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Currently available data show mixed results as to whether the processing of emotional information has the same characteristics in the native (L1) as in the second language (L2) of bilinguals. We conducted a functional magnetic resonance imaging (fMRI) experiment to shed light on the neurocognitive mechanisms underlying bilinguals’ emotional processing in L1 and L2 during an emotional interference task (i.e., the Emotional Stroop Task – EST). Our sample comprised proficient Italian-English bilinguals who learned their L2 during childhood mainly in instructional rather than immersive contexts. In spite of no detectable behavioural effects, we found stronger brain activations for L1 versus L2 emotional words in sectors of the posteromedial cortex involved in attention modulation, episodic memory, and affective processing. While fMRI findings are consistent with the hypothesis of a stronger emotional resonance when processing words in a native language, our overall pattern of results points to the different sensitivity of behavioural and hemodynamic responses to emotional information in the two languages of bilingual speakers.

Introduction: Brain hypometabolism patterns have been previously associated with cognitive decline in Parkinson's disease (PD). Our aim is to evaluate the impact of single-subject fluorodeoxyglucose (FDG)-PET brain hypometabolism on long-term cognitive and motor outcomes in PD. Methods: Forty-nine non-demented PD patients with baseline brain FDG-PET data underwent an extensive clinical follow-up for 8 years. The ability of FDG-PET to predict long-term cognitive and motor progression was evaluated using Cox regression and mixed ANCOVA models. Results: Participants were classified according to FDG-PET pattern in PD with typical (n = 26) and atypical cortical metabolism (n = 23). Patients with atypical brain hypometabolic patterns showed higher incidence of dementia (60% vs 3%; HR = 18.3), hallucinations (56% vs 7%, HR = 7.3) and faster motor decline compared to typical pattern group. Conclusion: This study argues for specific patterns of FDG-PET cortical hypometabolism in PD as a prognostic marker for long term cognitive and motor outcomes at single-subject level.

Computational morphometry of magnetic resonance images represents a powerful tool for studying macroscopic differences in human brains. In the present study (N participants = 829), we combined different techniques and measures of brain morphology to investigate one of the most compelling topics in neuroscience: sexual dimorphism in human brain structure. When accounting for overall larger male brains, results showed limited sex differences in gray matter volume (GMV) and surface area. On the other hand, we found larger differences in cortical thickness, favoring both males and females, arguably as a result of region-specific differences. We also observed higher values of fractal dimension, a measure of cortical complexity, for males versus females across the four lobes. In addition, we applied source-based morphometry, an alternative method for measuring GMV based on the independent component analysis. Analyses on independent components revealed higher GMV in fronto-parietal regions, thalamus and caudate nucleus for females, and in cerebellar- temporal cortices and putamen for males, a pattern that is largely consistent with previous findings.

Previous brain functional specialization evidence has shown that both aware and unaware visual processing of manipulable objects activate left premotor, parietal, and posterior temporal cortices, which are thought to constitute object-directed action and object-function processing streams. An open question is whether, both under supraliminal and subliminal processing conditions, there is directional spread of activation along these functional streams, leading to causal inter-regional connectivity effects. In this study, we used Dynamic Causal Modelling to estimate the effective connectivity influences within the premotor-parieto-temporal network, as a function of factorial contrasts for Manipulability (manipulable vs non-manipulable objects) and Perceptual Awareness (above vs below perceptual threshold). We modeled forward and backward connections originating from visual area V4, as a region underlying object texture segregation, and spreading through the left premotor-parieto-temporal network. Both above and below perceptual threshold, the visual processing of manipulable objects was associated with a specific increase of reciprocal effective connectivity coupling among left premotor-parieto-temporal regions. Aware and unaware manipulable object processing differed from each other for their distinct patterns of top-down activation enhancement exerted, in the former case, by left premotor-parieto-temporal regions on area V4 and, in the latter case, by left premotor on temporal regions. Although it is only under aware processing conditions that effective connectivity in the action representation system may promote object visual contour segregation in area V4, our results suggest that the encoding of object-action and object-function information can occur through left-hemispheric premotor, parietal, and temporal causal interdependencies, even when the object is not consciously perceived.

Epilepsy is classically considered a childhood disease. However, it represents the third most frequent neurological condition in the elderly, following stroke, and dementia. With the progressive aging of the general population, the number of patients with Late-Onset Epilepsy (LOE) is constantly growing, with important economic and social consequences, in particular for the more developed countries where the percentage of elderly people is higher. The most common causes of LOE are structural, mainly secondary to cerebrovascular or infectious diseases, brain tumors, trauma, and metabolic or toxic conditions. Moreover, there is a growing body of evidence linking LOE with neurodegenerative diseases, particularly Alzheimer's disease (AD). However, despite a thorough characterization, the causes of LOE remain unknown in a considerable portion of patients, thus termed as Late-Onset Epilepsy of Unknown origin (LOEU). In order to identify the possible causes of the disease, with an important impact in terms of treatment and prognosis, LOE patients should always undergo an exhaustive phenotypic characterization. In this work, we provide a detailed review of the main clinical and instrumental techniques for the adequate characterization of LOE patients in the clinical practice. This work aims to provide an easy and effective tool that supports routine activity of the clinicians facing LOE.

Objectives: Attention-deficit hyperactivity disorder (ADHD) in adulthood shows high co-occurrence rates with cocaine use disorder (CoUD). The self-medication hypothesis (SMH) provides a theoretical explanation for this comorbidity. This study investigates the neurobiological mechanisms that could support SMH in adult patients with attention-deficit hyperactivity disorder with cocaine use disorder (ADHD–CoUD). Materials and Methods: We included 19 ADHD–CoUD patients (84.2% male; age: 32.11 years [7.18]) and 16 CoUD patients (68.7% male; age: 36.63 years [8.12]). All subjects underwent a fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET) brain scan. We tested brain metabolism differences between ADHD–CoUD and CoUD patients using voxel-based and regions of interest (ROIs)-based analyses. The correlation between dependence/abstinence duration and regional brain metabolism was also assessed in the two groups. Lastly, we investigated the integrity of brain metabolic connectivity of mesocorticolimbic and nigrostriatal dopaminergic systems, and large-scale brain networks involved in ADHD and addictions. Results: The voxel-wise and ROIs-based approaches showed that ADHD–CoUD patients had a lower metabolism in the thalamus and increased metabolism in the amygdala and parahippocampus, bilaterally, than CoUD subjects and healthy controls (HCs). Metabolism in the thalamus negatively correlated with years of dependence in ADHD–CoUD patients. Moreover, connectivity analyses revealed that ADHD–CoUD patients had a more preserved metabolic connectivity than CoUD patients in the dopaminergic networks and large-scale networks involved in self-regulation mechanisms of attention and behaviors (i.e., anterior default mode network [ADMN], executive network [ECN], and anterior salience network [aSAN]). Conclusions: We demonstrated distinct neuropathological substrates underlying substance-use behaviors in ADHD–CoUD and CoUD patients. Furthermore, we provided neurobiol

Background: The amnestic presentation of mild cognitive impairment (aMCI) represents the most common prodromal stage of Alzheimer's disease (AD) dementia. There is, however, some evidence of aMCI with typical amnestic syndrome but showing long-term clinical stability. The ability to predict stability or progression to dementia in the aMCI condition is important, particularly for the selection of candidates in clinical trials. We aimed to establish the role of in vivo biomarkers, as assessed by cerebrospinal fluid (CSF) measures and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging, in predicting prognosis in a large aMCI cohort. Methods: We conducted a retrospective study, including 142 aMCI subjects who had a long follow-up (4–19 years), baseline CSF data and [18F]FDG-PET scans individually assessed by validated voxel-based procedures, classifying subjects into either limbic-predominant or AD-like hypometabolism patterns. Results: The two aMCI cohorts were clinically comparable at baseline. At follow-up, the aMCI group with a limbic-predominant [18F]FDG-PET pattern showed clinical stability over a very long follow-up (8.20 ± 3.30 years), no decline in Mini-Mental State Examination score, and only 7% conversion to dementia. Conversely, the aMCI group with an AD-like [18F]FDG-PET pattern had a high rate of dementia progression (86%) over a shorter follow-up (6.47 ± 2.07 years). Individual [18F]FDG-PET hypometabolism patterns predicted stability or conversion with high accuracy (area under the curve = 0.89), sensitivity (0.90) and specificity (0.89). In the limbic-predominant aMCI cohort, CSF biomarkers showed large variability and no prognostic value. Conclusions: In a large series of clinically comparable subjects with aMCI at baseline, the specific [18F]FDG-PET limbic-predominant hypometabolism pattern was associated with clinical stability, making progression to AD very unlikely. The identificatio

Background: Preclinical and pathology evidence suggests an involvement of brain dopamine (DA) circuitry in Alzheimer’s disease (AD). We in vivo investigated if, when, and in which target regions [123I]FP-CIT-SPECT regional binding and molecular connectivity are damaged along the AD course. Methods: We retrospectively selected 16 amyloid-positive subjects with mild cognitive impairment due to AD (AD-MCI), 22 amyloid-positive patients with probable AD dementia (AD-D), and 74 healthy controls, all with available [123I]FP-CIT-SPECT imaging. We tested whether nigrostriatal vs. mesocorticolimbic dopaminergic targets present binding potential loss, via MANCOVA, and alterations in molecular connectivity, via partial correlation analysis. Results were deemed significant at p < 0.05, after Bonferroni correction for multiple comparisons. Results: We found significant reductions of [123I]FP-CIT binding in both AD-MCI and AD-D compared to controls. Binding reductions were prominent in the major targets of the ventrotegmental-mesocorticolimbic pathway, namely the ventral striatum and the hippocampus, in both clinical groups, and in the cingulate gyrus, in patients with dementia only. Within the nigrostriatal projections, only the dorsal caudate nucleus showed reduced [123I]FP-CIT binding, in both groups. Molecular connectivity assessment revealed a widespread loss of inter-connections among subcortical and cortical targets of the mesocorticolimbic network only (poor overlap with the control group as expressed by a Dice coefficient ≤ 0.25) and no alterations of the nigrostriatal network (high overlap with controls, Dice coefficient = 1). Conclusion: Local- and system-level alterations of the mesocorticolimbic dopaminergic circuitry characterize AD, already in prodromal disease phases. These results might foster new therapeutic strategies for AD. The clinical correlates of these findings deserve to be carefully considered within the emergence of both neuropsychiatric symptoms an

Purpose: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. Methods: Selection of HC images was based on visual rating, after Cook’s distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB. Results: Two-step Cook’s distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. Conclusions: The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.

Purpose We evaluated brain metabolic dysfunctions and associations with neurological and biological parameters in acute, subacute and chronic COVID-19 phases to provide deeper insights into the pathophysiology of the disease.Methods Twenty-six patients with neurological symptoms (neuro-COVID-19) and [F-18]FDG-PET were included. Seven patients were acute (< 1 month (m) after onset), 12 subacute (4 >= 1-m, 4 >= 2-m and 4 >= 3-m) and 7 with neuro-post-COVID-19 (3 >= 5-m and 4 >= 7-9-m). One patient was evaluated longitudinally (acute and 5-m). Brain hypo- and hypermetabolism were analysed at single-subject and group levels. Correlations between severity/extent of brain hypo- and hypermetabolism and biological (oxygen saturation and C-reactive protein) and clinical variables (global cognition and Body Mass Index) were assessed.Results The "fronto-insular cortex" emerged as the hypometabolic hallmark of neuro-COVID-19. Acute patients showed the most severe hypometabolism affecting several cortical regions. Three-m and 5-m patients showed a progressive reduction of hypometabolism, with limited frontal clusters. After 7-9 months, no brain hypometabolism was detected. The patient evaluated longitudinally showed a diffuse brain hypometabolism in the acute phase, almost recovered after 5 months. Brain hypometabolism correlated with cognitive dysfunction, low blood saturation and high inflammatory status. Hypermetabolism in the brainstem, cerebellum, hippocampus and amygdala persisted over time and correlated with inflammation status.Conclusion Synergistic effects of systemic virus-mediated inflammation and transient hypoxia yield a dysfunction of the fronto-insular cortex, a signature of CNS involvement in neuro-COVID-19. This brain dysfunction is likely to be transient and almost reversible. The long-lasting brain hypermetabolism seems to reflect persistent inflammation processes.

Background and ObjectiveThis was a multicenter study aimed at investigating the characteristics of cognitive decline, neuropsychiatric symptoms, and brain imaging in individuals with subjective cognitive decline (SCD) and subtle cognitive decline (pre-mild cognitive impairment [pre-MCI]).MethodsData were obtained from the Network-AD project (NET-2011-02346784). The included participants underwent baseline cognitive and neurobehavioral evaluation, FDG-PET, and amyloid PET. We used principal component analysis (PCA) to identify independent neuropsychological and neuropsychiatric dimensions and their association with brain metabolism.ResultsA total of 105 participants (SCD = 49, pre-MCI = 56) were included. FDG-PET was normal in 45% of participants and revealed brain hypometabolism in 55%, with a frontal-like pattern as the most frequent finding (28%). Neuropsychiatric symptoms emerging from the Neuropsychiatric Inventory and the Starkstein Apathy Scale were highly prevalent in the whole sample (78%). An abnormal amyloid load was detected in the 18% of the participants who underwent amyloid PET (n = 60). PCA resulted in 3 neuropsychological factors: (1) executive/visuomotor, correlating with hypometabolism in frontal and occipital cortices and basal ganglia; (2) memory, correlating with hypometabolism in temporoparietal regions; and (3) visuospatial/constructional, correlating with hypometabolism in frontoparietal cortices. Two factors emerged from the neuropsychiatric PCA: (1) affective, correlating with hypometabolism in orbitofrontal and cingulate cortex and insula; (2) hyperactive/psychotic, correlating with hypometabolism in frontal, temporal, and parietal regions.DiscussionFDG-PET evidence suggests either normal brain function or different patterns of brain hypometabolism in SCD and pre-MCI. These results indicate that SCD and pre-MCI represent heterogeneous populations. Different neuropsychological and neuropsychiatric profiles emerged, which correlated with neu

Introduction: Dementia with Lewy bodies (DLB) may represent a diagnostic challenge, since its clinical picture overlaps with other dementia. Two toolkits have been developed to aid the clinician to diagnose DLB: the Lewy Body Composite Risk Score (LBCRS) and the Assessment Toolkit for DLB (AT-DLB). We aim to evaluate the reliability of these two questionnaires, and their ability to enhance the interpretation of the international consensus diagnostic criteria. Methods: LBCRS and AT-DLB were distributed to 135 Italian Neurological Centers for Cognitive Decline and Dementia (CDCDs), with the indication to administer them to all patients with dementia referred within the subsequent 3 months. We asked to subsequently apply consensus criteria for DLB diagnosis, to validate the diagnostic accuracy of the two toolkits. Results: A total of 23 Centers joined the study; 1854 patients were enrolled. We found a prevalence of possible or probable DLB of 13% each (26% total), according to the consensus criteria. LBCRS toolkit showed good reliability, with a Cronbach alpha of 0.77, stable even after removing variables from the construct. AT-DLB toolkit Cronbach alpha was 0.52 and, after the subtraction of the “cognitive fluctuation” criterion, was only 0.31. Accuracy, sensitivity, and specificity were higher for LBCRS vs. AT-DLB. However, when simultaneously considered in the logistic models, AT-DLB showed a better performance (p < 0.001). Overall, the concordance between LBCRS positive and AT-DLB possible/probable was of 78.02% Conclusions: In a clinical setting, the LBCRS and AT-DLB questionnaires have good accuracy for DLB diagnosis.

Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 k

Objective: Neuroinflammation is considered a key driver for neurodegeneration in several neurological diseases, including amyotrophic lateral sclerosis (ALS). SOD1 mutations cause about 20% of familial ALS, and related pathology might generate microglial activation triggering neurodegeneration. 11C-PK11195 is the prototypical and most validated PET radiotracer, targeting the 18-kDa translocator protein which is overexpressed in activated microglia. In this study, we investigated microglia activation in asymptomatic (ASYM) and symptomatic (SYM) SOD1 mutated carriers, by using 11C-PK11195 and PET imaging. Methods: We included 20 subjects: 4 ASYM-carriers, neurologically normal, 6 SYM-carriers with probable ALS, and 10 healthy controls. A receptor parametric mapping procedure estimated 11C-PK11195 binding potentials and voxel-wise statistical comparisons were performed at group and single-subject levels. Results: Both the SYM- and ASYM-carriers showed significant microglia activation in cortical and subcortical structures, with variable patterns at individual level. Clusters of activation were present in occipital and temporal regions, cerebellum, thalamus, and medulla oblongata. Notably, SYM-carriers showed microglia activation also in supplementary and primary motor cortices and in the somatosensory regions. Interpretation: In vivo neuroinflammation occurred in all SOD1 mutated cases since the presymptomatic stages, as shown by a significant cortical and subcortical microglia activation. The involvement of sensorimotor cortex became evident at the symptomatic disease stage. Although our data indicate the role of in vivo PET imaging for assessing resident microglia in the investigation of SOD1-ALS pathophysiology, further studies are needed to clarify the temporal and spatial dynamics of microglia activation and its relationship with neurodegeneration.

OBJECTIVE: To investigate the types of errors produced in a picture naming task by patients with neurodegenerative dementia due to different etiologies and their neural correlates. METHODS: The same standardized picture naming test was administered to a consecutive sample of patients (n = 148) who had been studied with [18F] FDG-PET. The errors were analyzed in 3 categories (visual, semantic, and phonologic). The PET data were analyzed using an optimized single-subject procedure, and the statistical parametric mapping multiple regression design was used to explore the correlation between each type of error and brain hypometabolism in the whole group. Metabolic connectivity analyses were run at the group level on 7 left hemisphere cortical areas corresponding to an a priori defined naming network. RESULTS: Semantic errors were predominant in most patients, independent of clinical diagnosis. In the whole group analysis, visual errors correlated with hypometabolism in the right inferior occipital lobe and in the left middle occipital lobe. Semantic errors correlated with hypometabolism in the left fusiform gyrus, the inferior and middle temporal gyri, and the temporal pole. Phonologic errors were associated with hypometabolism in the left superior and middle temporal gyri. Both positive (occipital-posterior fusiform) and negative (anterior fusiform gyrus and the superior anterior temporal lobe) connectivity changes were associated with semantic errors. CONCLUSIONS: Naming errors reflect the dysfunction of separate stages of the naming process and are specific markers for different patterns of brain involvement. These correlations are not limited to primary progressive aphasia but extend to other neurodegenerative dementias.

Quantification of amyloid PET studies is most accurate if regions of interest (ROIs) are not affected by the presence of cerebrospinal fluid. Patients with high amyloid load often have great atrophy, therefore, the use of atlas-based ROIs, instead of patient specific anatomy, can underestimate amyloid load, leading to a bias. Traditionally, this can be overcome only using MR anatomical sequences, which are burdensome and might not be ideal to be performed for each patient in the clinical routine. In this work, we propose to overcome this issue by using a method based on deep learning. As CT scans provide anatomical information, even at the very low doses used for PET attenuation correction, we propose the use of such a scan, together with the PET one, for a U-NET based segmentation. The approach achieves a median DICE score of 77% on a validation cohort of N=20 patients, even when using only N=14 patients in the training dataset. A dedicated data augmentation strategy is used, and the individual contribution of each modality is analyzed. We find that the joint effect of PET and CT is beneficial (median DICE: PET only 73.0%, CT only 74%). A near perfect correlation with MR-based quantification was also found.

Background: Alzheimer's disease (AD) is characterized by an involvement of brain dopamine (DA) circuitry, the presence of which has been associated with emergence of both neuropsychiatric symptoms and cognitive deficits. Objective: In order to investigate whether and how the DA pathways are involved in the pathophysiology of AD, we assessed by in vivo neuroimaging the structural and metabolic alterations of subcortical and cortical DA pathways and targets. Methods: We included 54 healthy control participants, 53 amyloid-positive subjects with mild cognitive impairment due to AD (MCI-AD), and 60 amyloid-positive patients with probable dementia due to AD (ADD), all with structural 3T MRI and 18F-FDG-PET scans. We assessed MRI-based gray matter reductions in the MCI-AD and ADD groups within an anatomical a priori-defined Nigrostriatal and Mesocorticolimbic DA pathways, followed by 18F-FDG-PET metabolic connectivity analyses to evaluate network-level metabolic connectivity changes. Results: We found significant tissue loss in the Mesocorticolimbic over the Nigrostriatal pathway. Atrophy was evident in the ventral striatum, orbitofrontal cortex, and medial temporal lobe structures, and already plateaued in the MCI-AD stage. Degree of atrophy in Mesocorticolimbic regions positively correlated with the severity of depression, anxiety, and apathy in MCI-AD and ADD subgroups. Additionally, we observed significant alterations of metabolic connectivity between the ventral striatum and fronto-cingulate regions in ADD, but not in MCI-AD. There were no metabolic connectivity changes within the Nigrostriatal pathway. Conclusion: Our cross-sectional data support a clinically-meaningful, yet stage-dependent, involvement of the Mesocorticolimbic system in AD. Longitudinal and clinical correlation studies are needed to further establish the relevance of DA system involvement in AD.

Background: Early-onset Alzheimer's disease (EOAD) is characterized by young age of onset (< 65 years), severe neurodegeneration, and rapid disease progression, thus differing significantly from typical late-onset Alzheimer's disease. Growing evidence suggests a primary role of neuroinflammation in AD pathogenesis. However, the role of microglia activation in EOAD remains a poorly explored field. Investigating microglial activation and its influence on the development of synaptic dysfunction and neuronal loss in EOAD may contribute to the understanding of its pathophysiology and to subject selection in clinical trials. In our study, we aimed to assess the amount of neuroinflammation and neurodegeneration and their relationship in EOAD patients, through positron emission tomography (PET) measures of microglia activation and brain metabolic changes. Methods: We prospectively enrolled 12 EOAD patients, classified according to standard criteria, who underwent standard neurological and neuropsychological evaluation, CSF analysis, brain MRI, and both [18F]-FDG PET and [11C]-(R)-PK11195 PET. Healthy controls databases were used for statistical comparison. [18F]-FDG PET brain metabolism in single subjects and as a group was assessed by an optimized SPM voxel-wise single-subject method. [11C]-PK11195 PET binding potentials were obtained using reference regions selected with an optimized clustering procedure followed by a parametric analysis. We performed a topographic interaction analysis and correlation analysis in AD-signature metabolic dysfunctional regions and regions of microglia activation. A network connectivity analysis was performed using the interaction regions of hypometabolism and [11C]-PK11195 PET BP increases. Results: EOAD patients showed a significant and extended microglia activation, as [11C]-PK11195 PET binding potential increases, and hypometabolism in typical AD-signature brain regions, i.e., temporo-parietal cortex, with additional variable frontal a

Recent studies in neurodegenerative conditions have increasingly highlighted that the same neuropathology can trigger different clinical phenotypes or, vice-versa, that similar phenotypes can be triggered by different neuropathologies. This evidence has called for the adoption of a pathology spectrum-based approach to study neurodegenerative proteinopathies. These conditions share brain deposition of abnormal protein aggregates, leading to aberrant biochemical, metabolic, functional, and structural changes. Positron emission tomography (PET) is a well-recognized and unique tool for the in vivo assessment of brain neuropathology, and novel PET techniques are emerging for the study of specific protein species. Today, key applications of PET range from early research and clinical diagnostic tools to their use in clinical trials for both participants screening and outcome evaluation. This position article critically reviews the role of distinct PET molecular tracers for different neurodegenerative proteinopathies, highlighting their strengths, weaknesses, and opportunities, with special emphasis on methodological challenges and future applications.

Introduction: A rapid transition from a clinical-based classification to a pathology-based classification of neurodegenerative conditions, largely promoted by the increasing availability of imaging biomarkers, is emerging. The Framework for Innovative Multi-tracer molecular Brain Imaging, funded by the EU Joint Program - Neurodegenerative Disease Research 2016 “Working Groups for Harmonisation and Alignment in Brain Imaging Methods for Neurodegeneration,” aimed at providing a roadmap for the applications of established and new molecular imaging techniques in dementia. Methods: We consider current and future implications of adopting a pathology-based framework for the use and development of positron emission tomography techniques. Results: This approach will enhance efforts to understand the multifactorial etiology of Alzheimer's disease and other dementias. Discussion: The availability of pathology biomarkers will soon transform clinical and research practice. Crucially, a comprehensive understanding of strengths and caveats of these techniques will promote an informed use to take full advantage of these tools.

Purpose: An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level. Methods: Selection of HC images was based on visual rating, after Cook’s distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB. Results: Two-step Cook’s distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes. Conclusions: The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.

Objective To evaluate the statistical parametric mapping (SPM) procedure for fluorodeoxyglucose (FDG)-PET imaging as a possible single-subject marker of progression to dementia in Parkinson disease (PD). Methods Fifty-four consecutive patients with PD without dementia (age at onset of 59.9 ± 10.1 years, disease duration of 5.3 ± 3.4 years) entered the study. The patients underwent an extensive motor and cognitive assessment and a single-subject FDG-PET SPM evaluation at baseline. A 4-year follow-up provided disease progression and dementia diagnosis. Results The FDG-PET SPM was evaluated by 2 expert raters allowing the identification of a "typical PD pattern" in 29 patients, whereas 25 patients presented with "atypical patterns," namely, dementia with Lewy bodies (DLB)-like (n = 12), Alzheimer disease (AD)-like (n = 6), corticobasal syndrome (CBS)-like (n = 5), and frontotemporal dementia (FTD)-like (n = 2). At 4-year follow-up, 13 patients, all showing atypical brain metabolic patterns at baseline, progressed to dementia (PD dementia). The DLB-and AD-like SPM patterns were the best predictor for incident dementia (p < 0.005, sensitivity 85%, specificity 88%), independently from demographics or cognitive baseline classification. Conclusions This study suggests that FDG-PET SPM at the single-subject level might help in identifying patients with PD at risk of developing dementia.

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global 18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCT SUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCT and SUVrMRI global uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine a