Brain connectomics: time for a molecular imaging perspective?

Single-Subject Network Analysis of FDOPA PET in Parkinson's Disease and Psychosis Spectrum

Greater understanding of individual biological differences is essential for developing more targeted treatment approaches to complex brain disorders. Traditional analysis methods in molecular imaging studies have primarily focused on quantifying tracer binding in specific brain regions, often neglecting inter-regional functional relationships. In this study, we propose a statistical framework that combines molecular imaging data with perturbation covariance analysis to construct single-subject networks and investigate individual patterns of molecular alterations. This framework was tested on [18F]-DOPA PET imaging as a marker of the brain dopamine system in patients with Parkinson's Disease (PD) and schizophrenia to evaluate its ability to classify patients and characterize their disease severity. Our results show that single-subject networks effectively capture molecular alterations, differentiate individuals with heterogeneous conditions, and account for within-group variability. Moreover, the approach successfully distinguishes between preclinical and clinical stages of psychosis and identifies the corresponding molecular connectivity changes in response to antipsychotic medications. Mapping molecular imaging networks presents a new and powerful method for characterizing individualized disease trajectories as well as for evaluating treatment effectiveness in future research.

Mapping serotonergic dynamics using drug-modulated molecular connectivity in rats

Understanding the complex workings of the brain is one of the most significant challenges in neuroscience, providing insights into normal brain function, neurological diseases, and the effects of potential therapeutics. A major challenge in this field lies in the limitations of traditional brain imaging techniques, which often capture only fragments of the complex puzzle of brain function. Our research employs a novel approach termed 'molecular connectivity' (MC), which combines the strengths of various imaging methods to provide a comprehensive view of how specific molecules, such as the serotonin transporter, interact across different brain regions and influence brain function. This innovative technique bridges the gap between functional magnetic resonance imaging (fMRI), known for its ability to monitor brain activity by tracking blood flow, and positron emission tomography (PET), which visualizes specific molecular changes. By integrating these methods, we can better understand how drugs influence brain function. Our study focuses on the application of dynamic [11C]DASB PET scans to map the distribution of serotonin transporters, key players in regulating mood and emotions, and examines how these transporters are altered following exposure to methylenedioxymethamphetamine (MDMA), which is commonly known as ecstasy. Through a detailed comparison of MC with traditional measures of brain connectivity, we reveal significant patterns that closely align with physiological changes. Our results revealed clear changes in molecular connectivity after a single dose of MDMA, establishing a direct link between the effects of drugs on serotonin transporter occupancy and changes in the functional brain network. This work offers a novel methodology for the in-depth study of brain function at the molecular level and opens new pathways for understanding how drugs modulate brain activity.

Influence of diabetes mellitus on metabolic networks in lung cancer patients: an analysis using dynamic total-body PET/CT imaging

INTRODUCTION

The intricate interplay between organs can give rise to a multitude of physiological conditions. Disruptions such as inflammation or tissue damage can precipitate the development of chronic diseases such as tumors or diabetes mellitus (DM). While both lung cancer and DM are the consequences of disruptions in homeostasis, the relationship between them is intricate. This study sought to investigate the potential influence of DM on lung cancer by employing total-body dynamic PET imaging.

METHODS

The present study proposes a framework for metabolic network analysis using total-body dynamic PET imaging of 20 lung cancer patients with DM (DM group) and 20 lung cancer patients without DM (Non-DM group), with the residuals of a third-order polynomial fit serving as an indicator of Pearson correlation.

RESULTS

The framework successfully captured the deviation of the DM group from the Non-DM group at both the edge and organ levels. At the edge level, there was a significant difference in the lesion- left ventricle (LV) between the DM and Non-DM groups (P < 0.05). Furthermore, we discovered a positive correlation between the absolute value of Z-score (ZCC) of lesion - LV and the duration of DM (R = 0.680, P < 0.001). At the organ level, there was a significant difference in the kidney, brain, and abdominal fat between the DM and Non-DM groups (P < 0.05).

CONCLUSION

This study demonstrated the feasibility of constructing metabolic networks to uncover complex alterations in lung cancer patients with DM. The findings contribute to understanding the systemic effects of DM on lung cancer metabolism and highlight the importance of personalized metabolic network analysis to comprehend the implications of concurrent diseases.

Reorganization of brain connectivity in post-COVID condition: a 18F-FDG PET study

BACKGROUND

A hypometabolic brain pattern has been reported in patients with post-COVID condition (PCC). The aim of this study was to investigate reorganization in metabolic connectivity in patients with PCC.

RESULTS

One hundred eighty-eight patients who underwent brain 18F-FDG PET for PCC were retrospectively included from two university hospital centres. These patients were age- and sex-matched to 120 healthy controls who underwent brain 18F-FDG PET before the COVID-19 outbreak. A voxel-based group comparison between patients and controls was performed (p-voxel at 0.005 uncorrected, p-cluster at 0.05 FWE corrected). Interregional correlation analyses of the identified clusters as well as sparse inverse covariance estimations at whole-brain scaling were also conducted. Both analyses were performed at the group level for all patients and then secondarily according to the postinfection delay; 88 and 100 patients, respectively, had a delay of less than or greater than 9 months (± 9 M). Three hypometabolic clusters, namely, the right frontotemporal, right and left cerebellar, were identified from the voxel-based group comparisons of PCC patients. Within this hypometabolic PCC pattern, a modification in metabolic connectivity was observed in patients compared with controls, which was more marked in the + 9 M group than in the - 9 M group. On the other hand, the graph analysis revealed a decrease in connectivity efficiency metrics in the PCC.

CONCLUSIONS

Metabolic connectivity is modified in patients with PCC within the hypometabolic post-COVID-19 network, with lasting reorganization evolving over time, suggesting functional adaptation.

Metabolic connectivity has greater predictive utility for age and cognition than functional connectivity

Recently developed high temporal resolution functional (18F)-fluorodeoxyglucose positron emission tomography (fPET) offers promise as a method for indexing the dynamic metabolic state of the brain in vivo by directly measuring a time series of metabolism at the post-synaptic neuron. This is distinct from functional magnetic resonance imaging (fMRI) that reflects a combination of metabolic, haemodynamic and vascular components of neuronal activity. The value of using fPET to understand healthy brain ageing and cognition over fMRI is currently unclear. Here, we use simultaneous fPET/fMRI to compare metabolic and functional connectivity and test their predictive ability for ageing and cognition. Whole-brain fPET connectomes showed moderate topological similarities to fMRI connectomes in a cross-sectional comparison of 40 younger (mean age 27.9 years; range 20-42) and 46 older (mean 75.8; 60-89) adults. There were more age-related within- and between-network connectivity and graph metric differences in fPET than fMRI. fPET was also associated with performance in more cognitive domains than fMRI. These results suggest that ageing is associated with a reconfiguration of metabolic connectivity that differs from haemodynamic alterations. We conclude that metabolic connectivity has greater predictive utility for age and cognition than functional connectivity and that measuring glucodynamic changes has promise as a biomarker for age-related cognitive decline.

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard

PURPOSE

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression.

METHODS

We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology's impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice.

RESULTS

Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations.

CONCLUSION

Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

Persistent dysfunctions of brain metabolic connectivity in long-covid with cognitive symptoms

PURPOSE

Our study examines brain metabolic connectivity in SARS-CoV-2 survivors during the acute-subacute and chronic phases, aiming to elucidate the mechanisms underlying the persistence of neurological symptoms in long-COVID patients.

METHODS

We perfomed a cross-sectional study including 44 patients (pts) with neurological symptoms who underwent FDG-PET scans, and classified to timing infection as follows: acute (7 pts), subacute (17 pts), long-term (20 pts) phases. Interregional correlation analysis (IRCA) and ROI-based IRCA were applied on FDG-PET data to extract metabolic connectivity in resting state networks (ADMN, PDMN, EXN, ATTN, LIN, ASN) of neuro-COVID pts in acute/subacute and long-term groups compared with healthy controls (HCs). Univariate approach was used to investigate metabolic alterations from the acute to sub-acute and long-term phase.

RESULTS

The acute/subacute phase was characterized by hyperconnectivity in EXN and ATTN networks; the same networks showed hypoconnectivity in the chronic phase. EXN and ATTN hypoconnectivity was consistent with clinical findings in long-COVID patients, e.g. altered performances in neuropsychological tests of executive and attention domains. The ASN and LIN presented hyperconnectivity in acute/subacute phase and normalized in long-term phase. The ADMN and PDMN presented a preseverved connectivity. Univariate analysis showed hypometabolism in fronto-insular cortex in acute phase, which reduced in sub-acute phase and disappeared in long-term phase.

CONCLUSION

A compensatory EXN and ATTN hyperconnectivity was found in the acute/subacute phase and hypoconnectivity in long-term. Hypoconnectivity and absence of hypometabolism suggest that connectivity derangement in frontal networks could be related to protraction of neurological symptoms in long-term COVID patients.

Age-Related Changes in Caudate Glucose Metabolism: Insights from Normative Modeling Study in Healthy Subjects

BACKGROUND

As the global population ages, the prevalence of neurodegenerative conditions, such as Alzheimer's disease (AD), Parkinson's disease (PD), dementia with Lewy bodies, and frontotemporal dementia, continues to rise. Understanding the impact of aging on striatal glucose metabolism is pivotal in identifying potential biomarkers for the early detection of these disorders.

METHODS

We investigated age-related changes in striatal glucose metabolism using both region of interest (ROI)-based and voxel-wise correlation analyses. Additionally, we employed a normative modeling approach to establish age-related metabolic trajectories and assess individual deviations from these normative patterns. In vivo cerebral glucose metabolism was quantified using a molecular neuroimaging technique, 18F-FDG PET.

RESULTS

Our results revealed significant negative correlations between age and glucose metabolism in the bilateral caudate. Furthermore, the normative modeling demonstrated a clear, progressive decline in caudate metabolism with advancing age, and the most pronounced reductions were observed in older individuals.

CONCLUSIONS

These findings suggest that metabolic reductions in the caudate may serve as a sensitive biomarker for normal aging and offer valuable insights into the early stages of neurodegenerative diseases. Moreover, by establishing age-specific reference values for caudate glucose metabolism, the normative model provides a framework for detecting deviations from expected metabolic patterns, which may facilitate the early identification of metabolic alterations that could precede clinical symptoms of neurodegenerative processes.

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.

Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction

Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.

Modeling PET Data Acquired During Nonsteady Conditions: What If Brain Conditions Change During the Scan?

Researchers use dynamic PET imaging with target-selective tracer molecules to probe molecular processes. Kinetic models have been developed to describe these processes. The models are typically fitted to the measured PET data with the assumption that the brain is in a steady-state condition for the duration of the scan. The end results are quantitative parameters that characterize the molecular processes. The most common kinetic modeling endpoints are estimates of volume of distribution or the binding potential of a tracer. If the steady state is violated during the scanning period, the standard kinetic models may not apply. To address this issue, time-variant kinetic models have been developed for the characterization of dynamic PET data acquired while significant changes (e.g., short-lived neurotransmitter changes) are occurring in brain processes. These models are intended to extract a transient signal from data. This work in the PET field dates back at least to the 1990s. As interest has grown in imaging nonsteady events, development and refinement of time-variant models has accelerated. These new models, which we classify as belonging to the first, second, or third generation according to their innovation, have used the latest progress in mathematics, image processing, artificial intelligence, and statistics to improve the sensitivity and performance of the earliest practical time-variant models to detect and describe nonsteady phenomena. This review provides a detailed overview of the history of time-variant models in PET. It puts key advancements in the field into historical and scientific context. The sum total of the methods is an ongoing attempt to better understand the nature and implications of neurotransmitter fluctuations and other brief neurochemical phenomena.

α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor density underlies intraregional and interregional functional centrality

Local and global functional connectivity densities (lFCD and gFCD, respectively), derived from functional magnetic resonance imaging (fMRI) data, represent the degree of functional centrality within local and global brain networks. While these methods are well-established for mapping brain connectivity, the molecular and synaptic foundations of these connectivity patterns remain unclear. Glutamate, the principal excitatory neurotransmitter in the brain, plays a key role in these processes. Among its receptors, the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) is crucial for neurotransmission, particularly in cognitive functions such as learning and memory. This study aimed to examine the association of the AMPAR density and FCD metrics of intraregional and interregional functional centrality. Using [11C]K-2, a positron emission tomography (PET) tracer specific for AMPARs, we measured AMPAR density in the brains of 35 healthy participants. Our findings revealed a strong positive correlation between AMPAR density and both lFCD and gFCD-lFCD across the entire brain. This correlation was especially notable in key regions such as the anterior cingulate cortex, posterior cingulate cortex, pre-subgenual frontal cortex, Default Mode Network, and Visual Network. These results highlight that postsynaptic AMPARs significantly contribute to both local and global functional connectivity in the brain, particularly in network hub regions. This study provides valuable insights into the molecular and synaptic underpinnings of brain functional connectomes.

Intra-striatal dopaminergic inter-subject covariance in social drinkers and non-treatment-seeking alcohol use disorder participants

One of the neurobiological correlates of alcohol use disorder (AUD) is the disruption of striatal dopaminergic function. Although regional differences in dopamine (DA) tone/function have been well studied, interregional relationships (represented as inter-subject covariance) have not been investigated and may offer a novel avenue for understanding DA tone. Positron emission tomography (PET) data with [11C]raclopride in 22 social drinking controls and 17 AUD participants were used to generate group-level striatal covariance (partial Pearson correlation) networks, which were compared edgewise as well as on global network metrics and community structure. An exploratory analysis examined the impact of tobacco cigarette use status. Striatal covariance was validated in an independent publicly available [18F]fallypride PET sample of healthy volunteers. Striatal covariance of control participants from both data sets showed a clear bipartition of the network into two distinct communities, one in the anterior and another in the posterior striatum. This organization was disrupted in the AUD participants' network, which showed significantly lower network metrics compared with the control participants' network. Stratification by cigarette use suggests differential consequences on group covariance networks. This work demonstrates that network neuroscience can quantify group differences in striatal DA and that its interregional interactions offer new insight into the consequences of AUD.

Local activity alterations in autism spectrum disorder correlate with neurotransmitter properties and ketamine induced brain changes

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered resting-state brain function. An increased excitation-inhibition (E/I) ratio is discussed as a potential pathomechanism but in-vivo evidence of disturbed neurotransmission underlying these functional alterations remains scarce. We compared rs-fMRI local activity (LCOR) between ASD (N=405, N=395) and neurotypical controls (N=473, N=474) in two independent cohorts (ABIDE1 and ABIDE2). We then tested how these LCOR alterations co-localize with specific neurotransmitter systems derived from nuclear imaging and compared them with E/I changes induced by GABAergic (midazolam) and glutamatergic medication (ketamine). Across both cohorts, ASD subjects consistently exhibited reduced LCOR, particularly in higher-order default mode network nodes, alongside increases in bilateral temporal regions, the cerebellum, and brainstem. These LCOR alterations negatively co-localized with dopaminergic (D1, D2, DAT), glutamatergic (NMDA, mGluR5), GABAergic (GABAa) and cholinergic neurotransmission (VAChT). The NMDA-antagonist ketamine, but not GABAa-potentiator midazolam, induced LCOR changes which co-localize with D1, NMDA and GABAa receptors, thereby resembling alterations observed in ASD. We find consistent local activity alterations in ASD to be spatially associated with several major neurotransmitter systems. NMDA-antagonist ketamine induced neurochemical changes similar to ASD-related alterations, supporting the notion that pharmacological modulation of the E/I balance in healthy individuals can induce ASD-like functional brain changes. These findings provide novel insights into neurophysiological mechanisms underlying ASD.

Association Between Glaucoma and Brain Structural Connectivity Based on Diffusion Tensor Tractography: A Bidirectional Mendelian Randomization Study

BACKGROUND

Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality.

METHODS

To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed.

RESULTS

Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity.

CONCLUSIONS

This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma.

Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson's disease

PURPOSE

While fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level.

METHODS

In the current study, this technique was employed to explore Parkinson's disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI.

RESULTS

Our findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni-Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni-Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036).

CONCLUSION

Metabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.

Omics-derived biological modules reflect metabolic brain changes in Alzheimer's disease

INTRODUCTION

Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive.

METHODS

Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis.

RESULTS

Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223) = 4.86, P value < 0.001) and zinc-finger-related regulatory units (peak-t(223) = 3.90, P value < 0.001).

DISCUSSION

By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD.

HIGHLIGHTS

We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Regional patterns of human cortex development correlate with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

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.

The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.

Tau propagation in the brain olfactory circuits is associated with smell perception changes in aging

The direct access of olfactory afferents to memory-related cortical systems has inspired theories about the role of the olfactory pathways in the development of cortical neurodegeneration in Alzheimer's disease (AD). In this study, we used baseline olfactory identification measures with longitudinal flortaucipir and PiB PET, diffusion MRI of 89 cognitively normal older adults (73.82 ± 8.44 years; 56% females), and a transcriptomic data atlas to investigate the spatiotemporal spreading and genetic vulnerabilities of AD-related pathology aggregates in the olfactory system. We find that odor identification deficits are predominantly associated with tau accumulation in key areas of the olfactory pathway, with a particularly strong predictive power for longitudinal tau progression. We observe that tau spreads from the medial temporal lobe structures toward the olfactory system, not the reverse. Moreover, we observed a genetic background of odor perception-related genes that might confer vulnerability to tau accumulation along the olfactory system.

Effects of Tasks on Functional Brain Connectivity Derived from Inter-Individual Correlations: Insights from Regional Homogeneity of Functional MRI Data

Research on brain functional connectivity often relies on intra-individual moment-to-moment correlations of functional brain activity, typically using techniques like functional MRI (fMRI). Inter-individual correlations are also employed on data from fMRI and positron emission tomography (PET). Many past studies have not specified tasks for participants, keeping them in an implicit "resting" condition. This lack of task specificity raises questions about how different tasks impact inter-individual correlation estimates. In our analysis of fMRI data from 100 unrelated participants, scanned during seven task conditions and in a resting state, we calculated Regional Homogeneity (ReHo) for each task as a regional measure of brain functions. We found that changes in ReHo due to different tasks were relatively small compared with the variations across brain regions. Cross-region variations of ReHo were highly correlated between different tasks. Similarly, whole-brain inter-individual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in inter-individual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, default mode network, and the interactions between them. The subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring inter-individual connectivity in specific brain systems.

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

Multi-modal Neuroimaging Phenotyping of Mnemonic Anosognosia in the Aging Brain

BACKGROUND

Unawareness is a behavioral condition characterized by a lack of self-awareness of objective memory decline. In the context of Alzheimer's Disease (AD), unawareness may develop in predementia stages and contributes to disease severity and progression. Here, we use in-vivo multi-modal neuroimaging to profile the brain phenotype of individuals presenting altered self-awareness of memory during aging.

METHODS

Amyloid- and tau-PET (N = 335) and resting-state functional MRI (N = 713) imaging data of individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4)/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study were used in this research. We applied whole-brain voxel-wise and region-of-interest analyses to characterize the cortical intersections of tau, amyloid, and functional connectivity networks underlying unawareness in the aging brain compared to aware, complainer and control groups.

RESULTS

Individuals with unawareness present elevated amyloid and tau burden in midline core regions of the default mode network compared to aware, complainer or control individuals. Unawareness is characterized by an altered network connectivity pattern featuring hyperconnectivity in the medial anterior prefrontal cortex and posterior occipito-parietal regions co-locating with amyloid and tau deposition.

CONCLUSIONS

Unawareness is an early behavioral biomarker of AD pathology. Failure of the self-referential system in unawareness of memory decline can be linked to amyloid and tau burden, along with functional network connectivity disruptions, in several medial frontal and parieto-occipital areas of the human brain.

Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage.

METHODS

We performed 18 F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis.

RESULTS

The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model.

DISCUSSION

The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.

Combination of fMRI and PET reveals the beneficial effect of three-phase enriched environment on post-stroke memory deficits by enhancing plasticity of brain connectivity between hippocampus and peri-hippocampal cortex

AIM

The three-phase enriched environment (EE) intervention paradigm has been shown to improve learning and memory function after cerebral ischemia, but the neuronal mechanisms are still unclear. This study aimed to investigate the hippocampal-cortical connectivity and the metabolic interactions between neurons and astrocytes to elucidate the underlying mechanisms of EE-induced memory improvement after stroke.

METHODS

Rats were subjected to permanent middle cerebral artery occlusion (pMCAO) or sham surgery and housed in standard environment or EE for 30 days. Memory function was examined by Morris water maze (MWM) test. Magnetic resonance imaging (MRI) was conducted to detect the structural and functional changes. [18 F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) was conducted to detect brain energy metabolism. PET-based brain connectivity and network analysis was performed to study the changes of hippocampal-cortical connectivity. Astrocyte-neuron metabolic coupling, including gap junction protein connexin 43 (Cx43), glucose transporters (GLUTs), and monocarboxylate transporters (MCTs), was detected by histological studies.

RESULTS

Our results showed EE promoted memory function improvement, protected structure integrity, and benefited energy metabolism after stroke. More importantly, EE intervention significantly increased functional connectivity between the hippocampus and peri-hippocampal cortical regions, and specifically regulated the level of Cx43, GLUTs and MCTs in the hippocampus and cortex.

CONCLUSIONS

Our results revealed the three-phase enriched environment paradigm enhanced hippocampal-cortical connectivity plasticity and ameliorated post-stroke memory deficits. These findings might provide some new clues for the development of EE and thus facilitate the clinical transformation of EE.

Diagnostic value of PET imaging in clinically unresponsive patients

Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts.

Fragmentation of functional resting state brain networks in a transgenic mouse model of tau pathology: A metabolic connectivity study using [18F]FDG-PET

In a previous study, regional reductions in cerebral glucose metabolism have been demonstrated in the tauopathy mouse model rTg4510 (Endepols et al., 2022). Notably, glucose hypometabolism was present in some brain regions without co-localized synaptic degeneration measured with [18F]UCB-H. We hypothesized that in those regions hypometabolism may reflect reduced functional connectivity rather than synaptic damage. To test this hypothesis, we performed seed-based metabolic connectivity analyses using [18F]FDG-PET data in this mouse model. Eight rTg4510 mice at the age of seven months and 8 non-transgenic littermates were injected intraperitoneally with 11.1 ± 0.8 MBq [18F]FDG and spent a 35-min uptake period awake in single cages. Subsequently, they were anesthetized and measured in a small animal PET scanner for 30 min. Three seed-based connectivity analyses were performed per group. Seeds were selected for apparent mismatch between [18F]FDG and [18F]UCB-H. A seed was placed either in the medial orbitofrontal cortex, dorsal hippocampus or dorsal thalamus, and correlated with all other voxels of the brain across animals. In the control group, the emerging correlative pattern was strongly overlapping for all three seed locations, indicating a uniform fronto-thalamo-hippocampal resting state network. In contrast, rTg4510 mice showed three distinct networks with minimal overlap. Frontal and thalamic networks were greatly diminished. The hippocampus, however, formed a new network with the whole parietal cortex. We conclude that resting-state functional networks are fragmented in the brain of rTg4510 mice. Thus, hypometabolism can be explained by reduced functional connectivity of brain areas devoid of tau-related pathology, such as the thalamus.

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.

Similarity between structural and proxy estimates of brain connectivity

Functional magnetic resonance and diffusion weighted imaging have so far made a major contribution to delineation of the brain connectome at the macroscale. While functional connectivity (FC) was shown to be related to structural connectivity (SC) to a certain degree, their spatial overlap is unknown. Even less clear are relations of SC with estimates of connectivity from inter-subject covariance of regional F18-fluorodeoxyglucose uptake (FDGcov) and grey matter volume (GMVcov). Here, we asked to what extent SC underlies three proxy estimates of brain connectivity: FC, FDGcov and GMVcov. Simultaneous PET/MR acquisitions were performed in 56 healthy middle-aged individuals. Similarity between four networks was assessed using Spearman correlation and convergence ratio (CR), a measure of spatial overlap. Spearman correlation coefficient was 0.27 for SC-FC, 0.40 for SC-FDGcov, and 0.15 for SC-GMVcov. Mean CRs were 51% for SC-FC, 48% for SC-FDGcov, and 37% for SC-GMVcov. These results proved to be reproducible and robust against image processing steps. In sum, we found a relevant similarity of SC with FC and FDGcov, while GMVcov consistently showed the weakest similarity. These findings indicate that white matter tracts underlie FDGcov to a similar degree as FC, supporting FDGcov as estimate of functional brain connectivity.

[The relationship between neuropsychological indicators and neuroimaging changes according to MRI morphometry in patients with Alzheimer's disease and glaucoma]

OBJECTIVE

Study of neuroimaging changes according to MRI morphometry and their comparison with the structure and severity of cognitive impairment (CI) in patients with Alzheimer's disease (AD) and primary open-angle glaucoma (POAG).

MATERIAL AND METHODS

The study involved 90 patients who were divided into two equal groups of 45 people and who early had diagnosis of AD (group 1; median age - 71 [66; 77] years) and POAG (group 2; median age - 68 [64; 77] years). 71] years). All patients underwent assessment of their neurological status, neuropsychological testing, structural MRI of the brain, followed by morphometric data processing. For the purpose of comparative assessment of the obtained MRI morphometry indicators, a group of healthy individuals was taken - group 3 (n=10).

RESULTS

In patients with AD, severe cognitive impairment (CI) was detected, and in patients with POAG, pre-dementia CI with a similar neurodegenerative nature was identified. According to MRI morphometry, in the group of patients with AD compared with POAG, there was a decrease in the volumes of gray matter of the brain, hippocampus, right thalamus, amygdala, entorhinal cortex, right cingulate gyrus, fusiform gyrus, as well as thicknesses: entorhinal cortex, cingulate gyrus and fusiform gyrus (p<0.05). When comparing volumes according to MRI morphometry with healthy individuals, patients with AD revealed a statistically significant decrease in all studied neuroimaging indicators, and when comparing thicknesses: left entorhinal cortex, fusiform gyrus; while in POAG only a decrease in the volumes of gray matter of the brain, thalamus, and right medial orbitofrontal cortex was noted.

CONCLUSION

POAG can be considered not only as an independent disease, but also as a predictor of the development of AD, therefore, the statistically significant differences we obtained between the AD group and POAG according to MRI morphometry may reflect the dynamics of the neurodegenerative process and depend on the severity of CI. In this connection, MRI morphometry can be considered not only as a method of early diagnosis, but also as an assessment of disease progression. In this case, it is important to determine not only the thicknesses, but also the volumes of brain structures according to MRI morphometry data.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

[Cerebral Small Vessel Disease: Advances in Understanding its Pathophysiology]

Sporadic cerebral small vessel disease determines age- and vascular-risk-factor-related processes of the small brain vasculature. The underlying pathology develops in a stage-dependent manner - probably over decades - often already starting in midlife. Endothelial and pericyte activation precedes blood-brain barrier leaks, extracellular matrix remodeling and neuroinflammation, which ultimately result in bleeds, synaptic and neural dysfunction. Hemodynamic compromise of the small vessel walls promotes perivascular drainage failure and accumulation of neurotoxic waste products in the brain. Clinical diagnosis is mainly based on magnetic resonance imaging according to the Standards for Reporting Vascular Changes on Neuroimaging 2. Cerebral amyloid angiopathy is particularly stratified according to the Boston v2.0 criteria. Small vessel disease of the brain could be clinically silent, or manifested through a heterogeneous spectrum of diseases, where cognitive decline and stroke-related symptoms are the most common ones. Prevention and therapy are centered around vascular risk factor control, physically and cognitively enriched life style and, presumably, maintenance of a good sleep quality, which promotes sufficient perivascular drainage. Prevention of ischemic stroke through anticoagulation that carries at the same time an increased risk for large brain hemorrhages - particularly in the presence of disseminated cortical superficial siderosis - remains one of the main challenges. The cerebral small vessel disease field is rapidly evolving, focusing on the establishment of early disease stage imaging and biofluid biomarkers of neurovascular unit remodeling and the compromise of perivascular drainage. New prevention and therapy strategies will correspondingly center around the dedicated targeting of, e. g., cellular small vessel wall and perivascular tissue structures. Growing knowledge about brain microvasculature bridging neuroimmunological, neurovascular and neurodegenerative fields might lead to a rethink about apparently separate disease entities and the development of overarching concepts for a common line of prevention and treatment for several diseases.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Tau-PET abnormality as a biomarker for Alzheimer's disease staging and early detection: a topological perspective

Alzheimer's disease can be detected early through biomarkers such as tau positron emission tomography (PET) imaging, which shows abnormal protein accumulations in the brain. The standardized uptake value ratio (SUVR) is often used to quantify tau-PET imaging, but topological information from multiple brain regions is also linked to tau pathology. Here a new method was developed to investigate the correlations between brain regions using subject-level tau networks. Participants with cognitive normal (74), early mild cognitive impairment (35), late mild cognitive impairment (32), and Alzheimer's disease (40) were included. The abnormality network from each scan was constructed to extract topological features, and 7 functional clusters were further analyzed for connectivity strengths. Results showed that the proposed method performed better than conventional SUVR measures for disease staging and prodromal sign detection. For example, when to differ healthy subjects with and without amyloid deposition, topological biomarker is significant with P < 0.01, SUVR is not with P > 0.05. Functionally significant clusters, i.e. medial temporal lobe, default mode network, and visual-related regions, were identified as critical hubs vulnerable to early disease conversion before mild cognitive impairment. These findings were replicated in an independent data cohort, demonstrating the potential to monitor the early sign and progression of Alzheimer's disease from a topological perspective for individual.

New Advances in the Exploration of Esterases with PET and Fluorescent Probes

Esterases are hydrolases that catalyze the hydrolysis of esters into the corresponding acids and alcohols. The development of fluorescent probes for detecting esterases is of great importance due to their wide spectrum of biological and industrial applications. These probes can provide a rapid and sensitive method for detecting the presence and activity of esterases in various samples, including biological fluids, food products, and environmental samples. Fluorescent probes can also be used for monitoring the effects of drugs and environmental toxins on esterase activity, as well as to study the functions and mechanisms of these enzymes in several biological systems. Additionally, fluorescent probes can be designed to selectively target specific types of esterases, such as those found in pathogenic bacteria or cancer cells. In this review, we summarize the recent fluorescent probes described for the visualization of cell viability and some applications for in vivo imaging. On the other hand, positron emission tomography (PET) is a nuclear-based molecular imaging modality of great value for studying the activity of enzymes in vivo. We provide some examples of PET probes for imaging acetylcholinesterases and butyrylcholinesterases in the brain, which are valuable tools for diagnosing dementia and monitoring the effects of anticholinergic drugs on the central nervous system.

A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used imaging techniques to visualize brain function. The use of inter-network covariation (a functional connectome) is a widely used approach to infer links among different brain networks. While whole brain resting fMRI connectomes are widely used, PET data has mostly been analyzed using a few regions of interest. There has been much less work estimating PET spatial networks and almost no work on their connectivity (covariation) in the context of a whole brain data-driven connectome, nor have there been direct comparisons between whole brain PET and fMRI connectomes. Here we present an approach to leverage spatially constrained ICA to compute an estimate of the PET connectome. Results reveal highly modularized connectome patterns that are complementary to that identified from resting fMRI. Similarly, we were able to identify comparable resting networks from a PiB PET scan that can be directly compared to networks in rest fMRI data and results reveal similar, but not identical, network spatial patterns, with the PET networks being slightly smoother and, in some cases, showing variations in subnodes. The resulting networks, decomposed into spatial maps and subject expressions (loading parameters) linked to resting fMRI provide a new way to evaluate the complementary information in PET and fMRI and open up new possibilities for biomarker development.Clinical Relevance-This study analyzes the whole-brain PET and fMRI connectomes, capturing the complementary information from both imaging modalities, thereby introducing a new scope for biomarker development.