography as a single biomarker. This accuracy increased to about 87% when including further imaging modalities and genetic information. We also identified several biomarker combinations as strong predictors of time to conversion. Use of amyloid validated training data resulted in increased sensitivities and decreased specificities for differentiation between stable and converter MCI when amyloid was included as a biomarker but not for other classifier combinations. Our results indicate that fully independent classifiers built only on AD and controls data and combining imaging, genetic, and/or neuropsychological biomarkers can more reliably discriminate between stable and converter MCI than single modality classifiers. Several biomarker combinations are identified as strongly predictive for the time to conversion to AD.

Data-driven identification of intensity normalization region based on longitudinal coherency of 18F-FDG metabolism in the healthy brain

OBJECTIVES

In brain ¹⁸F-FDG PET data intensity normalization is usually applied to control for unwanted factors confounding brain metabolism. However, it can be difficult to determine a proper intensity normalization region as a reference for the identification of abnormal metabolism in diseased brains. In neurodegenerative disorders, differentiating disease-related changes in brain metabolism from age-associated natural changes remains challenging. This study proposes a new data-driven method to identify proper intensity normalization regions in order to improve separation of age-associated natural changes from disease related changes in brain metabolism.

METHODS

127 female and 128 male healthy subjects (age: 20 to 79) with brain¹⁸F-FDG PET/CT in the course of a whole body cancer screening were included. Brain PET images were processed using SPM8 and were parcellated into 116 anatomical regions according to the AAL template. It is assumed that normal brain ¹⁸F-FDG metabolism has longitudinal coherency and this coherency leads to better model fitting. The coefficient of determination R² was proposed as the coherence coefficient, and the total coherence coefficient (overall fitting quality) was employed as an index to assess proper intensity normalization strategies on single subjects and age-cohort averaged data. Age-associated longitudinal changes of normal subjects were derived using the identified intensity normalization method correspondingly. In addition, 15 subjects with clinically diagnosed Parkinson's disease were assessed to evaluate the clinical potential of the proposed new method.

RESULTS

Intensity normalizations by paracentral lobule and cerebellar tonsil, both regions derived from the new data-driven coherency method, showed significantly better coherence coefficients than other intensity normalization regions, and especially better than the most widely used global mean normalization. Intensity normalization by paracentral lobule was the most consistent method within both analysis strategies (subject-based and age-cohort averaging). In addition, the proposed new intensity normalization method using the paracentral lobule generates significantly higher differentiation from the age-associated changes than other intensity normalization methods.

CONCLUSION

Proper intensity normalization can enhance the longitudinal coherency of normal brain glucose metabolism. The paracentral lobule followed by the cerebellar tonsil are shown to be the two most stable intensity normalization regions concerning age-dependent brain metabolism. This may provide the potential to better differentiate disease-related changes from age-related changes in brain metabolism, which is of relevance in the diagnosis of neurodegenerative disorders.

Optimizing PiB-PET SUVR change-over-time measurement by a large-scale analysis of longitudinal reliability, plausibility, separability, and correlation with MMSE

Quantitative measurements of change in β-amyloid load from Positron Emission Tomography (PET) images play a critical role in clinical trials and longitudinal observational studies of Alzheimer's disease. These measurements are strongly affected by methodological differences between implementations, including choice of reference region and use of partial volume correction, but there is a lack of consensus for an optimal method. Previous works have examined some relevant variables under varying criteria, but interactions between them prevent choosing a method via combined meta-analysis. In this work, we present a thorough comparison of methods to measure change in β-amyloid over time using Pittsburgh Compound B (PiB) PET imaging.

METHODS

We compare 1,024 different automated software pipeline implementations with varying methodological choices according to four quality metrics calculated over three-timepoint longitudinal trajectories of 129 subjects: reliability (straightness/variance); plausibility (lack of negative slopes); ability to predict accumulator/non-accumulator status from baseline value; and correlation between change in β-amyloid and change in Mini Mental State Exam (MMSE) scores.

RESULTS AND CONCLUSION

From this analysis, we show that an optimal longitudinal measure of β-amyloid from PiB should use a reference region that includes a combination of voxels in the supratentorial white matter and those in the whole cerebellum, measured using two-class partial volume correction in the voxel space of each subject's corresponding anatomical MR image.

Effects of arterial transit delay on cerebral blood flow quantification using arterial spin labeling in an elderly cohort

PURPOSE

To investigate whether measurement of arterial transit time (ATT) can improve the accuracy of arterial spin labeling (ASL) cerebral blood flow (CBF) quantification in an elderly cohort due to the potentially prolonged ATT in the cohort.

MATERIALS AND METHODS

We employed a 1-minute, low-resolution (12 mm in-plane), sequential multidelay ATT measurement (both with and without vessel suppression) approach to characterize and correct ATT errors in CBF imaging of an elderly, clinical cohort. In all, 140 nondemented subjects greater than 70 years old were imaged at 3T with a single delay, volumetric continuous ASL sequence and also with the fast ATT measurement method. Nine healthy young subjects (28 ± 6 years old) were also imaged.

RESULTS

ATTs measured without vessel suppression (superior frontal: 1.51 ± 0.27 sec) in the elderly were significantly shorter than those with suppression (P < 0.0001). Correction of CBF for ATT significantly increased average CBF in multiple brain regions where ATT was longer than the postlabeling delay (P < 0.01) and decreased intersubject variability of CBF in frontal, parietal, and occipital regions (P < 10^-8 ). Measured ATT with vessel suppression was significantly longer in the elderly subjects (eg, superior frontal: 1.76 ± 0.25 sec) compared to the younger adults (superior frontal: 1.59 ± 0.19 sec) in basal ganglia and frontal cortical regions (P < 0.05).

CONCLUSION

The ATT measurement is beneficial for imaging of elderly clinical populations. If ATT mapping is not feasible or available, postlabeling delays of 2-2.3 seconds should be used for elderly populations based on longest measured regional ATTs.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:472-481.

Longitudinal FDG-PET features for the classification of Alzheimer's disease

The majority of existing computer-aided diagnosis (CAD) schemes for Alzheimer's disease (AD) rely on the analysis of biomarkers at a single time-point, ignoring the progressive nature of the disorder. Recently, a method was proposed by Gray et al. [1] for the multi-region analysis of longitudinal fluorodeoxyglucose positron emission tomography (FDG-PET) images which reported classification improvements by using regional signal intensities combined with regional change over a 12 month period. In this paper we extend the approach proposed in [1] to the analysis of the entire brain pattern. Compared to [1], our method uses voxel-wise differences and avoids segmentation of the images into regions of interest. For our study, FDG-PET scans at the baseline and at 12-month follow-up of cognitively normal (CN), mild cognitive impairment (MCI) and AD subjects were retrieved from the Alzheimer's disease neuroimaging initiative (ADNI) database. For both AD and MCI identification, the best classification results were achieved by combining cross-sectional and longitudinal information rather than using only the cross-sectional data. Furthermore, the longitudinal voxel-based analysis outperformed multi-region analysis.

Long-Term Efficacy of Memantine in Parkinson' Disease Dementia: An 18-Month Prospective Perfusion Single Photon Emission Computed Tomography Preliminary Study

Background and Purpose

Although the treatment efficacy of memantine in Parkinson's disease dementia (PDD) has been reported after several weeks of administration, the long-term effects on brain perfusion and clinical symptoms remain unclear. The current study aimed to follow-up PDD patients after 18 months of memantine treatment using ^99mTc hexamethylpropylene amine oxime single photon emission computed tomography (SPECT).

Methods

A total of 15 patients with PDD and 11 healthy participants were recruited into this study and they were assessed with brain SPECT, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Global Deterioration Scale (GDS), and Neuropsychiatric Inventory (NPI). Differences in regional cerebral blood flow (rCBF) between the two groups were evaluated at baseline. After 18 months of memantine administration, changes in brain perfusion, severity of dementia, cognition, and neuropsychiatric disturbances were examined in the patients with PDD.

Results

The PDD group showed hypoperfusion in most of the cortical, subcortical, and cerebellar areas compared to healthy controls at baseline. At the follow-up, changes in rCBF, CDR (p=0.32), sum of box of CDR (p=0.49), MMSE (p=0.61), GDS (p=0.79), and NPI (p=0.23) were not significant in the PDD patients.

Conclusions

Our findings implicate that memantine may delay the progression of brain perfusion deficits and clinical symptoms of PDD in the long term.

Uniform distributions of glucose oxidation and oxygen extraction in gray matter of normal human brain: No evidence of regional differences of aerobic glycolysis

Regionally variable rates of aerobic glycolysis in brain networks identified by resting-state functional magnetic resonance imaging (R-fMRI) imply regionally variable adenosine triphosphate (ATP) regeneration. When regional glucose utilization is not matched to oxygen delivery, affected regions have correspondingly variable rates of ATP and lactate production. We tested the extent to which aerobic glycolysis and oxidative phosphorylation power R-fMRI networks by measuring quantitative differences between the oxygen to glucose index (OGI) and the oxygen extraction fraction (OEF) as measured by positron emission tomography (PET) in normal human brain (resting awake, eyes closed). Regionally uniform and correlated OEF and OGI estimates prevailed, with network values that matched the gray matter means, regardless of size, location, and origin. The spatial agreement between oxygen delivery (OEF≈0.4) and glucose oxidation (OGI ≈ 5.3) suggests that no specific regions have preferentially high aerobic glycolysis and low oxidative phosphorylation rates, with globally optimal maximum ATP turnover rates (VATP ≈ 9.4 µmol/g/min), in good agreement with (31)P and (13)C magnetic resonance spectroscopy measurements. These results imply that the intrinsic network activity in healthy human brain powers the entire gray matter with ubiquitously high rates of glucose oxidation. Reports of departures from normal brain-wide homogeny of oxygen extraction fraction and oxygen to glucose index may be due to normalization artefacts from relative PET measurements.

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.

Metabolic connectivity as index of verbal working memory

Positron emission tomography (PET) data are commonly analyzed in terms of regional intensity, while covariant information is not taken into account. Here, we searched for network correlates of healthy cognitive function in resting state PET data. PET with [(18)F]-fluorodeoxyglucose and a test of verbal working memory (WM) were administered to 35 young healthy adults. Metabolic connectivity was modeled at a group level using sparse inverse covariance estimation. Among 13 WM-relevant Brodmann areas (BAs), 6 appeared to be robustly connected. Connectivity within this network was significantly stronger in subjects with above-median WM performance. In respect to regional intensity, i.e., metabolism, no difference between groups was found. The results encourage examination of covariant patterns in FDG-PET data from non-neurodegenerative populations.

Washout allometric reference method (WARM) for parametric analysis of [(11)C]PIB in human brains

Rapid clearance and disappearance of a tracer from the circulation challenges the determination of the tracer's binding potentials in brain (BP_ND) by positron emission tomography (PET). This is the case for the analysis of the binding of radiolabeled [¹¹C]Pittsburgh Compound B ([¹¹C]PIB) to amyloid-β (Aβ) plaques in brain of patients with Alzheimer's disease (AD). To resolve the issue of rapid clearance from the circulation, we here introduce the flow-independent Washout Allometric Reference Method (WARM) for the analysis of washout and binding of [¹¹C]PIB in two groups of human subjects, healthy aged control subjects (HC), and patients suffering from AD, and we compare the results to the outcome of two conventional analysis methods. We also use the rapid initial clearance to obtain a surrogate measure of the rate of cerebral blood flow (CBF), as well as a method of identifying a suitable reference region directly from the [¹¹C]PIB signal. The difference of average absolute CBF values between the AD and HC groups was highly significant (P < 0.003). The CBF measures were not significantly different between the groups when normalized to cerebellar gray matter flow. Thus, when flow differences confound conventional measures of [¹¹C]PIB binding, the separate estimates of CBF and BP_ND provide additional information about possible AD. The results demonstrate the importance of data-driven estimation of CBF and BP_ND, as well as reference region detection from the [¹¹C]PIB signal. We conclude that the WARM method yields stable measures of BP_ND with relative ease, using only integration for noise reduction and no model regression. The method accounts for relative flow differences in the brain tissue and yields a calibrated measure of absolute CBF directly from the [¹¹C]PIB signal. Compared to conventional methods, WARM optimizes the Aβ plaque load discrimination between patients with AD and healthy controls (P = 0.009).

Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer's disease and mild cognitive impairment

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network's attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

Reference cluster normalization improves detection of frontotemporal lobar degeneration by means of FDG-PET

Positron emission tomography with [18F] fluorodeoxyglucose (FDG-PET) plays a well-established role in assisting early detection of frontotemporal lobar degeneration (FTLD). Here, we examined the impact of intensity normalization to different reference areas on accuracy of FDG-PET to discriminate between patients with mild FTLD and healthy elderly subjects. FDG-PET was conducted at two centers using different acquisition protocols: 41 FTLD patients and 42 controls were studied at center 1, 11 FTLD patients and 13 controls were studied at center 2. All PET images were intensity normalized to the cerebellum, primary sensorimotor cortex (SMC), cerebral global mean (CGM), and a reference cluster with most preserved FDG uptake in the aforementioned patients group of center 1. Metabolic deficits in the patient group at center 1 appeared 1.5, 3.6, and 4.6 times greater in spatial extent, when tracer uptake was normalized to the reference cluster rather than to the cerebellum, SMC, and CGM, respectively. Logistic regression analyses based on normalized values from FTLD-typical regions showed that at center 1, cerebellar, SMC, CGM, and cluster normalizations differentiated patients from controls with accuracies of 86%, 76%, 75% and 90%, respectively. A similar order of effects was found at center 2. Cluster normalization leads to a significant increase of statistical power in detecting early FTLD-associated metabolic deficits. The established FTLD-specific cluster can be used to improve detection of FTLD on a single case basis at independent centers – a decisive step towards early diagnosis and prediction of FTLD syndromes enabling specific therapies in the future.

Brain energy metabolism and blood flow differences in healthy aging

Cerebral metabolic rate of oxygen consumption (CMRO(2)), cerebral blood flow (CBF), and oxygen extraction fraction (OEF) are important indices of healthy aging of the brain. Although a frequent topic of study, changes of CBF and CMRO(2) during normal aging are still controversial, as some authors find decreases of both CBF and CMRO(2) but increased OEF, while others find no change, and yet other find divergent changes. In this reanalysis of previously published results from positron emission tomography of healthy volunteers, we determined CMRO(2) and CBF in 66 healthy volunteers aged 21 to 81 years. The magnitudes of CMRO(2) and CBF declined in large parts of the cerebral cortex, including association areas, but the primary motor and sensory areas were relatively spared. We found significant increases of OEF in frontal and parietal cortices, excluding primary motor and somatosensory regions, and in the temporal cortex. Because of the inverse relation between OEF and capillary oxygen tension, increased OEF can compromise oxygen delivery to neurons, with possible perturbation of energy turnover. The results establish a possible mechanism of progression from healthy to unhealthy brain aging, as the regions most affected by age are the areas that are most vulnerable to neurodegeneration.

Glucose metabolism in small subcortical structures in Parkinson's disease

OBJECTIVES

Evidence from experimental animal models of Parkinson's disease (PD) suggests a characteristic pattern of metabolic perturbation in discrete, very small basal ganglia structures. These structures are generally too small to allow valid investigation by conventional positron emission tomography (PET) cameras. However, the high-resolution research tomograph (HRRT) PET system has a resolution of 2 mm, sufficient for the investigation of important structures such as the pallidum and thalamic subnuclei.

MATERIALS AND METHODS

Using the HRRT, we performed [(18)F]-fluorodeoxyglucose (FDG) scans on 21 patients with PD and 11 age-matched controls. We employed three types of normalization: white matter, global mean, and data-driven normalization. We performed volume-of-interest analyses of small subcortical gray matter structures. Voxel-based comparisons were performed to investigate the extent of cortical hypometabolism.

RESULTS

The most significant level of relative subcortical hypermetabolism was detected in the external pallidum (GPe), irrespective of normalization strategy. Hypermetabolism was suggested also in the internal pallidum, thalamic subnuclei, and the putamen. Widespread cortical hypometabolism was seen in a pattern very similar to previously reported patterns in patients with PD.

CONCLUSION

The presence and extent of subcortical hypermetabolism in PD is dependent on type of normalization. However, the present findings suggest that PD, in addition to widespread cortical hypometabolism, is probably characterized by true hypermetabolism in the GPe. This finding was predicted by the animal 2-deoxyglucose autoradiography literature, in which high-magnitude hypermetabolism was also most robustly detected in the GPe.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease

Imaging biomarkers for Alzheimer's disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer's Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer's disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer's disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer's disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials.

Correlations between FDG PET glucose uptake-MRI gray matter volume scores and apolipoprotein E ε4 gene dose in cognitively normal adults: a cross-validation study using voxel-based multi-modal partial least squares

We previously introduced a voxel-based, multi-modal application of the partial least square algorithm (MMPLS) to characterize the linkage between patterns in a person's complementary complex datasets without the need to correct for multiple regional comparisons. Here we used it to demonstrate a strong correlation between MMPLS scores to characterize the linkage between the covarying patterns of fluorodeoxyglucose positron emission tomography (FDG PET) measurements of regional glucose metabolism and magnetic resonance imaging (MRI) measurements of regional gray matter associated with apolipoprotein E (APOE) ε4 gene dose (i.e., three levels of genetic risk for late-onset Alzheimer's disease (AD)) in cognitively normal, late-middle-aged persons. Coregistered and spatially normalized FDG PET and MRI images from 70% of the subjects (27 ε4 homozygotes, 36 ε4 heterozygotes and 67 ε4 non-carriers) were used in a hypothesis-generating MMPLS analysis to characterize the covarying pattern of regional gray matter volume and cerebral glucose metabolism most strongly correlated with APOE-ε4 gene dose. Coregistered and spatially normalized FDG PET and MRI images from the remaining 30% of the subjects were used in a hypothesis-testing MMPLS analysis to generate FDG PET-MRI gray matter MMPLS scores blind to their APOE genotype and characterize their relationship to APOE-ε4 gene dose. The hypothesis-generating analysis revealed covarying regional gray matter volume and cerebral glucose metabolism patterns that resembled those in traditional univariate analyses of AD and APOE-ε4 gene dose and PET-MRI scores that were strongly correlated with APOE-ε4 gene dose (p<1 × 10(-16)). The hypothesis-testing analysis results showed strong correlations between FDG PET-MRI gray matter scores and APOE-ε4 gene dose (p = 8.7 × 10(-4)). Our findings support the possibility of using the MMPLS to analyze complementary datasets from the same person in the presymptomatic detection and tracking of AD.

Cerebral oxygen metabolism in patients with early Parkinson's disease

AIM

Decreased activity of the mitochondrial electron transport chain (ETC) has been implicated in the pathogenesis of Parkinson's disease (PD). This model would most likely predict a decrease in the rate of cerebral oxygen consumption (CMRO(2)). To test this hypothesis, we compared CMRO(2) and cerebral blood flow (CBF) PET scans from PD patients and healthy controls.

MATERIALS AND METHODS

Nine early-stage PD patients and 15 healthy age-matched controls underwent PET scans for quantitative mapping of CMRO(2) and CBF. Between-group differences were evaluated for absolute data and intensity-normalized values.

RESULTS

No group differences were detected in regional magnitudes of CMRO(2) or CBF. Upon normalization using the reference cluster method, significant relative CMRO(2) decreases were evident in widespread prefrontal, parieto-occipital, and lateral temporal regions. Sensory-motor and subcortical regions, brainstem, and the cerebellum were spared. A similar pattern was evident in normalized CBF data, as described previously.

CONCLUSION

While the data did not reveal substantially altered absolute CMRO(2) in brain of PD patients, employing data-driven intensity normalization revealed widespread relative CMRO(2) decreases in cerebral cortex. The detected pattern was very similar to that reported in earlier CBF and CMRglc studies of PD, and in the CBF images from the same subjects. Thus, the present results are consistent with the occurrence of parallel declines in CMRO(2), CBF, and CMRglc in spatially contiguous cortical regions in early PD, and support the hypothesis that ETC dysfunction could be a primary pathogenic mechanism in early PD.

Empirical derivation of the reference region for computing diagnostic sensitive ¹⁸fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample

Careful selection of the reference region for non-quantitative positron emission tomography (PET) analyses is critically important for Region of Interest (ROI) data analyses. We introduce an empirical method of deriving the most suitable reference region for computing neurodegeneration sensitive (18)fluorodeoxyglucose (FDG) PET ratios based on the dataset collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Candidate reference regions are selected based on a heat map of the difference in coefficients of variation (COVs) of FDG ratios over time for each of the Automatic Anatomical Labeling (AAL) atlas regions normalized by all other AAL regions. Visual inspection of the heat map suggests that the portion of the cerebellum and vermis superior to the horizontal fissure is the most sensitive reference region. Analyses of FDG ratio data show increases in significance on the order of ten-fold when using the superior portion of the cerebellum as compared with the traditionally used full cerebellum. The approach to reference region selection in this paper can be generalized to other radiopharmaceuticals and radioligands as well as to other disorders where brain changes over time are hypothesized and longitudinal data is available. Based on the empirical evidence presented in this study, we demonstrate the usefulness of the COV heat map method and conclude that intensity normalization based on the superior portion of the cerebellum may be most sensitive to measuring change when performing longitudinal analyses of FDG-PET ratios as well as group comparisons in Alzheimer's disease. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.

Typical cerebral metabolic patterns in neurodegenerative brain diseases

The differential diagnosis of neurodegenerative brain diseases on clinical grounds is difficult, especially at an early disease stage. Several studies have found specific regional differences of brain metabolism applying [(18)F]-fluoro-deoxyglucose positron emission tomography (FDG-PET), suggesting that this method can assist in early differential diagnosis of neurodegenerative brain diseases.We have studied patients who had an FDG-PET scan on clinical grounds at an early disease stage and included those with a retrospectively confirmed diagnosis according to strictly defined clinical research criteria. Ninety-six patients could be included of which 20 patients with Parkinson's disease (PD), 21 multiple system atrophy (MSA), 17 progressive supranuclear palsy (PSP), 10 corticobasal degeneration (CBD), 6 dementia with Lewy bodies (DLB), 15 Alzheimer's disease (AD), and 7 frontotemporal dementia (FTD). FDG PET images of each patient group were analyzed and compared to18 healthy controls using Statistical Parametric Mapping (SPM5).Disease-specific patterns of relatively decreased metabolic activity were found in PD (contralateral parietooccipital and frontal regions), MSA (bilateral putamen and cerebellar hemispheres), PSP (prefrontal cortex and caudate nucleus, thalamus, and mesencephalon), CBD (contralateral cortical regions), DLB (occipital and parietotemporal regions), AD (parietotemporal regions), and FTD (frontotemporal regions).The integrated method addressing a spectrum of various neurodegenerative brain diseases provided means to discriminate patient groups also at early disease stages. Clinical follow-up enabled appropriate patient inclusion. This implies that an early diagnosis in individual patients can be made by comparing each subject's metabolic findings with a complete database of specific disease related patterns.

Characterizing Alzheimer's disease using a hypometabolic convergence index

This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.

Molecular imaging and the neuropathologies of Parkinson's disease

The main motor symptoms of Parkinson's disease (PD) are linked to degeneration of the nigrostriatal dopamine (DA) fibers, especially those innervating the putamen. This degeneration can be assessed in molecular imaging studies with presynaptic tracers such as [(18)F]-fluoro-L-DOPA (FDOPA) and ligands for DA transporter ligands. However, the pathologies of PD are by no means limited to nigrostriatal loss. Results of post mortem and molecular imaging studies reveal parallel degenerations of cortical noradrenaline (NA) and serotonin (5-HT) innervations, which may contribute to affective and cognitive changes of PD. Especially in advanced PD, cognitive impairment can come to resemble that seen in Alzheimer's dementia, as can the degeneration of acetylcholine innervations arising in the basal forebrain. The density of striatal DA D(2) receptors increases in early untreated PD, consistent with denervation upregulation, but there is an accelerated rate of DA receptor loss as the disease advances. Animal studies and post mortem investigations reveal changes in brain opioid peptide systems, but these are poorly documented in imaging studies of PD. Relatively minor changes in the binding sites for GABA are reported in cortex and striatum of PD patients. There remains some controversy about the expression of the 18 kDa translocator protein (TSPO) in activated microglia as an indicator of an active inflammatory component of neurodegeneration in PD. A wide variety of autonomic disturbances contribute to the clinical syndrome of PD; the degeneration of myocardial sympathetic innervation can be revealed in SPECT studies of PD patients with autonomic failure. Considerable emphasis has been placed on investigations of cerebral blood flow and energy metabolism in PD. Due to the high variance of these physiological estimates, researchers have often employed normalization procedures for the sensitive detection of perturbations in relatively small patient groups. However, a widely used normalization to the global mean must be used with caution, as it can result in spurious findings of relative hypermetabolic changes in subcortical structures. A meta-analysis of the quantitative studies to date shows that there is in fact widespread hypometabolism and cerebral blood flow in the cerebral cortex, especially in frontal cortex and parietal association areas. These changes can bias the use of global mean normalization, and probably represent the pathophysiological basis of the cognitive impairment of PD.

FDG-PET mapping the brain substrates of visuo-constructive processing in Alzheimer's disease

The anatomical basis of visuo-constructive impairment in AD is widely unexplored. FDG-PET can be used to determine functional neuronal networks underlying specific cognitive performance in the human brain. In the present study, we determined the pattern of cortical metabolism that was associated with visuo-constructive performance in AD. We employed two widely used visuo-constructive tests that differ in their demand on visual perception and processing capacity. Resting state FDG-PET scans were obtained in 29 probable AD patients, and cognitive tests were administered. We made a voxel-based regression analysis of FDG uptake to scores in visual test performance, using the SPM5 software. Performance in the CERAD Drawing test correlated with FDG uptake in the bilateral inferior temporal gyri, bilateral precuneus, right cuneus, right supramarginal gyrus and right middle temporal gyrus covering areas of dorsal and ventral visual streams. In contrast, performance in the more complex RBANS Figure Copy test correlated with FDG uptake in the bilateral fusiform gyri, right inferior temporal gyrus, left anterior cingulate gyrus, left parahippocampal gyrus, right middle temporal gyrus and right insula, encompassing the ventral visual stream and areas of higher-level visual processing. The study revealed neuronal networks underlying impaired visual test performance in AD. The extent of involvement of visual and higher order association cortex increased with greater test complexity. From a clinical point of view, both of these widely used visual tests evaluate the integrity of complementary cortical networks and may contribute complementary information on the integrity of visual processing in AD.

Cortical hypometabolism and hypoperfusion in Parkinson's disease is extensive: probably even at early disease stages

Recent cerebral blood flow (CBF) and glucose consumption (CMRglc) studies of Parkinson's disease (PD) revealed conflicting results. Using simulated data, we previously demonstrated that the often-reported subcortical hypermetabolism in PD could be explained as an artifact of biased global mean (GM) normalization, and that low-magnitude, extensive cortical hypometabolism is best detected by alternative data-driven normalization methods. Thus, we hypothesized that PD is characterized by extensive cortical hypometabolism but no concurrent widespread subcortical hypermetabolism and tested it on three independent samples of PD patients. We compared SPECT CBF images of 32 early-stage and 33 late-stage PD patients with that of 60 matched controls. We also compared PET FDG images from 23 late-stage PD patients with that of 13 controls. Three different normalization methods were compared: (1) GM normalization, (2) cerebellum normalization, (3) reference cluster normalization (Yakushev et al.). We employed standard voxel-based statistics (fMRIstat) and principal component analysis (SSM). Additionally, we performed a meta-analysis of all quantitative CBF and CMRglc studies in the literature to investigate whether the global mean (GM) values in PD are decreased. Voxel-based analysis with GM normalization and the SSM method performed similarly, i.e., both detected decreases in small cortical clusters and concomitant increases in extensive subcortical regions. Cerebellum normalization revealed more widespread cortical decreases but no subcortical increase. In all comparisons, the Yakushev method detected nearly identical patterns of very extensive cortical hypometabolism. Lastly, the meta-analyses demonstrated that global CBF and CMRglc values are decreased in PD. Based on the results, we conclude that PD most likely has widespread cortical hypometabolism, even at early disease stages. In contrast, extensive subcortical hypermetabolism is probably not a feature of PD.

Subcortical elevation of metabolism in Parkinson's disease--a critical reappraisal in the context of global mean normalization

In a recent issue of NeuroImage, we presented evidence that biased global mean (GM) normalization of brain PET data can generate the appearance of subcortical foci with relative hypermetabolism in patients with Parkinson's disease (PD), and other degenerative disorders. In a commentary to our article, Ma and colleagues presented a study seeking to establish that a pattern of widespread hypermetabolism, known as the Parkinson's disease related pattern (PDRP) is a genuine metabolic feature of PD. In the present paper, we respond to the arguments presented by Ma et al., and we provide a critical reappraisal of the evidence for the existence of the PDRP. To this end, we present new analyses of PET data sets, which demonstrate that very similar patterns of relative subcortical increases are seen in PD, Alzheimer's disease, hepatic encephalopathy, healthy aging, and simulation data. Furthermore, longitudinal studies of PD previously reported relative hypermetabolism in very small anatomical structures such as the subthalamic nucleus. We now demonstrate how focal hypermetabolism attributed to small nuclei can similarly arise as a consequence of GM normalization. Finally, we give a comprehensive summary of the entire deoxyglucose autoradiography literature on acquired parkinsonism in experimental animals. Based on this evidence, we conclude that (1) there is no quantitative evidence for widespread subcortical hypermetabolism in PD, (2) very similar patterns of subcortical hyperactivity are evident in various other brain disorders whenever GM normalization is utilized, and (3) the PDRP is not evident in animal models of PD. In the absence of quantitative evidence for the PDRP, our alternative interpretation of normalization bias seems the more parsimonious explanation for the reports of relative hypermetabolism in PD.