Resting metabolic connectivity in prodromal Alzheimer's disease. A European Alzheimer Disease Consortium (EADC) project

Clinical and metabolic profiles in behavioural frontotemporal dementia: Impact of age at onset

AIM

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder, with considerable variability of age-at-onset. We explored clinical and metabolic differences between early- and late-onset behavioural FTD (bvFTD), assuming that they might represent different disease phenotypes.

MATERIALS AND METHODS

We retrospectively studied consecutive patients diagnosed with prodromal or overt bvFTD with [18F]FDG PET scan, neuropsychological assessment (NPS), and Neuropsychiatric Inventory (NPI) available at baseline. Patients were divided into three groups based on age-at-onset: early onset-bvFTD (EO-bvFTD, age<70), late onset-bvFTD (LO-bvFTD, age 70-75) and very late onset-bvFTD (vLO-bvFTD, age>75). NPS and NPI were compared between groups and in the subset of prodromal patients, to study different syndromic phenotypes. Voxel-based analysis compared brain [18F]FDG PET of EO-bvFTD, LO-bvFTD and vLO-bvFTD independently, with respect to healthy controls, to explore metabolic differences. An inter-regional metabolic covariance analysis was performed in frontal lobe subregions, to explore differences in brain connectivity. Moreover, we supported our result using a correlation-based approach on clinical and metabolic variables.

RESULTS

101 bvFTD (62 prodromal bvFTD) were enrolled (EO-bvFTD: n = 36, prodromal n = 21; LO-bvFTD: n = 36, prodromal: n = 22; vLO-bvFTD: n = 29, prodromal: n = 19). Greater verbal memory deficit was evident in LO-bvFTD and vLO-bvFTD compared to EO-bvFTD (immediate recall: p = .018; p = .024; delayed recall: both p = .001, respectively), with similar results in the subset of prodromal patients. EO-bvFTD and LO-bvFTD had a higher behavioural severity than vLO-bvFTD. LO-bvFTD and vLO-bvFTD showed more widespread relative hypometabolism, with a greater involvement of posterior, subcortical and temporo-limbic regions compared with EO-bvFTD. Moreover, vLO-bvFTD showed a different pattern of intrafrontal metabolic covariance compared to EO-bvFTD and LO-bvFTD.

DISCUSSION

The cognitive-behavioural profile of bvFTD differs between early- and late-onset, already from the prodromal stage of the disease. Both metabolic pattern and functional connectivity vary based on age-at-onset. Understanding these differences could contribute to improve diagnostic accuracy and understanding the underling pathological heterogeneity.

Metabolic Brain PET Connectivity

This review examines the role of metabolic connectivity based on fluorodeoxyglucose-PET in understanding brain network organization across neurologic disorders, with a focus on neurodegenerative diseases. The article explores key methodologies for metabolic connectivity study and highlights altered connectivity patterns in Alzheimer's, Parkinson's, frontotemporal dementia, and other conditions. It also discusses emerging applications, including single-subject analyses and brain-organ interactions.

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Dopamine D1-Receptor Organization Contributes to Functional Brain Architecture

Recent work has recognized a gradient-like organization in cortical function, spanning from primary sensory to transmodal cortices. It has been suggested that this axis is aligned with regional differences in neurotransmitter expression. Given the abundance of dopamine D1-receptors (D1DR), and its importance for modulation and neural gain, we tested the hypothesis that D1DR organization is aligned with functional architecture, and that inter-regional relationships in D1DR co-expression modulate functional cross talk. Using the world's largest dopamine D1DR-PET and MRI database (N = 180%, 50% female), we demonstrate that D1DR organization follows a unimodal-transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. We also demonstrate that individual differences in D1DR density between unimodal and transmodal regions are associated with functional differentiation of the apices in the cortical hierarchy. Finally, we show that spatial co-expression of D1DR primarily modulates couplings within, but not between, functional networks. Together, our results show that D1DR co-expression provides a biomolecular layer to the functional organization of the brain.

Validity and value of metabolic connectivity in mouse models of β-amyloid and tauopathy

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (β-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-µPET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes ≥12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas β-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different β-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum

BACKGROUND

The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear.

OBJECTIVE

To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points.

METHODS

We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up.

RESULTS

In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12.

CONCLUSION

These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

Altered metabolic connectivity within the limbic cortico-striato-thalamo-cortical circuit in presymptomatic and symptomatic behavioral variant frontotemporal dementia

BACKGROUND

Behavioral variant frontotemporal dementia (bvFTD) is predominantly considered a dysfunction in cortico-cortical transmission, with limited direct investigation of cortical-subcortical transmission. Thus, we aimed to characterize the metabolic connectivity between areas of the limbic cortico-striato-thalamic-cortical (CSTC) circuit in presymptomatic and symptomatic bvFTD patients.

METHODS

Thirty-three bvFTD patients and 33 unrelated healthy controls were recruited for this study. Additionally, six asymptomatic carriers of the MAPT P301L mutation were compared with 12 non-carriers who were all from the same family of bvFTD. Each participant underwent neuropsychological assessment, genetic testing, and a hybrid PET/MRI scan. Seed-based metabolic connectivity based on [¹⁸F]-fluorodeoxyglucose PET between the main components within the limbic CSTC circuit was explored according to the Oxford-GSK-Imanova Striatal Connectivity Atlas.

RESULTS

BvFTD patients exhibited reduced metabolic connectivity between the relays in the limbic CSTC circuit, which included the frontal region (ventromedial prefrontal cortex, orbitofrontal cortex, rectus gyrus, and anterior cingulate cortex), the limbic striatum, and thalamus compared to controls. In the bvFTD patients, the involvement of the limbic CSTC circuit was associated with the severity of behavior disruption, as measured by the frontal behavior inventory, the disinhibition subscale, and the apathy subscale. Notably, asymptomatic MAPT carriers had weakened frontostriatal connectivity but enhanced striatothalamus and thalamofrontal connectivity within the limbic CSTC circuit compared with noncarriers.

CONCLUSION

These findings suggested that aberrant metabolic connectivity within the limbic CSTC circuit is present in symptomatic and even asymptomatic stages of bvFTD. Thus, metabolic connectivity patterns could be used as a potential biomarker to detect the presymptomatic stage and track disease progression.

Lateralization of the crossed cerebellar diaschisis-associated metabolic connectivities in cortico-ponto-cerebellar and cortico-rubral pathways

This study aimed to explore the glucose metabolic profile of extrapyramidal system in patients with crossed cerebellar diaschisis (CCD). Furthermore, the metabolic connectivities in cortico-ponto-cerebellar and cortico-rubral pathways associated with CCD were also investigated. A total of 130 CCD positive (CCD+) and 424 CCD negative (CCD-) patients with unilateral cerebral hemisphere hypometabolism on ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) were enrolled. Besides, the control group consisted of 56 subjects without any brain structural and metabolic abnormalities. Apart from the "autocorrelation", metabolic connectivity pattern of right or left affected cerebellar hemisphere involved unilateral (left or right, respectively) caudate, pallidum, putamen, thalamus and red nucleus, in CCD+ patients with left or right supratentorial lesions, respectively (P_uncorrected < 0.001, cluster size > 200). CCD+ group had significantly lower asymmetry index (AI) in cortico-ponto-cerebellar pathway (including ipsilateral cerebral white matter, ipsilateral pons, contralateral cerebellum white matter and contralateral cerebellum exterior cortex) and cortico-rubral pathway (including ipsilateral caudate, thalamus proper, pallidum, putamen, ventral diencephalon and red nucleus) than those of both CCD- and control groups (all P < 0.05). AI in contralateral cerebellum exterior cortex was significantly positively correlated with that in ipsilateral caudate, putamen, pallidum, thalamus proper, ventral diencephalon, red nucleus and pons among CCD+ group (all P < 0.01), but only with that in ipsilateral caudate and putamen among CCD- group (both P < 0.001). These results provide additional insight into the involvement of both cortico-ponto-cerebellar and cortico-rubral pathways in the presence of CCD, underlining the need for further investigation about the role of their aberrant metabolic connectivities in the associated symptoms of CCD.

Tau Pathology Profile Across a Parietal-Hippocampal Brain Network Is Associated With Spatial Reorientation Learning and Memory Performance in the 3xTg-AD Mouse

In early Alzheimer's disease (AD) spatial navigation is one of the first impairments to emerge; however, the precise cause of this impairment is unclear. Previously, we showed that, in a mouse model of tau and amyloid beta (Aβ) aggregation, getting lost represents, at least in part, a failure to use distal cues to get oriented in space and that impaired parietal-hippocampal network level plasticity during sleep may underlie this spatial disorientation. However, the relationship between tau and amyloid beta aggregation in this brain network and impaired spatial orientation has not been assessed. Therefore, we used several approaches, including canonical correlation analysis and independent components analysis tools, to examine the relationship between pathology profile across the parietal-hippocampal brain network and spatial reorientation learning and memory performance. We found that consistent with the exclusive impairment in 3xTg-AD 6-month female mice, only 6-month female mice had an ICA identified pattern of tau pathology across the parietal-hippocampal network that were positively correlated with behavior. Specifically, a higher density of pTau positive cells predicted worse spatial learning and memory. Surprisingly, despite a lack of impairment relative to controls, 3-month female, as well as 6- and 12- month male mice all had patterns of tau pathology across the parietal-hippocampal brain network that are predictive of spatial learning and memory performance. However, the direction of the effect was opposite, a negative correlation, meaning that a higher density of pTau positive cells predicted better performance. Finally, there were not significant group or region differences in M78 density at any of the ages examined and ICA analyses were not able to identify any patterns of 6E10 staining across brain regions that were significant predictors of behavioral performance. Thus, the pattern of pTau staining across the parietal-hippocampal network is a strong predictor of spatial learning and memory performance, even for mice with low levels of tau accumulation and intact spatial re-orientation learning and memory. This suggests that AD may cause spatial disorientation as a result of early tau accumulation in the parietal-hippocampal network.

Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer's Disease

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer's disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

In vivo MRI Structural and PET Metabolic Connectivity Study of Dopamine Pathways in Alzheimer's Disease

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.

Individual Brain Metabolic Signatures in Corticobasal Syndrome

BACKGROUND

Corticobasal syndrome (CBS) is the usual clinical presentation of patients with corticobasal degeneration pathology. Nevertheless, there are CBS individuals with postmortem neuropathology typical of Alzheimer's disease (AD).

OBJECTIVE

In this study, we aim to detect FDG-PET metabolic signatures at the single-subject level in a CBS sample, also evaluated with cerebrospinal fluid (CSF) markers for AD pathology.

METHODS

21 patients (68.9±6.4 years; MMSE score = 21.7±6.3) fulfilling current criteria for CBS were enrolled. All underwent a clinical-neuropsychological assessment and an instrumental evaluation for biomarkers of neurodegeneration, amyloid and tau pathology (i.e., FDG-PET imaging and CSF Aβ42 and tau levels) at close intervals. CBS subjects were classified according to the presence or absence of CSF markers of AD pathology (i.e., low Aβ42 and high phosphorylated tau levels). Optimized voxel-based SPM procedures provided FDG-PET metabolic patterns at the single-subject and group levels.

RESULTS

Eight CBS had an AD-like CSF profile (CBS-AD), while thirteen were negative (CBS-noAD). The two subgroups did not differ in demographic characteristics or global cognitive impairment. FDG-PET SPM t-maps identified different metabolic signatures. Namely, all CBS-AD patients showed the typical AD-like hypometabolic pattern involving posterior cingulate cortex, precuneus and temporo-parietal cortex, whereas CBS-noAD cases showed bilateral hypometabolism in fronto-insular cortex and basal ganglia that is typical of the frontotemporal lobar degeneration spectrum.

DISCUSSION

These results strongly suggest the inclusion of FDG-PET imaging in the diagnostic algorithm of individuals with CBS clinical phenotype in order to early identify functional metabolic signatures due to different neuropathological substrates, thus improving the diagnostic accuracy.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

18F-FDG brain PET hypometabolism in patients with long COVID

PURPOSE

In the context of the worldwide outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), some patients report functional complaints after apparent recovery from COVID-19. This clinical presentation has been referred as "long COVID." We here present a retrospective analysis of ¹⁸F-FDG brain PET of long COVID patients from the same center with a biologically confirmed diagnosis of SARS-CoV-2 infection and persistent functional complaints at least 3 weeks after the initial infection.

METHODS

PET scans of 35 patients with long COVID were compared using whole-brain voxel-based analysis to a local database of 44 healthy subjects controlled for age and sex to characterize cerebral hypometabolism. The individual relevance of this metabolic profile was evaluated to classify patients and healthy subjects. Finally, the PET abnormalities were exploratory compared with the patients' characteristics and functional complaints.

RESULTS

In comparison to healthy subjects, patients with long COVID exhibited bilateral hypometabolism in the bilateral rectal/orbital gyrus, including the olfactory gyrus; the right temporal lobe, including the amygdala and the hippocampus, extending to the right thalamus; the bilateral pons/medulla brainstem; the bilateral cerebellum (p-voxel < 0.001 uncorrected, p-cluster < 0.05 FWE-corrected). These metabolic clusters were highly discriminant to distinguish patients and healthy subjects (100% correct classification). These clusters of hypometabolism were significantly associated with more numerous functional complaints (brainstem and cerebellar clusters), and all associated with the occurrence of certain symptoms (hyposmia/anosmia, memory/cognitive impairment, pain and insomnia) (p < 0.05). In a more preliminary analysis, the metabolism of the frontal cluster which included the olfactory gyrus was worse in the 7 patients treated by ACE drugs for high blood pressure (p = 0.032), and better in the 3 patients that had used nasal decongestant spray at the infectious stage (p < 0.001).

CONCLUSION

This study demonstrates a profile of brain PET hypometabolism in long COVID patients with biologically confirmed SARS-CoV-2 and persistent functional complaints more than 3 weeks after the initial infection symptoms, involving the olfactory gyrus and connected limbic/paralimbic regions, extended to the brainstem and the cerebellum. These hypometabolisms are associated with patients' symptoms, with a biomarker value to identify and potentially follow these patients. The hypometabolism of the frontal cluster, which included the olfactory gyrus, seems to be linked to ACE drugs in patients with high blood pressure, with also a better metabolism of this olfactory region in patients using nasal decongestant spray, suggesting a possible role of ACE receptors as an olfactory gateway for this neurotropism.

Changes in brain glucose metabolism and connectivity in somatoform disorders: an 18F-FDG PET study

Somatoform disorders (SFD) are defined as a syndrome characterized by somatic symptoms which cannot be explained by organic reasons. Chronic or recurrent forms of somatization lead to heavy emotional and financial burden to the patients and their families. However, the underlying etiology of SFD is largely unknown. The purpose of this study is to investigate the changed brain glucose metabolic pattern in SFD. In this study, 18 SFD patients and 21 matched healthy controls were enrolled and underwent an ¹⁸F-FDG PET scan. First, we explored the altered brain glucose metabolism in SFD. Then, we calculated the mean ¹⁸F-FDG uptake values for 90 AAL regions, and detected the changed brain metabolic connectivity between the most significantly changed regions and all other regions. In addition, the Pearson coefficients between the neuropsychological scores and regional brain ¹⁸F-FDG uptake values were computed for SFD patients. We found that SFD patients showed extensive hypometabolism in bilateral superolateral prefrontal cortex, insula, and regions in bilateral temporal gyrus, right angular gyrus, left gyrus rectus, right fusiform gyrus, right rolandic operculum and bilateral occipital gyrus. The metabolic connectivity between right insula and prefrontal areas, as well as within prefrontal areas was enhanced in SFD. And several brain regions were associated with the somatic symptoms, including insula, putamen, middle temporal gyrus, superior parietal gyrus and orbital part of inferior frontal gyrus. Our study revealed widespread alterations of the brain glucose metabolic pattern in SFD patients. Those findings might elucidate the neuronal mechanisms with glucose metabolism and shed light on the pathology of SFD.

The role of anterior prefrontal cortex in prospective memory: an exploratory FDG-PET study in early Alzheimer's disease

From previous studies in healthy volunteers the prefrontal regions are deeply involved in prospective memory (PM), although little is known about the functional neural basis of PM in prodromal Alzheimer's disease (AD). To this end, we retrospectively recruited 18 patients with mild cognitive impairment caused by AD and 23 matched healthy control subjects who had undergone ¹⁸F-fluorodeoxyglucose positron emission tomography and the PM-specific paradigm test. Brain metabolism was correlated with the PM score in the 2 groups separately to find those brain areas correlated with PM performance, which were then used as a hub for an inter-regional metabolic connectivity analyses (inter-regional correlation analysis). Of note, in mild cognitive impairment caused by AD, but not in healthy control subjects, PM score positively correlated with metabolic levels in the right anterior prefrontal cortex (middle and inferior frontal gyri), which disclosed a loss of interhemispheric connectivity in the inter-regional correlation analysis. According to our findings, the functioning of the right anterior prefrontal cortex and its interhemispheric metabolic connectivity is crucial in early AD to sustain PM performance, which deteriorates along with progressive metabolic failure of the interconnected areas.

Alterations in the metabolic networks of temporal lobe epilepsy patients: A graph theoretical analysis using FDG-PET

OBJECTIVE

The aim of this study is to investigate changes in metabolic networks based on fluorodeoxyglucose positron emission tomography (FDG-PET) in patients with drug-resistant temporal lobe epilepsy (TLE) (with and without hippocampal sclerosis [HS]) when compared with healthy controls.

METHODS

We retrospectively enrolled 30 patients with drug-resistant temporal lobe epilepsy (17 patients with HS and 13 patients without HS) and 39 healthy controls. All subjects underwent interictal FDG-PET scans, which were analyzed to obtain metabolic connectivity using graph theoretical analysis. We investigated the differences in metabolic connectivity between patients with drug-resistant TLE (with and without HS) and healthy controls.

RESULTS

When compared with healthy controls, TLE patients with HS showed alterations of global and local metabolic connectivity. When considering global connectivity, TLE patients with HS had a decreased average degree with increased modularity. When considering local connectivity, TLE patients with HS displayed alterations of betweeness centrality in widespread regions. However, there were no alterations of global metabolic connectivity in TLE patients without HS when compared with healthy controls. In addition, when compared to TLE patients without HS, TLE patients with HS had increased modularity.

SIGNIFICANCE

Our study demonstrates more severe alterations in metabolic networks based on FDG-PET in TLE patients with HS than in those without HS and healthy controls. This may represent distinct epileptic networks in TLE patients with HS versus those without HS, although both are drug-resistant focal epilepsy.

Impaired Hippocampal-Cortical Interactions during Sleep in a Mouse Model of Alzheimer's Disease

Spatial learning is impaired in humans with preclinical Alzheimer's disease (AD). We reported similar impairments in 3xTg-AD mice learning a spatial reorientation task. Memory reactivation during sleep is critical for learning-related plasticity, and memory consolidation is correlated with hippocampal sharp wave ripple (SWR) density, cortical delta waves (DWs), cortical spindles, and the temporal coupling of these events-postulated as physiological substrates for memory consolidation. Further, hippocampal-cortical discoordination is prevalent in individuals with AD. Thus, we hypothesized that impaired memory consolidation mechanisms in hippocampal-cortical networks could account for spatial memory deficits. We assessed sleep architecture, SWR-DW dynamics, and memory reactivation in a mouse model of tauopathy and amyloidosis implanted with a recording array targeting isocortex and hippocampus. Mice underwent daily recording sessions of rest-task-rest while learning the spatial reorientation task. We assessed memory reactivation by matching activity patterns from the approach to the unmarked reward zone to patterns during slow-wave sleep (SWS). AD mice had more SWS, but reduced SWR density. The increased SWS compensated for reduced SWR density so there was no reduction in SWR number. In control mice, spindles were phase-coupled with DWs, and hippocampal SWR-cortical DW coupling was strengthened in post-task sleep and was correlated with performance on the spatial reorientation task the following day. However, in AD mice, SWR-DW and spindle-DW coupling were impaired. Thus, reduced SWR-DW coupling may cause impaired learning in AD, and spindle-DW coupling during short rest-task-rest sessions may serve as a biomarker for early AD-related changes in these brain dynamics.

Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer's Disease

BACKGROUND

Several automatic tools have been implemented for semi-quantitative assessment of brain [18]F-FDG-PET.

OBJECTIVE

We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls.

METHODS

Sixty-two pAD patients (MMSE score = 27.0±1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2±1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [18]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM).

RESULTS

The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods.

CONCLUSION

The study confirms the good accuracy of [18]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.

Electroacupuncture-Related Metabolic Brain Connectivity in Neuropathic Pain due to Brachial Plexus Avulsion Injury in Rats

Objective: The present study aimed to investigate the analgesic effect of electroacupuncture (EA) in neuropathic pain due to brachial plexus avulsion injury (BPAI) and related changes in the metabolic brain connectivity. Methods: Neuropathic pain model due to BPAI was established in adult female Sprague-Dawley rats. EA stimulations (2/15 Hz, 30 min/day, 5-day intervention followed by 2-day rest in each session) were applied to the fifth-seventh cervical "Jiaji" acupoints on the noninjured side from 1st to 12th weeks following BPAI (EA group, n = 8). Three control groups included sham EA (nonelectrical acupuncture applied to 3 mm lateral to the real "Jiaji" acupoints), BPAI-only, and normal rats (no particular intervention; eight rats in each group). Thermal withdrawal latency (TWL) of the noninjured forepaw was regularly tested to evaluate the threshold of thermalgesia. Small animal [fluorine-18]-fluoro-2-deoxy-_D-glucose (¹⁸F-FDG) PET/CT scans of brain were conducted at the end of 4th, 12th, and 16th weeks to explore metabolic alterations of brain. Results: In the EA group, the TWL of the noninjured forepaw significantly decreased following BPAI and then increased following EA stimulation, compared with sham EA (P < 0.001). The metabolic brain connectivity among somatosensory cortex (SC), motor cortex (MC), caudate putamen (Cpu), and dorsolateral thalamus (DLT) in bilateral hemispheres decreased throughout the 16 weeks' observation in the BPAI-only group, compared with the normal rats (P < 0.05). In the EA group, the strength of connectivity among the above regions were found to be increased at the end of 4th week following BPAI modeling, decreased at 12th week, and then increased again at 16th week (P < 0.05). The changes in metabolic connectivity were uncharacteristic and dispersed in the sham EA group. Conclusion: The study revealed long-term and extensive changes of metabolic brain connectivity in EA-treated BPAI-induced neuropathic pain rats. Bilateral sensorimotor and pain-related brain regions were mainly involved in this process. It indicated that modulation of brain metabolic connectivity might be an important mechanism of analgesic effect in EA stimulation for the treatment of neuropathic pain.

The combined effects of microglia activation and brain glucose hypometabolism in early-onset Alzheimer's disease

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 and occipital hypometabolism in the EOAD variants. There was a spatial concordance in the interaction areas and significant correlations between the two biological changes. The network analysis showed a disruption of frontal connectivity induced by the metabolic/microglia effects.

Conclusion

The severe microglia activation characterizing EOAD and contributing to neurodegeneration may be a marker of rapid disease progression. The coupling between brain glucose hypometabolism and local immune response in AD-signature regions supports their biological interaction.

Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease

Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on Pearson correlation or linear regression models. However, these models are prone to spurious correlations arising from uninformative shared variance and multicollinearity. Notably, there are no appropriate multivariate statistical models available that can easily integrate dozens of multicollinear variables derived from such data, being able to utilize the additional information provided from the combination of data sources. Gaussian graphical models (GGMs) can estimate the conditional dependency from given data, which is conceptually expected to closely reflect the underlying causal relationships between various variables. Hence, we applied GGMs to assess multimodal regional brain alterations in AD. We obtained data from N = 972 subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for each of the 108 cortical and subcortical brain regions. GGMs were estimated using a Bayesian framework for the combined multimodal data and the resulted conditional dependency networks were compared to classical covariance networks based on Pearson correlation. Additionally, graph-theoretical network statistics were calculated to determine network alterations associated with disease status. The resulting conditional dependency matrices were much sparser (≈10% density) than Pearson correlation matrices (≈50% density). Within imaging modalities, conditional dependency networks yielded clusters connecting anatomically adjacent regions. For the associations between different modalities, only few region-specific connections were detected. Network measures such as small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory, i.e., increased small-world coefficient in early mild cognitive impairment (MCI), similar values in late MCI, and decreased values in AD dementia patients compared to cognitively normal controls. In conclusion, GGMs removed commonly shared variance among multimodal measures of regional brain alterations in MCI and AD, and yielded sparser matrices compared to correlation networks based on the Pearson coefficient. Therefore, GGMs may be used as alternative to thresholding-approaches typically applied to correlation networks to obtain the most informative relations between variables.

The clinico-metabolic correlates of language impairment in corticobasal syndrome and progressive supranuclear palsy

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CBS and PSP patients show heterogeneous language profiles.

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Patients with nfvPPA profile show the typical nfvPPA hypometabolic pattern.

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Parietal hypometabolism characterizes CBS cases with undefined language deficits.

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Frontal hypometabolism characterizes PSP cases with undefined language deficits.

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Patients without language deficit show a predominant right hemisphere involvement.

Purpose

To assess the clinical-metabolic correlates of language impairment in a large sample of patients clinically diagnosed as corticobasal syndrome (CBS) and progressive supranuclear palsy syndrome (PSPs).

Methods

We included 70 patients fulfilling current criteria for CBS (n = 33) or PSPs (n = 37). All subjects underwent clinical-neuropsychological and FDG-PET assessments at the time of diagnosis. The whole patient's cohort was grouped into three subgroups according to the language characteristics, i.e., (a) nfv-PPA; (b) subtle language impairments, LANG-; (c) no language deficits, NOL-. FDG-PET data were analysed using an optimized voxel-based SPM method at the single-subject and group levels in order to evaluate specific hypometabolic patterns and regional dysfunctional FDG-PET commonalities in subgroups.

Results

21 patients had a nfvPPA diagnosis (i.e., nfv-PPA/CBS = 12 and nfv-PPA/PSPs = 9), while 20 patients had a subtle language impairment LANG- (i.e., CBS = 12 and PSPs = 8), not fulfilling the criteria for a nfv-PPA diagnosis. The remaining sample (i.e., 9/33 CBS and 20/37 PSPs patients) did not show any language deficit. FDG-PET results in individuals with a nfv-PPA diagnosis were consistent with the typical nfv-PPA pattern of hypometabolism (i.e., left fronto-insular and superior medial frontal cortex involvement), both in CBS and PSPs. The LANG-CBS and LANG-PSPs subjects had different FDG-PET hypometabolic patterns involving, respectively, parietal and frontal regions. As expected, NOL-CBS and NOL-PSPs showed a predominant right hemisphere involvement, with selective functional metabolic signatures typical of the two syndromes.

Conclusions

Language impairments, fulfilling the nfv-PPA criteria, are associated with both CBS and PSPs clinical presentations early in the disease course. Subtle language deficits may be present in an additional proportion of patients not fulfilling the nfv-PPA criteria. The topography of brain hypometabolism is a major dysfunctional signature of language deficits in CBS and PSPs clinical phenotypes.

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study

BACKGROUND

Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia.

METHODS

In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models-a demographics model, a hippocampal volume model, and a CSF biomarkers model-by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework.

FINDINGS

We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59-0·65), validated hippocampal volume model (0·67, 0·62-0·72), and updated CSF biomarkers model (0·72, 0·68-0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71-0·76).

INTERPRETATION

We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour.

FUNDING

ZonMW-Memorabel.

Variant-specific vulnerability in metabolic connectivity and resting-state networks in behavioural variant of frontotemporal dementia

Brain connectivity measures represent candidate biomarkers of neuronal dysfunction in neurodegenerative diseases. Previous findings suggest that the behavioural variant of frontotemporal dementia (bvFTD) and its variants (i.e., frontal and temporo-limbic) may be related to the vulnerability of distinct functional connectivity networks. In this study, 82 bvFTD patients were included, and two patient groups were identified as frontal and temporo-limbic bvFTD variants. Two advanced multivariate analytical approaches were applied to FDG-PET data, i.e., sparse inverse covariance estimation (SICE) method and seed-based interregional correlation analysis (IRCA). These advanced methods allowed the assessment of (i) the whole-brain metabolic connectivity, without any a priori assumption, and (ii) the main brain resting-state networks of crucial relevance for cognitive and behavioural functions. In the whole bvFTD group, we found dysfunctional connectivity patterns in frontal and limbic regions and in all major brain resting-state networks as compared to healthy controls (HC N = 82). In the two bvFTD variants, SICE and IRCA analyses identified variant-specific reconfigurations of whole-brain connectivity and resting-state networks. Specifically, the frontal bvFTD variant was characterised by metabolic connectivity alterations in orbitofrontal regions and anterior resting-state networks, while the temporo-limbic bvFTD variant was characterised by connectivity alterations in the limbic and salience networks. These results highlight different neural vulnerabilities in the two bvFTD variants, as shown by the dysfunctional connectivity patterns, with relevance for the different neuropsychological profiles. This new evidence provides further insight in the variability of bvFTD and may contribute to a more accurate classification of these patients.

Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography

Positron emission tomography (PET) represents a unique molecular tool to get in vivo access to a wide spectrum of biological and neuropathological processes, of crucial relevance for neurodegenerative conditions. Although most PET findings are based on massive univariate approaches, in the last decade the increasing interest in multivariate methods has paved the way to the assessment of unexplored cerebral features, spanning from resting state brain networks to whole-brain connectome properties. Currently, the combination of molecular neuroimaging techniques with multivariate connectivity methods represents one of the most powerful, yet still emerging, approach to achieve novel insights into the pathophysiology of neurodegenerative diseases. In this review, we will summarize the available evidence in the field of PET molecular connectivity, with the aim to provide an overview of how these studies may increase the understanding of the pathogenesis of neurodegenerative diseases, over and above "traditional" structural/functional connectivity studies. Considering the available evidence, a major focus will be represented by molecular connectivity studies using [18F]FDG-PET, today applied in the major neuropathological spectra, from amyloidopathies and tauopathies to synucleinopathies and beyond. Pioneering studies using PET tracers targeting brain neuropathology and neurotransmission systems for connectivity studies will be discussed, their strengths and limitations highlighted with reference to both applied methodology and results interpretation. The most common methods for molecular connectivity assessment will be reviewed, with particular emphasis on the available strategies to investigate molecular connectivity at the single-subject level, of potential relevance for not only research but also diagnostic purposes. Finally, we will highlight possible future perspectives in the field, with reference in particular to newly available PET tracers, which will expand the application of molecular connectivity to new, exciting, unforeseen possibilities.

Making Sense of Connectivity

In addition to the assessment of local alterations of specific brain regions, the investigation of entire networks with in vivo neuroimaging techniques has gained increasing attention. In general, connectivity analysis refers to the investigation of links between brain regions, with the aim to characterize their interactions and information transfer. These may represent or relate to different physiological characteristics (structural, functional, or metabolic information) and can be calculated across different levels of granularity (2 regions vs whole brain). In this article, we provide an overview of different connectivity analysis approaches with interpretations and limitations as well as examples in pharmacological imaging and clinical applications. Structural connectivity obtained from diffusion MRI enables the reconstruction of neuronal fiber tracts. These physical links represent major constraints of functional connections, which are in turn defined as correlations between signal time courses. In addition, molecular connectivity approaches based on PET imaging enable the assessment of interregional associations of metabolic demands and neurotransmitter systems. Application of these approaches in clinical investigations has demonstrated novel alterations in various neurological and psychiatric disorders on a network level. Future work should aim for the combined assessment of multiple imaging modalities and to establish robust biomarkers for clinical use. These advancements will further improve the biological interpretation of connectivity metrics and networks of the human brain.

Covariance statistics and network analysis of brain PET imaging studies

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer's disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer's Disease: Graph Theory Applied to Resting State EEG

Graph theory analysis on resting state electroencephalographic rhythms disclosed topological properties of cerebral network. In Alzheimer's disease (AD) patients, this approach showed mixed results. Granger causality matrices were used as input to the graph theory allowing to estimate the strength and the direction of information transfer between electrode pairs. The number of edges (degree), the number of inward edges (in-degree), of outgoing edges (out-degree) were statistically compared among healthy controls, patients with mild cognitive impairment due to AD (AD-MCI) and AD patients with mild dementia (ADD) to evaluate if degree abnormality could involve low and/or high degree vertices, the so called hubs, in both prodromal and over dementia stage. Clustering coefficient and local efficiency were evaluated as measures of network segregation, path length and global efficiency as measures of integration, the assortativity coefficient as a measure of resilience. Degree, in-degree and out-degree values were lower in AD-MCI and ADD than the control group for non-hubs and hubs vertices. The number of edges was preserved for frontal electrodes, where patients' groups showed an additional hub in F3. Clustering coefficient was lower in ADD compared with AD-MCI in the right occipital electrode, and it was positively correlated with mini mental state examination. Local and global efficiency values were lower in patients' than control groups. Our results show that the topology of the network is altered in AD patients also in its prodromal stage, begins with the reduction of the number of edges and the loss of the local and global efficiency.

Feeling of presence in dementia with Lewy bodies is related to reduced left frontoparietal metabolism

Feeling of presence (FOP) refers to the vivid sensation of a person’s presence near oneself and is common in Dementia with Lewy Bodies (DLB). Based on previous observations on epileptic subjects, we hypothesized that DLB subjects with FOP would harbour ¹⁸F-fluorodeoxyglucose PET hypometabolism in left parietal areas. 25 subjects (mean age 71.9 ± 6.7, disease duration at scan 1.7 ± 1.5 years) were included in the study, of whom nine (36%) experienced FOP. No significant between-group difference was observed regarding dopamine transporters striatal uptake (p = 0.64), daily dopaminergic treatment dosage (p = 0.88) and visual hallucinations (p = 0.83). Statistical parametric mapping showed that subjects with FOP had a significantly reduced glucose metabolism in several left frontoparietal areas (p < 0.001), including superior parietal lobule and precuneus. Interregional correlation analysis of these areas showed specific connectivity with right insula and putamen in the FOP subgroup and right orbitofrontal and superior frontal in subjects without FOP. This provides further evidence about the role of a left frontoparietal network and suggest a possible contribution of impaired orbitofrontal reality filtering associated with FOP.

Interregional metabolic connectivity of 2-deoxy-2[18 F]fluoro-D-glucose positron emission tomography in vagus nerve stimulation for pediatric patients with epilepsy: A retrospective cross-sectional study

OBJECTIVE

With the recognition of epilepsy as a network disease that disrupts the organizing ability of resting-state brain networks, vagus nerve stimulation (VNS) may control epileptic seizures through modulation of functional connectivity. We evaluated preoperative 2-deoxy-2[¹⁸ F]fluoro-D-glucose (FDG) positron emission tomography (PET) in VNS-implanted pediatric patients with refractory epilepsy to analyze the metabolic connectivity of patients and its prognostic role in seizure control.

METHODS

Preoperative PET data of 66 VNS pediatric patients who were followed up for a minimum of 1 year after the procedure were collected for the study. Retrospective review of the patients' charts was performed, and five patients with inappropriate PET data or major health issues were excluded. We conducted an independent component analysis of FDG-PET to extract spatial metabolic components and their activities, which were used to perform cross-sectional metabolic network analysis. We divided the patients into VNS-effective and VNS-ineffective groups (VNS-effective group, ≥50% seizure reduction; VNS-ineffective group, <50% reduction) and compared metabolic connectivity differences between groups using a permutation test.

RESULTS

Thirty-four (55.7%) patients showed >50% seizure reduction from baseline frequency 1 year after VNS. A significant difference in metabolic connectivity evaluated by preoperative FDG-PET was noted between groups. Relative changes in glucose metabolism were strongly connected among the areas of brainstem, cingulate gyrus, cerebellum, bilateral insula, and putamen in patients with <50% seizure control after VNS.

SIGNIFICANCE

This study shows that seizure outcome of VNS may be influenced by metabolic connectivity, which can be obtained from preoperative PET imaging. This study of metabolic connectivity analysis may contribute in further understanding of the mechanism of VNS in intractable seizures.

Metabolic regional and network changes in Alzheimer's disease subtypes

Clinical variants of Alzheimer's disease (AD) include the common amnestic subtype as well as subtypes characterised by leading visual processing impairments or by multimodal neurocognitive deficits. We investigated regional metabolic patterns and networks between AD subtypes. The study comprised 9 age-matched controls and 25 patients with mild to moderate AD. Methods included clinical and neuropsychological assessment, high-resolution FDG PET and T1-weighted 3D MR imaging with PET-MR coregistration, grey matter segmentation, atlas-based regions-of-interest, linear mixed effects and regional correlation analysis. Regional metabolic patterns differed significantly between groups, but significant hypometabolism in the posterior cingulate cortex (PCC) was common to all subtypes. The most distinctive regional abnormality was occipital hypometabolism in the visual subtype. In controls, two large clusters of positive regional metabolic correlations were observed. The most pronounced breakdown of the normal correlation pattern was found in amnestic patients who, in contrast, showed the least regional focal metabolic deficits. The normal positive correlation between PCC and hippocampus was lost in all subtypes. In conclusion, PCC hypometabolism and metabolic correlation breakdown between PCC and hippocampus are the common functional core of all AD subtypes. Network alterations exceed focal regional impairment and are most prominent in the amnestic subtype.

Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques

Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p < 0.00001); however, community structure is similar at network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

The Role of Single-Subject Brain Metabolic Patterns in the Early Differential Diagnosis of Primary Progressive Aphasias and in Prediction of Progression to Dementia

Background and Objective: Primary progressive aphasia (PPA) is a clinical syndrome due to different neurodegenerative conditions in which an accurate early diagnosis needs to be supported by a reliable diagnostic tool at the individual level. In this study, we investigated in PPA the FDG-PET brain metabolic patterns at the single-subject level, in order to assess the case-to-case variability and its relationship with clinical-neuropsychological findings.

Material and Methods: 55 patients (i.e., 11 semantic variant/sv-PPA, 19 non fluent variant/nfv-PPA, 17 logopenic variant/lv-PPA, 3 slowly progressive anarthria/SPA, and 5 mixed PPA/m-PPA) were included. Clinical-neuropsychological information and FDG-PET data were acquired at baseline. A follow-up of 27.4±12.55 months evaluated the clinical progression. Brain metabolism was analyzed using an optimized and validated voxel-based SPM method at the single-subject level.

Results: FDG-PET voxel-wise metabolic assessment revealed specific metabolic signatures characterizing each PPA variant at the individual level, reflecting the underlying neurodegeneration in language networks. Notably, additional dysfunctional patterns predicted clinical progression to specific dementia conditions. In the case of nfv-PPA, a metabolic pattern characterized by involvement of parietal, subcortical and brainstem structures predicted progression to a corticobasal degeneration syndrome or to progressive supranuclear palsy. lv-PPA and sv-PPA cases who progressed to Alzheimer’s disease and frontotemporal dementia at the follow-up presented with extended bilateral patterns at baseline.

Discussion: Our results indicate that FDG-PET voxel-wise imaging is a valid biomarker for the early differential diagnosis of PPAs and for the prediction of progression to specific dementia condition. This study supports the use of FDG-PET imaging quantitative assessment in clinical settings for a better characterization of PPA individuals and prognostic definition of possible endo-phenotypes.

Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study

This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.

A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60–70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample t-test. Fifth, the support vector machines (SVM) and their probabilistic variant (pSVM) were constructed to provide local, followed by global diagnosis. The system was evaluated on scans of normal control (NC) vs. mild cognitive impairment (MCI) (19 NC and 65 MCI scans). The proposed system showed superior accuracy, specificity, and sensitivity as compared to other related work.

The emerging role of PET imaging in dementia

A compelling need in the field of neurodegenerative diseases is the development and validation of biomarkers for early identification and differential diagnosis. The availability of positron emission tomography (PET) neuroimaging tools for the assessment of molecular biology and neuropathology has opened new venues in the diagnostic design and the conduction of new clinical trials. PET techniques, allowing the in vivo assessment of brain function and pathology changes, are increasingly showing great potential in supporting clinical diagnosis also in the early and even preclinical phases of dementia. This review will summarize the most recent evidence on fluorine-18 fluorodeoxyglucose-, amyloid -, tau -, and neuroinflammation - PET tools, highlighting strengths and limitations and possible new perspectives in research and clinical applications. Appropriate use of PET tools is crucial for a prompt diagnosis and target evaluation of new developed drugs aimed at slowing or preventing dementia.

Multielement concentration analysis of Swiss mice brains on experimental model of Alzheimer's disease induced by β‐amyloid oligomers

Alzheimer's disease (AD) is an insidious, progressive, and irreversible brain disorder that leads to memory loss and severe cognitive decline. However, the discovery that soluble β‐amyloid oligomers are potent central nervous system neurotoxins has led to a new view of AD pathogenesis. Metals, such as zinc, iron, and copper, are all increased in the aged brain, and the contents of those metals in the brain of AD patients are higher than non‐AD people. So, altered homeostasis of metal may result in the development of AD. Total reflection X‐ray fluorescence analysis (TXRF) is a multielement analytical technique, which can be used for elemental trace analysis. In this work, TXRF was used to evaluate the elemental concentration and distribution on mice brain regions. Three groups were studied: control, AD10, and AD100, being the two latter were given a single intracerebroventricular injection of 10 pmol and 100 pmol (β‐amyloid oligomers), respectively, to induce experimental AD. All samples were submitted to acid digestion. The TXRF measurements were performed at the X‐Ray Fluorescence Beamline at Brazilian National Synchrotron Light Laboratory, using a monochromatic beam (11.5 keV) for the excitation. It was possible to determine the concentrations of the following elements: P, S, K, Fe, Cu, and Zn. Results showed differences in the elemental concentration in some brain regions between the AD groups and the control group. Furthermore, the difference in the hypothalamus in AD10 groups, both female and male, suggests an association between AD and changes in these elements. Copyright © 2017 John Wiley & Sons, Ltd.

18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer's disease (AD) patients at the mild cognitive impairment (MCI) stage

PURPOSE

We aimed to identify the cortical regions where hypometabolism can predict the speed of conversion to dementia in mild cognitive impairment due to Alzheimer's disease (MCI-AD).

METHODS

We selected from the clinical database of our tertiary center memory clinic, eighty-two consecutive MCI-AD that underwent 18F-fluorodeoxyglucose (FDG) PET at baseline during the first diagnostic work-up and were followed up at least until their clinical conversion to AD dementia. The whole group of MCI-AD was compared in SPM8 with a group of age-matched healthy controls (CTR) to verify the presence of AD diagnostic-pattern; then the correlation between conversion time and brain metabolism was assessed to identify the prognostic-pattern. Significance threshold was set at p < 0.05 False-Discovery-Rate (FDR) corrected at peak and at cluster level. Each MCI-AD was then compared with CTR by means of a SPM single-subject analysis and grouped according to presence of AD diagnostic-pattern and prognostic-pattern. Kaplan-Meier-analysis was used to evaluate if diagnostic- and/or prognostic-patterns can predict speed of conversion to dementia.

RESULTS

Diagnostic-pattern corresponded to typical posterior hypometabolism (BA 7, 18, 19, 30, 31 and 40) and did not correlate with time to conversion, which was instead correlated with metabolic levels in right middle and inferior temporal gyri as well as in the fusiform gyrus (prognostic-pattern, BA 20, 21 and 38). At Kaplan-Meier analysis, patients with hypometabolism in the prognostic pattern converted to AD-dementia significantly earlier than patients not showing significant hypometabolism in the right middle and inferior temporal cortex (9 versus 19 months; Log rank p < 0.02, Breslow test: p < 0.003, Tarone-Ware test: p < 0.007).

CONCLUSION

The present findings support the role of FDG PET as a robust progression biomarker even in a naturalist population of MCI-AD. However, not the AD-typical diagnostic-pattern in posterior regions but the middle and inferior temporal metabolism captures speed of conversion to dementia in MCI-AD since baseline. The highlighted prognostic pattern is a further, independent source of heterogeneity in MCI-AD and affects a primary-endpoint on interventional clinical trials (time of conversion to dementia).

The frequency and influence of dementia risk factors in prodromal Alzheimer's disease

We investigated whether dementia risk factors were associated with prodromal Alzheimer's disease (AD) according to the International Working Group-2 and National Institute of Aging-Alzheimer's Association criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox-regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hypercholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with mild cognitive impairment. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e., preclinical) of AD.

Metabolic connectomics targeting brain pathology in dementia with Lewy bodies

Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak's early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment.

Metabolic connectivity for differential diagnosis of dementing disorders

Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than on connectivity measurements. Here, we test metabolic connectivity for differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients with mild Alzheimer's disease, 52 patients with mild frontotemporal lobar degeneration, and 45 healthy elderly subjects. Sparse inverse covariance estimation and selection were used to identify patterns of metabolic, inter-subject covariance on the basis of 60 regional values. Relative to healthy subjects, significantly more pathological within-lobe connections were found in the parietal lobe of patients with Alzheimer's disease, and in the frontal and temporal lobes of subjects with frontotemporal lobar degeneration. Relative to the frontotemporal lobar degeneration group, more pathological connections between the parietal and temporal lobe were found in the Alzheimer's disease group. The obtained connectivity patterns differentiated between two patients groups with an overall accuracy of 83%. Linear discriminant analysis and univariate methods provided an accuracy of 74% and 69%, respectively. There are characteristic patterns of abnormal metabolic connectivity in mild Alzheimer's disease and frontotemporal lobar degeneration. Such patterns can be utilized for single-subject analyses and might be more accurate in the differential diagnosis of dementing disorders than traditional intensity-based analyses.

Modulation of glucose metabolism and metabolic connectivity by β-amyloid

Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar β-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to β-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as β-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of β-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling β-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and β-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of β-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.