Making Sense of Connectivity

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.

Individual differences in conditioned pain modulation are associated with functional connectivity within the descending antinociceptive pathway

ABSTRACT

The perception of pain and ability to cope with it varies widely amongst people, which in part could be due to the presence of inhibitory (antinociceptive) or facilitatory (pronociceptive) effects in conditioned pain modulation (CPM). This study examined whether individual differences in CPM reflect functional connectivity (FC) strengths within nodes of the descending antinociceptive pathway (DAP). A heat-based CPM paradigm and resting-state functional magnetic resonance imaging (rs-fMRI) were used to test the hypothesis that an individual's capacity to exhibit inhibitory CPM (changes in test stimuli [TS] pain due to a conditioning stimulus [CS]) reflects FC of the subgenual anterior cingulate cortex (sgACC), periaqueductal gray (PAG), and rostral ventromedial medulla (RVM). A total of 151 healthy participants (72 men, 79 women) underwent CPM testing and rs-fMRI. Three types of CPM were identified based on the effect of the CS on TS pain: (1) Antinociception: CS reduced TS pain in 45% of participants, (2) No-CPM: CS did not change TS pain in 15% of participants, and (3) Pronociception: CS increased TS pain in 40% of participants. Only the Antinociceptive subgroup exhibited FC between the left sgACC and PAG, right sgACC and PAG, and RVM and PAG. Furthermore, only the Antinociceptive subgroup exhibited a correlation of both left and right sgACC-RVM FC (medium effect sizes) with CPM effect magnitude. Women, compared with men were more likely to be categorized as pronociceptive. These data support the proposition that FC of the DAP reflects or contributes to inhibitory CPM.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

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.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Move Your Body, Boost Your Brain: The Positive Impact of Physical Activity on Cognition across All Age Groups

While the physical improvements from exercise have been well documented over the years, the impact of physical activity on mental health has recently become an object of interest. Physical exercise improves cognition, particularly attention, memory, and executive functions. However, the mechanisms underlying these effects have yet to be fully understood. Consequently, we conducted a narrative literature review concerning the association between acute and chronic physical activity and cognition to provide an overview of exercise-induced benefits during the lifetime of a person. Most previous papers mainly reported exercise-related greater expression of neurotransmitter and neurotrophic factors. Recently, structural and functional magnetic resonance imaging techniques allowed for the detection of increased grey matter volumes for specific brain regions and substantial modifications in the default mode, frontoparietal, and dorsal attention networks following exercise. Here, we highlighted that physical activity induced significant changes in functional brain activation and cognitive performance in every age group and could counteract psychological disorders and neural decline. No particular age group gained better benefits from exercise, and a specific exercise type could generate better cognitive improvements for a selected target subject. Further research should develop appropriate intervention programs concerning age and comorbidity to achieve the most significant cognitive outcomes.

Whole-body metabolic connectivity framework with functional PET

The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk. Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow. The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys. The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states.

Brain connectomics: time for a molecular imaging perspective?

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Effects of bilateral sequential theta-burst stimulation on functional connectivity in treatment-resistant depression: First results

BACKGROUND

Previous studies suggest that transcranial magnetic stimulation exerts antidepressant effects by altering functional connectivity (FC). However, knowledge about this mechanism is still limited. Here, we aimed to investigate the effect of bilateral sequential theta-burst stimulation (TBS) on FC in treatment-resistant depression (TRD) in a sham-controlled longitudinal study.

METHODS

TRD patients (n = 20) underwent a three-week treatment of intermittent TBS of the left and continuous TBS of the right dorsolateral prefrontal cortex (DLPFC). Upon this trial's premature termination, 15 patients had received active TBS and five patients sham stimulation. Resting-state functional magnetic resonance imaging was performed at baseline and after treatment. FC (left and right DLPFC) was estimated for each participant, followed by group statistics (t-tests). Furthermore, depression scores were analyzed (linear mixed models analysis) and tested for correlation with FC.

RESULTS

Both groups exhibited reductions of depression scores, however, there was no significant main effect of group, or group and time. Anticorrelations between DLPFC and the subgenual cingulate cortex (sgACC) were observed for baseline FC, corresponding to changes in depression severity. Treatment did not significantly change DLPFC-sgACC connectivity, but significantly reduced FC between the left stimulation target and bilateral anterior insula.

CONCLUSIONS

Our data is compatible with previous reports on the relevance of anticorrelation between DLPFC and sgACC for treatment success. Furthermore, FC changes between left DLPFC and bilateral anterior insula highlight the effect of TBS on the salience network.

LIMITATIONS

Due to the limited sample size, results should be interpreted with caution and are of exploratory nature.

Increased left dorsolateral prefrontal cortex density following escitalopram intake during relearning: a randomized, placebo-controlled trial in healthy humans

Background

Serotonergic agents affect brain plasticity and reverse stress-induced dendritic atrophy in key fronto-limbic brain areas associated with learning and memory.

Objectives

The aim of this study was to investigate effects of the antidepressant escitalopram on gray matter during relearning in healthy individuals to inform a model for depression and the neurobiological processes of recovery.

Design

Randomized double blind placebo control, monocenter study.

Methods

In all, 76 (44 females) healthy individuals performed daily an associative learning task with emotional or non-emotional content over a 3-week period. This was followed by a 3-week relearning period (randomly shuffled association within the content group) with concurrent daily selective serotonin reuptake inhibitor (i.e., 10 mg escitalopram) or placebo intake.

Results

Via voxel-based morphometry and only in individuals that developed sufficient escitalopram blood levels over the 21-day relearing period, an increased density of the left dorsolateral prefrontal cortex was found. When investigating whether there was an interaction between relearning and drug intervention for all participants, regardless of escitalopram levels, no changes in gray matter were detected with either surfaced-based or voxel-based morphometry analyses.

Conclusion

The left dorsolateral prefrontal cortex affects executive function and emotional processing, and is a critical mediator of symptoms and treatment outcomes of depression. In line, the findings suggest that escitalopram facilitates neuroplastic processes in this region if blood levels are sufficient. Contrary to our hypothesis, an effect of escitalopram on brain structure that is dependent of relearning content was not detected. However, this may have been a consequence of the intensity and duration of the interventions.

Registration

ClinicalTrials.gov Identifier: NCT02753738; Trial Name: Enhancement of learning associated neural plasticity by Selective Serotonin Reuptake Inhibitors; URL: https://clinicaltrials.gov/ct2/show/NCT02753738.

Graph theory approach for the structural-functional brain connectome of depression

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

Static versus Functional PET: Making Sense of Metabolic Connectivity

Recently, Jamadar et al. (2021, Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 31(6), 2855-2867) compared the patterns of brain connectivity or covariance as obtained from 3 neuroimaging measures: 1) functional connectivity estimated from temporal correlations in the functional magnetic resonance imaging blood oxygen level-dependent signal, metabolic connectivity estimated, 2) from temporal correlations in 16-s frames of dynamic [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), which they designate as functional FDG-PET (fPET), and 3) from intersubject correlations in static FDG-PET images (sPET). Here, we discuss a number of fundamental issues raised by the Jamadar study. These include the choice of terminology, the interpretation of cross-modal findings, the issue of group- to single-subject level inferences, and the meaning of metabolic connectivity as a biomarker. We applaud the methodological approach taken by the authors, but wish to present an alternative perspective on their findings. In particular, we argue that sPET and fPET can both provide valuable information about brain connectivity. Certainly, resolving this conundrum calls for further experimental and theoretical efforts to advance the developing framework of PET-based brain connectivity indices.

Practical recommendations for the management of treatment-resistant depression with esketamine nasal spray therapy: Basic science, evidence-based knowledge and expert guidance

OBJECTIVES

Despite the available therapies for treatment-resistant depression (TRD), there are a limited number that are evidence-based and effective in this hard-to-treat population. Esketamine nasal spray, an intranasal N-methyl-d-aspartate (NMDA) glutamate receptor antagonist, is a novel, fast-acting option in this patient population. This manuscript provides expert guidance on the practicalities of using esketamine nasal spray.

METHODS

A group of six European experts in major depressive disorder (MDD) and TRD, with clinical experience of treating patients with esketamine nasal spray, first generated practical recommendations, before editing and voting on these to develop consensus statements during an online meeting.

RESULTS

The final consensus statements encompass not only pre-treatment considerations for patients with TRD, but also specific guidelines for clinicians to consider during and post-administration of esketamine nasal spray.

CONCLUSIONS

Esketamine nasal spray is a novel, fast-acting agent that provides an additional treatment option for patients with TRD who have previously failed several therapies. The guidance here is based on the authors' experience and the available literature; however, further real-world use of esketamine nasal spray will add to existing knowledge. The recommendations offer practical guidance to clinicians who are unfamiliar with esketamine nasal spray.

Evaluating global brain connectivity as an imaging marker for depression: influence of preprocessing strategies and placebo-controlled ketamine treatment

Major depressive disorder (MDD) is associated with altered global brain connectivity (GBC), as assessed via resting-state functional magnetic resonance imaging (rsfMRI). Previous studies found that antidepressant treatment with ketamine normalized aberrant GBC changes in the prefrontal and cingulate cortices, warranting further investigations of GBC as a putative imaging marker. These results were obtained via global signal regression (GSR). This study is an independent replication of that analysis using a separate dataset. GBC was analyzed in 28 individuals with MDD and 22 healthy controls (HCs) at baseline, post-placebo, and post-ketamine. To investigate the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)/cerebrospinal fluid (CSF) signals only (BASE); (2) WM/CSF + GSR (GSR); and (3) WM/CSF + physiological parameter regression (PHYSIO). Reduced GBC was observed in individuals with MDD only at baseline in the anterior and medial cingulate cortices, as well as in the prefrontal cortex only after regressing the global signal. Ketamine had no effect compared to baseline or placebo in either group in any pipeline. PHYSIO did not resemble GBC preprocessed with GSR. These results concur with several studies that used GSR to study GBC. Further investigations are warranted into disease-specific components of global fMRI signals that may drive these results and of GBCr as a potential imaging marker in MDD.

Update on PET imaging biomarkers in the diagnosis of neuropsychiatric disorders

PURPOSE OF REVIEW

To give an update on recent imaging studies probing positron emission tomography (PET) as a tool for improving biomarker-guided diagnosis of neuropsychiatric disorders.

RECENT FINDINGS

Several studies confirmed the value of imaging of regional neuronal activity and imaging of dopaminergic, serotonergic, and other neuroreceptor function in the diagnostic process of neuropsychiatric disorders, particularly schizophrenia, depression/bipolar disorder, substance use disorders, obsessive compulsive disorders (OCD), and attention-deficit/hyperactivity disorder. Additionally, imaging brain microglial activation using translocator protein 18 kDa (TSPO) radiotracer allows for unique in-vivo insights into pathophysiological neuroinflammatory changes underlying schizophrenia, affective disorders, and OCD.

SUMMARY

The role of PET imaging in the biomarker-guided diagnostic process of neuropsychiatric disorders has been increasingly acknowledged in recent years. Future prospective studies are needed to define the value of PET imaging for diagnosis, treatment decisions, and prognosis in neuropsychiatric disorders.

Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions

Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro- and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies.

We can estimate the higher order organization of cortical gray matter as a connectome using structural MRI. However, this methodology, termed morphometric similarity, requires multiple advanced neuroimaging protocols that are unsuitable, unavailable, or intolerable to certain populations, including children and some clinical groups. In a large, homogenous dataset of healthy young adults, we estimated these connectomes using three different feature sets, each extracted from fewer MRI sequences. Even when produced using only T1-weighted structural MRI scans, these connectomes were broadly similar to those produced with more complex or numerous MRI sequences. We did not replicate previous findings linking variation in the morphometric similarity networks (MSNs) to individual differences in cognitive abilities. We highlight potential reasons for this, including the developmental stage of the young adult imaging cohort in which our hypotheses were tested, and conclude that this study provides putative evidence that, in those populations where advanced imaging is not plausible, MSNs generated from T1-weighted structural MRIs are a promising alternative.

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

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