Cross-sectional and Longitudinal Age-related Disintegration in Functional Connectivity: Reference Ability Neural Network Cohort.

Some theories of aging have linked age-related cognitive decline to a reduction in distinctiveness of neural processing. Observed age-related correlation increases among disparate cognitive tasks have supported the dedifferentiation hypothesis. We previously showed cross-sectional evidence for age-related correlation decreases instead, supporting an alternative disintegration hypothesis. In the current study, we extended our previous research to a longitudinal sample. We tested 135 participants (20-80 years) at two time points-baseline and 5-year follow-up-on a battery of 12 in-scanner tests, each tapping one of four reference abilities. We performed between-tasks correlations within domain (convergent) and between domain (discriminant) at both the behavioral and neural level, calculating a single measure of construct validity (convergent - discriminant). Cross-sectionally, behavioral construct validity was significantly different from chance at each time point, but longitudinal change was not significant. Analysis by median age split revealed that older adults showed higher behavioral validity, driven by higher discriminant validity (lower between-tasks correlations). Participant-level neural validity decreased over time, with convergent validity consistently greater than discriminant validity; this finding was also observed at the cross-sectional level. In addition, a disproportionate decrease in neural validity with age remained significant after controlling for demographic factors. Factors predicting longitudinal changes in global cognition (mean performance across all 12 tasks) included age, change in neural validity, education, and National Adult Reading Test (premorbid intelligence). Change in neural validity partially mediated the effect of age on change in global cognition. Our findings support the theory of age-related disintegration, linking cognitive decline to changes in neural representations over time.

Brain reserve affects the expression of cognitive reserve networks.

Cognitive reserve (CR) explains differential susceptibility of cognitive performance to neuropathology. However, as brain pathologies progress, cognitive decline occurs even in individuals with initially high CR. The interplay between the structural brain health (= level of brain reserve) and CR-related brain networks therefore requires further research. Our sample included 142 individuals aged 60-70 years. National Adult Reading Test intelligence quotient (NART-IQ) was our CR proxy. On an in-scanner Letter Sternberg task, we used ordinal trend (OrT) analysis to extract a task-related brain activation pattern (OrT slope) for each participant that captures increased expression with task load (one, three, and six letters). We assessed whether OrT slope represents a neural mechanism underlying CR by associating it with task performance and NART-IQ. Additionally, we investigated how the following brain reserve measures affect the association between NART-IQ and OrT slope: mean cortical thickness, total gray matter volume, and brain volumes proximal to the areas contained in the OrT patterns. We found that higher OrT slope was associated with better task performance and higher NART-IQ. Further, the brain reserve measures were not directly associated with OrT slope, but they affected the relationship between NART-IQ and OrT slope: NART-IQ was associated with OrT slope only in individuals with high brain reserve. The degree of brain reserve has an impact on how (and perhaps whether) CR can be implemented in brain networks in older individuals.

A randomized trial of blood donor iron repletion on red cell quality for transfusion and donor cognition and well-being.

Although altruistic regular blood donors are vital for the blood supply, many become iron deficient from donation-induced iron loss. The effects of blood donation-induced iron deficiency on red cell transfusion quality or donor cognition are unknown. In this double-blind, randomized trial, adult iron-deficient blood donors (n = 79; ferritin < 15 µg/L and zinc protoporphyrin >60 µMol/mol heme) who met donation qualifications were enrolled. A first standard blood donation was followed by the gold-standard measure for red cell storage quality: a 51-chromium posttransfusion red cell recovery study. Donors were then randomized to intravenous iron repletion (1 g low-molecular-weight iron dextran) or placebo. A second donation ∼5 months later was followed by another recovery study. Primary outcome was the within-subject change in posttransfusion recovery. The primary outcome measure of an ancillary study reported here was the National Institutes of Health Toolbox-derived uncorrected standard Cognition Fluid Composite Score. Overall, 983 donors were screened; 110 were iron-deficient, and of these, 39 were randomized to iron repletion and 40 to placebo. Red cell storage quality was unchanged by iron repletion: mean change in posttransfusion recovery was 1.6% (95% confidence interval -0.5 to 3.8) and -0.4% (-2.0 to 1.2) with and without iron, respectively. Iron repletion did not affect any cognition or well-being measures. These data provide evidence that current criteria for blood donation preserve red cell transfusion quality for the recipient and protect adult donors from measurable effects of blood donation-induced iron deficiency on cognition. This trial was registered at www.clinicaltrials.gov as NCT02889133 and NCT02990559.

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort.

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Persistent homology-based functional connectivity and its association with cognitive ability: Life-span study.

Brain-segregation attributes in resting-state functional networks have been widely investigated to understand cognition and cognitive aging using various approaches [e.g., average connectivity within/between networks and brain system segregation (BSS)]. While these approaches have assumed that resting-state functional networks operate in a modular structure, a complementary perspective assumes that a core-periphery or rich club structure accounts for brain functions where the hubs are tightly interconnected to each other to allow for integrated processing. In this article, we apply a novel method, persistent homology (PH), to develop an alternative to standard functional connectivity by quantifying the pattern of information during the integrated processing. We also investigate whether PH-based functional connectivity explains cognitive performance and compare the amount of variability in explaining cognitive performance for three sets of independent variables: (1) PH-based functional connectivity, (2) graph theory-based measures, and (3) BSS. Resting-state functional connectivity data were extracted from 279 healthy participants, and cognitive ability scores were generated in four domains (fluid reasoning, episodic memory, vocabulary, and processing speed). The results first highlight the pattern of brain-information flow over whole brain regions (i.e., integrated processing) accounts for more variance of cognitive abilities than other methods. The results also show that fluid reasoning and vocabulary performance significantly decrease as the strength of the additional information flow on functional connectivity with the shortest path increases. While PH has been applied to functional connectivity analysis in recent studies, our results demonstrate potential utility of PH-based functional connectivity in understanding cognitive function.

Transfer learning for cognitive reserve quantification.

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

Segregation of functional networks is associated with cognitive resilience in Alzheimer's disease.

Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer's disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain's connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer's disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer's disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer's disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer's disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer's sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer's disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer's disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer's disease.

A framework for identification of a resting-bold connectome associated with cognitive reserve.

The concept of cognitive reserve proposes that specific life experiences result in more flexible or resilient cognitive processing allowing some people to cope better with age- or disease-related brain changes than others. Imaging studies seeking to understand the neural implementation of cognitive reserve have most often used task-related fMRI studies. Using that approach, we recently described a task-invariant cognitive-reserve network whose expression correlated with IQ and that moderated between cortical thickness and cognitive performance. Here we sought to identify a pattern of resting BOLD connectivity related to cognitive reserve. We identified a connectome pattern whose connectivity correlated with IQ in both the derivation sample and a separate replication sample. The majority of the edges showing positive relationships with IQ implicate frontal regions. In the derivation sample, connectivity either moderated the relationship between mean cortical thickness and a set of cognitive outcomes or accounted for unique variance in cognitive performance after accounting for cortical thickness. In a replication sample we found that expression of this connectome correlated significantly with the primary endpoint of IQ, and also accounted for unique variance in cognitive performance beyond cortical thickness. Our findings represent an intermediate level of replication and are unlikely to have arisen purely by type-I error. This connectivity pattern therefore meets some of our theoretical criteria for a cognitive reserve-related network and provides insight into the neural implementation of cognitive reserve. Further, expression of this connectome could potentially be used as a direct measure of cognitive reserve, and as an outcome measure for intervention studies that seek to influence cognitive reserve. Future validation of and re-derivation of the pattern in expanded data sets by our and other groups will lead to further improved estimates of cognitive reserve in resting functional connectivity.

Reference Ability Neural Network-selective functional connectivity across the lifespan.

Previous studies have demonstrated that four latent variables, or reference abilities (RAs), can account for the majority of age-related changes in cognition: these being episodic memory, fluid reasoning, speed of processing, and vocabulary. In the current study, we focused on RA-selective functional connectivity patterns that vary with both age and behavior. We analyzed fMRI data from 287 community-dwelling adults (20-80 years) on a battery of tests relating to the four RAs (three tests per RA = 12 tests). Functional connectivity values were calculated between a pre-defined set of 264 ROIs (nodes). Across all participants, we (a) identified connections (edges) that correlated with an RA-specific indicator variable and, indexing only these edges; (b) performed linear regression analysis per edge, regressing indicator correlations (Model 1) and connectivity values (Model 2) on Age, Behavioral Performance, and the Interaction term; and (c) took the conjunction of significant edges between models. Results revealed a different subset of edges for each RA whose connectivity strength and domain-selectivity varied with age and behavior. Strikingly, the fluid reasoning RA was particularly vulnerable to the effects of age and displayed the most extensive connectivity and selectivity "footprint" for behavior. These findings indicate that different functional networks are recruited across RA, with fluid reasoning displaying a special status among them.

Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning.

OBJECTIVE: To build a model to predict cognitive status reflecting structural, functional, and white matter integrity changes in early multiple sclerosis (MS). METHODS: Based on Symbol Digit Modalities Test (SDMT) performance, 183 early MS patients were assigned "lower" or "higher" performance groups. Three-dimensional (3D)-T2, T1, diffusion weighted, and resting-state magnetic resonance imaging (MRI) data were acquired in 3T. Using Random Forest, five models were trained to classify patients into two groups based on 1-demographic/clinical, 2-lesion volume/location, 3-local/global tissue volume, 4-local/global diffusion tensor imaging, and 5-whole-brain resting-state-functional-connectivity measures. In a final model, all important features from previous models were concatenated. Area under the receiver operating characteristic curve (AUC) values were calculated to evaluate classifier performance. RESULTS: The highest AUC value (0.90) was achieved by concatenating all important features from neuroimaging models. The top 10 contributing variables included volumes of bilateral nucleus accumbens and right thalamus, mean diffusivity of left cingulum-angular bundle, and functional connectivity among hubs of seven large-scale networks. CONCLUSION: These results provide an indication of a non-random brain pattern mostly compromising areas involved in attentional processes specific to patients who perform worse in SDMT. High accuracy of the final model supports this pattern as a potential neuroimaging biomarker of subtle cognitive changes in early MS.

fMRI-guided white matter connectivity in fluid and crystallized cognitive abilities in healthy adults.

This study examined within-subject differences among three fluid abilities that decline with age: reasoning, episodic memory and processing speed, compared with vocabulary, a crystallized ability that is maintained with age. The data were obtained from the Reference Ability Neural Network (RANN) study from which 221 participants had complete behavioral data for all 12 cognitive tasks, three per ability, along with fMRI and diffusion weighted imaging data. We used fMRI task activation to guide white matter tractography, and generated mean percent signal change in the regions associated with the processing of each ability along with diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), for each cognitive ability. Qualitatively brain regions associated with vocabulary were more localized and lateralized to the left hemisphere whereas the fluid abilities were associated with brain activations that were more distributed across the brain and bilaterally situated. Using continuous age, we observed smaller correlations between MD and age for white matter tracts connecting brain regions associated with the vocabulary ability than that for the fluid abilities, suggesting that vocabulary white matter tracts were better maintained with age. Furthermore, after multiple comparisons correction and accounting for age, education, and sex, the mean percent signal change for episodic memory showed positive associations with behavioral performance. Overall, the vocabulary ability may be better maintained with age due to the more localized brain regions involved, which places smaller reliance on long distance white matter tracts for signal transduction. These results support the hypothesis that functional activation and white matter structures underlying the vocabulary ability contribute to the ability's greater resistance against aging.

Between-network Functional Connectivity Is Modified by Age and Cognitive Task Domain.

Research on the cognitive neuroscience of aging has identified myriad neurocognitive processes that are affected by the aging process, with a focus on identifying neural correlates of cognitive function in aging. This study aimed to test whether internetwork connectivity among six cognitive networks is sensitive to age-related changes in neural efficiency and cognitive functioning. A factor analytic connectivity approach was used to model network interactions during 11 cognitive tasks grouped into four primary cognitive domains: vocabulary, perceptual speed, fluid reasoning, and episodic memory. Results showed that both age and task domain were related to internetwork connectivity and that some of the connections among the networks were associated with performance on the in-scanner tasks. These findings demonstrate that internetwork connectivity among several cognitive networks is not only affected by aging and task demands but also shows a relationship with task performance. As such, future studies examining internetwork connectivity in aging should consider multiple networks and multiple task conditions to better measure dynamic patterns of network flexibility over the course of cognitive aging.

Brain biomarkers and cognition across adulthood.

Understanding the associations between brain biomarkers (BMs) and cognition across age is of paramount importance. Five hundred and sixty-two participants (19-80 years old, 16 mean years of education) were studied. Data from structural T1, diffusion tensor imaging, fluid-attenuated inversion recovery, and resting-state functional magnetic resonance imaging scans combined with a neuropsychological evaluation were used. More specifically, the measures of cortical, entorhinal, and parahippocampal thickness, hippocampal and striatal volume, default-mode network and fronto-parietal control network, fractional anisotropy (FA), and white matter hyperintensity (WMH) were assessed. z-Scores for three cognitive domains measuring episodic memory, executive function, and speed of processing were computed. Multiple linear regressions and interaction effects between each of the BMs and age on cognition were examined. Adjustments were made for age, sex, education, intracranial volume, and then, further, for general cognition and motion. BMs were significantly associated with cognition. Across the adult lifespan, slow speed was associated with low striatal volume, low FA, and high WMH burden. Poor executive function was associated with low FA, while poor memory was associated with high WMH burden. After adjustments, results were significant for the associations: speed-FA and WMH, memory-entorhinal thickness. There was also a significant interaction between hippocampal volume and age in memory. In age-stratified analyses, the most significant associations for the young group occurred between FA and executive function, WMH, and memory, while for the old group, between entorhinal thickness and speed, and WMH and speed, executive function. Unique sets of BMs can explain variation in specific cognitive domains across adulthood. Such results provide essential information about the neurobiology of aging.

Reference ability neural networks and behavioral performance across the adult life span.

To better understand the impact of aging, along with other demographic and brain health variables, on the neural networks that support different aspects of cognitive performance, we applied a brute-force search technique based on Principal Components Analysis to derive 4 corresponding spatial covariance patterns (termed Reference Ability Neural Networks -RANNs) from a large sample of participants across the age range. 255 clinically healthy, community-dwelling adults, aged 20-77, underwent fMRI while performing 12 tasks, 3 tasks for each of the following cognitive reference abilities: Episodic Memory, Reasoning, Perceptual Speed, and Vocabulary. The derived RANNs (1) showed selective activation to their specific cognitive domain and (2) correlated with behavioral performance. Quasi out-of-sample replication with Monte-Carlo 5-fold cross validation was built into our approach, and all patterns indicated their corresponding reference ability and predicted performance in held-out data to a degree significantly greater than chance level. RANN-pattern expression for Episodic Memory, Reasoning and Vocabulary were associated selectively with age, while the pattern for Perceptual Speed showed no such age-related influences. For each participant we also looked at residual activity unaccounted for by the RANN-pattern derived for the cognitive reference ability. Higher residual activity was associated with poorer brain-structural health and older age, but -apart from Vocabulary-not with cognitive performance, indicating that older participants with worse brain-structural health might recruit alternative neural resources to maintain performance levels.

A task-invariant cognitive reserve network.

The concept of cognitive reserve (CR) can explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or disease-related brain changes. Epidemiologic evidence indicates that CR helps maintain performance in the face of pathology across multiple cognitive domains. We therefore tried to identify a single, "task-invariant" CR network that is active during the performance of many disparate tasks. In imaging data acquired from 255 individuals age 20-80 while performing 12 different cognitive tasks, we used an iterative approach to derive a multivariate network that was expressed during the performance of all tasks, and whose degree of expression correlated with IQ, a proxy for CR. When applied to held out data or forward applied to fMRI data from an entirely different activation task, network expression correlated with IQ. Expression of the CR pattern accounted for additional variance in fluid reasoning performance over and above the influence of cortical thickness, and also moderated between cortical thickness and reasoning performance, consistent with the behavior of a CR network. The identification of a task-invariant CR network supports the idea that life experiences may result in brain processing differences that might provide reserve against age- or disease-related changes across multiple tasks.

Dynamic Patterns of Brain Structure-Behavior Correlation Across the Lifespan.

Although the brain/behavior correlation is one of the premises of cognitive neuroscience, there is still no consensus about the relationship between brain measures and cognitive function, and only little is known about the effect of age on this relationship. We investigated the age-associated variations on the spatial patterns of cortical thickness correlates of four cognitive domains. We showed that the spatial distribution of the cortical thickness correlates of each cognitive domain is distinctive and depicts varying age-association differences across the adult lifespan. Specifically, the present study provides evidence that distinct cognitive domains are associated with unique structural patterns in three adulthood periods: Early, middle, and late adulthood. These findings suggest a dynamic interaction between multiple neural substrates supporting each cognitive domain across the adult lifespan.

ß-Amyloid Deposition Is Associated with Decreased Right Prefrontal Activation during Task Switching among Cognitively Normal Elderly.

The accumulation of ß-amyloid (Aß) peptides, a pathological hallmark of Alzheimer's disease (AD), has been associated with functional alterations, often in an episodic memory system with a particular emphasis on medial temporal lobe function. The topography of Aß deposition, however, largely overlaps with frontoparietal control (FPC) regions implicated in cognitive control that has been shown to be impaired in early mild AD. To understand the neural mechanism underlying early changes in cognitive control with AD, we examined the impact of Aß deposition on task-evoked FPC activation using functional magnetic resonance imaging (fMRI) in humans. Forty-three young and 62 cognitively normal older adults underwent an fMRI session during an executive contextual task in which task difficulty varied: single (either letter case or vowel/consonant judgment task) vs dual (switching between letter case and vowel/consonant decisions) task. Older subjects additionally completed (18)F-florbetaben positron emission tomography scans and were classified as either amyloid positive (Aß+) or negative (Aß-). Consistent with previous reports, age-related increases in brain activity were found in FPC regions commonly identified across groups. For both task conditions, Aß-related increases in brain activity were found compared with baseline activity. For higher cognitive control load, however, Aß+ elderly showed reduced task-switching activation in the right inferior frontal cortex. Our findings suggest that with Aß deposition, brain activation in the cognitive control region reaches a maximum with lower control demand and decreases with higher control demand, which may underlie early impairment in cognitive control with AD progression. SIGNIFICANCE STATEMENT: The accumulation of ß-amyloid (Aß) peptides, a pathological hallmark of Alzheimer's disease, spatially overlaps with frontoparietal control (FPC) regions implicated in cognitive control, but the impact of Aß deposition on FPC regions is largely unknown. Using functional magnetic resonance imaging with a task-switching task, we found Aß-related increases in FPC regions compared with baseline activity. For higher cognitive control load, however, Aß-related hypoactivity was found in the right inferior frontal cortex, a region highly implicated in cognitive control. The findings suggest that with Aß deposition, task-related brain activity may reach a plateau early and undergo downstream pathways of neural dysfunction, which may relate to the early impairment of cognitive control seen in the progression of Aß pathology.

Cognitive neuroscience neuroimaging repository for the adult lifespan.

With recent advances in neuroimaging technology, it is now possible to image human brain function in vivo, which revolutionized the cognitive neuroscience field. However, like any other newly developed technique, the acquisition of neuroimaging data is costly and logistically challenging. Furthermore, studying human cognition requires acquiring a large amount of neuroimaging data, which might not be feasible to do by every researcher in the field. Here, we describe our group's efforts to acquire one of the largest neuroimaging datasets that aims to investigate the neural substrates of age-related cognitive decline, which will be made available to share with other investigators. Our neuroimaging repository includes up to 14 different functional images for more than 486 subjects across the entire adult lifespan in addition to their 3 structural images. Currently, data from 234 participants have been acquired, including all 14 functional and 3 structural images, which is planned to increased to 375 participants in the next few years. A complete battery of neuropsychological tests was also administered to all participants. The neuroimaging and accompanying psychometric data will be available through an online and easy-to-use data sharing website.

Metabolic connectivity: methods and applications.

PURPOSE OF REVIEW: Metabolic connectivity modelling aims to detect functionally interacting brain regions based on PET recordings with the glucose analogue [F]fluorodeoxyglucose (FDG). Here, we outline the most popular metabolic connectivity methods and summarize recent applications in clinical and basic neuroscience. RECENT FINDINGS: Metabolic connectivity is modelled by various methods including a seed correlation, sparse inverse covariance estimation, independent component analysis and graph theory. Given its multivariate nature, metabolic connectivity possess added value relative to conventional univariate analyses of FDG-PET data. As such, metabolic connectivity provides valuable insights into pathophysiology and diagnosis of dementing, movement disorders, and epilepsy. Metabolic connectivity can also identify resting state networks resembling patterns of functional connectivity as derived from functional MRI data. SUMMARY: Metabolic connectivity is a valuable concept in the fast-developing field of brain connectivity, at least as reasonable as functional connectivity of functional MRI. So far, the value of metabolic connectivity is best established in neurodegenerative disorders, but studies in other brain diseases as well as in the healthy state are emerging. Growing evidence indicates that metabolic connectivity may serve a marker of normal and pathological cognitive function. A relationship of metabolic connectivity with structural and functional connectivity is yet to be established.