Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma–neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience. et al. review the burgeoning field of cancer neuroscience as studied using human brain mapping. They outline key historical developments in functional neuroimaging and glioma biology, respectively, highlighting several areas where the two fields can continue to be merged.Mandal   Brain, 19401‘The great practical significance of an exact knowledge of the forms of growth and extension of gliomas needs no special emphasis. For both the neurologist and the neurosurgeon this knowledge is certainly one of the most important aspects of the pathology of the gliomas. It is therefore surprising that this aspect of the problem has been rather neglected in modern research on this subject.’—Hans Scherer, 2–5 In a similar way, the Belgian neuropathologist, Hans Scherer, could not dream of providing in vivo evidence for his proposals of glioma migration along brain networks, presenting only static pathology slides to support his claims. As the ability to non-invasively map brain networks in humans reaches maturity, it is well worth revisiting the literature of modern neuroscience's predecessors to test their prescient ideas.For at least 80 years, the study of glioma origins and migration has been a topic of clinical import yet limited scientific fruit. Even today, it could be argued that this aspect of the problem has not received commensurate attention given the potential implications of a complete understanding of glioma migration patterns for clinical treatment of primary brain tumours. One reason why a systems-level understanding of glioma development has yet to be achieved is because the technologies necessary to study brain disease from a top-down approach have emerged only recently. In other areas of neurology, network-based models of disease spread and cognitive impact were proposed well before the technologies to rigorously test the relevant hypotheses existed.13 for the application of non-invasive imaging of brain structure and function in the scientific study and clinical management of diffuse gliomas.In this review, we therefore aim to introduce the reader to two emerging fields—human brain mapping and glioma biology—that can be synthesized to provide insights into the origins and development of glioma tumours. Many techniques exist in human brain mapping, including structural, diffusion and functional MRI (fMRI) as well as electroencephalography (EEG), magnetoencephalography (MEG) and electrocorticography. Each of these techniques has produced relevant and important data regarding glioma pathophysiology. The prior articles  review t14 reports of temporally correlated, blood–oxygen level dependent (BOLD) signals within the motor cortex. At this point in history, the BOLD signal was widely interpreted within the context of task-induced elevations, topographically organized upon brain tissue relevant for performing a given task; it was not yet clear how to interpret covariance in this signal across brain regions during the resting state (i.e. in the absence of a task). At first, the nature of these correlations was suspected to be artefactual by many of Biswal's colleagues15, with one senior investigator complaining that his confusing results were an ill consequence of allowing graduate students to design experiments. However, through subsequent, carefully designed studies, Biswal ruled out confounding vascular, head motion and respiratory signals as primary drivers of these spatiotemporal BOLD correlations, and the community grew increasingly more accepting of the view that these patterns could reflect neuronal connectivity. The pattern of BOLD correlations identified by Biswal came to be known as the ‘sensorimotor network’ and is displayed in Non-invasive mapping of human brain networks was first described in 1995 with Biswal and colleagues’18 However, instead of interpreting this constellation of regions as ‘deactivated’ during attention-demanding cognitive tasks, Raichle and colleagues19 proposed that they could instead represent a network of regions that were ‘activated’ in the resting state, reflecting an introspective, ‘default’ mode. A few years later, Michael Greicius and colleagues20 produced the first map of the DMN derived from a resting-state functional connectivity analysis by applying the seed-based approach introduced by Biswal to identify the network of brain regions functionally correlated with the posterior cingulate cortex (The discovery of the default mode network (DMN) represents the next major milestone in the conceptual development of resting-state functional connectivity. The DMN had first been described as a ‘task-negative’ network of association cortical regions that appeared deactivated in experimental functional imaging studies whenever a task condition was contrasted with the resting state.e cortex . Current21 However, the tight dependence on seed location is also a limitation of the seed-based approach, since slight differences in the registration of a seed between subjects can result in the identification of distinct networks.22 A second approach utilizes a source-separation, dimensionality reduction technique called independent component analysis (ICA) to decompose fMRI data into components of signal and noise.23–25 ICA is used both to denoise data and identify resting-state functional networks, which naturally manifest as the signal components from the analysis  data from 1000 college-aged individuals, as well as sophisticated surface-based registration techniques, Yeo et al. identified two optimal clustering solutions which decomposed the cerebral cortex into a fixed set of parsimonious networks: a 7-network solution and a 17-network solution. Most subsequent investigators have focused on the seven-network solution for its simplicity and useful insights into brain organization  through white matter tracts because of the hydrophobic composition of myelin sheaths. Thus, diffusion MRI can provide insight into the locations of large-scale white matter pathways by tracking the movement of water molecules.Diffusion tractography is a method to visualize white matter tracts in the brain from diffusion MRI sequences.et al.31 in 1994 and reflects one of the first attempts to visualize white matter pathways using diffusion MRI data. The application of the diffusion tensor allows the researcher to model the shape (i.e. direction) of water diffusion in such a way that can track the complex geometry of white matter pathways in the brain.30 A diffusion probability density function can be computed for each voxel, which is exploited to trace the trajectory of a white matter tract.32Diffusion tensor imaging was developed by Basser 32 Probabilistic tractography samples a large number of different possible pathways from a starting seed point based on the diffusion probability density function at each voxel.33 Each approach has its own advantages and disadvantages. Probabilistic tractography is more resource-intensive given the large number of simulated pathways, but it also more comprehensively covers each possible structural connection to a given seed location.32 An array of canonical white matter pathways that can be mapped by probabilistic tractography are shown in 34Two approaches to diffusion tractography exist: ‘deterministic’ and ‘probabilistic’ tractography. Deterministic tractography attempts aims to delineate a white matter tract by starting at a seed point, then following a single direction at each voxel to track the pathway throughout the brain.36 Although prior studies have highlighted a suboptimal false positive rate in diffusion tractography, advances in image acquisition like multishell diffusion imaging allow for more accurate results.37 It is also worth noting that different research teams can derive slightly different white matter bundles from the same diffusion-weighted imaging dataset, simply because different groups have different protocols and processing pipelines.38 Some groups have published high-quality, standardized workflows with openly available software to address this problem.34 The results of diffusion tractography can also be compared to human neuroanatomical atlases derived from post-mortem brain tissue to address any ambiguity.39 Overall, diffusion tractography reflects an extremely useful technique for mapping in vivo structural connectivity, with numerous potential clinical applications, including in the resection of gliomas (which often live in the white matter).Diffusion tensor imaging was improved upon in the 2000s with the development of techniques like diffusion spectrum imaging and high angular resolution diffusion imaging, which address the fact that multiple white matter fibres often cross at a single voxel.40 and Sporns et al.41 to describe the full collection of the brain's connections. While first applied to maps of structural connectivity, soon this term was also invoked to describe the catalogue of functional connections present in any given individual's brain as elucidated by resting-state functional imaging. Although it was well-recognized that interindividual variation should exist across connectomes, a tantalizing goal of cognitive neuroscience arose to uncover the general wiring diagram of the human brain. In 2009, the National Institute of Health funded two consortiums primarily aimed at understanding functional and anatomical connectivity, respectively, in a large-scale effort called the Human Connectome Project.42 Many positive outcomes came from this project including improved image acquisition, processing and visualization techniques,44 a multimodal brain parcellation45 as well as a detailed, ‘wiring diagram’ of the healthy brain in early adulthood.46 A similarly ambitious imaging study was initiated by the UK Biobank in 2014 which mapped the healthy connectome in late adulthood.48 The quantity and quality of data describing ‘normal’ brain organization, characterized throughout the lifespan, now provide the unprecedented opportunity to better understand how the brain diverges in disease, with implications for the clinical management of brain tumours.In 2005, the term ‘connectome’ was coined simultaneously by Hagmann49 to unpacking intercellular dynamics that influence glioma growth.50 To understand brain networks, it is useful to consult other areas of mathematics and science that have developed a toolset and vocabulary to characterize networks. The branch of mathematics concerned with networks is called graph theory.52 Graph theory is the study of graphs, which are mathematical objects composed of nodes  and edges  that each connect two nodes. A graph can be thought of as the mathematical representation of a real-world network. Conceiving of networks in the abstract, as graphs, has been useful in the fields of computer science, molecular biology, physics and sociology,53–60 prompting the application of this approach to systems neuroscience.62 When graph theory is used to interpret functional networks derived from resting-state imaging, the nodes of the graph typically represent broad brain regions as defined by a chosen parcellation scheme, while the edges reflect some measure of the statistical relationship  of the BOLD signals at two nodes.63 These edges can be either weighted by the strength of the relationship or binarized by some threshold. Since the directionality of the relationship is difficult to assess in fMRI due to the limited temporal resolution, the edges in graphs based on rs-fMRI data tend to be undirected, although in network science more broadly edges can have directionality. Negatively weighted edges remain enigmatic and are often ignored. A directly analogous approach can be applied to EEG, MEG or electrocorticography, where signal power within frequency bands that relate to neural firing can be correlated across distinct nodes.64Brain network analysis has several potential applications in neuro-oncology, from guiding tumour resections63 Network statistics that assess each property are described in 65 while metrics like modularity assess the intraconnectivity of larger clusters of nodes compared to the connectivity between nodes in different modules.66Within the context of brain networks, there are three general properties of interest: integration, segregation and centrality.67 This measure is closely related to ‘local efficiency’, which quantifies the importance of a node to the network's global efficiency.68 Finally, the centrality of a node can be assessed by their intermodular connectivity, through metrics like ‘participation coefficient’.66 Nodes that help connect different modules together are called ‘connectors’, whereas nodes that are highly connected within their own module are described as ‘provincial’.69 All three classes of centrality measures described above are used to define hubs, or especially well-connected nodes, of a network, which are of broad importance in both healthy brain function and pathological dysfunction.Finally, ‘centrality’ refers broadly to the importance of a given node to the network, where ‘importance’ can be defined in several ways. The most straightforward measure of centrality is ‘nodal degree’ which the total number of connections a node makes with other nodes. A related measure is ‘nodal strength’, which applies to weighted graphs and refers to the sum of weights involving a given node. Another important metric of centrality is ‘betweenness centrality’, which refers to the fraction of all shortest paths that involve a given node.65 Small-worldness is defined as some ratio between the integration and segregation of a network. A network with an optimal balance of both is one where each node has a relatively short path to every other node, while organization into clusters or modules is still maintained.70 Based on simulations comparing empirical brain networks to surrogate networks with preserved distributional properties yet randomized topology, networks describing nervous systems across many species, ranging from the Caenorhabditis elegans neuronal connectome to the human resting-state functional connectome, have been observed to possess significant small-worldness.71Among the first developments in network neuroscience was the discovery of small world brain networks.72 These long-distance connections are responsible for the small-worldness of brain networks, as they allow for efficient communication between distant brain regions.62 To support long-distance communication, small-world networks will typically also possess hubs, which mediate the paths between nodes in their local neighbourhood and far-away modules.73What does small-worldness mean for brain organization? In spatially embedded networks, such as the brain, high levels of integration are essentially always expected. Nodes that are close to one another in space tend to be connected, given the low physiological cost of forming connections with close-by nodes, resulting in high levels of clustering. More interestingly, brain networks tend to also possess long-distance connections, which often involve multiple modules.74 By definition, hubs form many connections, which require constant grooming throughout development dedicated to axon myelination and synaptogenesis. In addition, hubs also form long-distance connections, which are especially physiologically expensive to maintain, since proteins in the presynaptic terminal are manufactured in the cell body, and thus must travel the full distance of the axon to reach their target. Considering this energy constraint, some authors proposed that brain networks have been sculpted over the course of evolution to meet an optimal balance between maximizing topological efficiency and minimizing wiring costs.75 Hubs are essential for maintaining this trade-off, yet because of their disproportionate metabolic demand, these regions are vulnerable to neurological and psychiatric disease .While hubs support efficient information transfer in spatially embedded networks, they come at a cost.76 As such, structural and functional networks are reasonably well-correlated, with both types of networks bearing similar characteristic features like small-worldness and hub organization.78 Nevertheless, there are important differences between structural and functional networks since functional connectivity is more dynamic and captures higher-order interactions mediated by multisynaptic connections.78 Many pathologies affect both structural and functional connectivity, so it is important to bear in mind that the two are closely interrelated.A major question of interest is how functional and structural brain network topology relate to one another. Functional connections are facilitated by an underlying structural network that mediates communication between distant neuronal populations.Resting-state functional connectivity studies have furthered our basic understanding of human brain function, advancing a more nuanced perspective compared to the localizationist view that preceded this approach. How has this technique improved our understanding of brain disorders? Translational research in network neuroscience can be understood as comprising two main arms targeted at clarifying the pathophysiology of neuropsychiatric disease as well as improving treatment planning for surgical and stimulation interventions.2 and Geschwind3 composed brain network models to explain the cognitive deficits associated with neuropsychiatric disorders such as aphasia and schizophrenia. Current techniques to map brain networks in vivo provide the first opportunity to experimentally examine the relationship between connectivity and disease.Despite the prominence of focal lesion studies throughout the history of neurology, the notion that brain diseases cause symptoms by affecting large-scale networks is quite old. Both Wernicke79 used PET imaging to localize amyloid beta, one of the main pathogenic proteins in Alzheimer's disease, and found that the biomarker co-localized with nodes of the DMN, most prominently the posterior cingulate cortex. This work was extended by Seeley and colleagues80 in 2009, who demonstrated that brain atrophy in a wide range of neurodegenerative conditions localized to large-scale brain networks.One of the first seminal studies in ‘pathoconnectomics’ linked the spatial patterning of neurodegeneration in Alzheimer's disease to the DMN. Buckner and colleagues81 demonstrated that the link between disease and connectivity extends even further. Using a large database summarizing the results from 392 of published voxel-based morphometry studies, they found that the brain regions which tend to be atrophied in a litany of neurological disorders co-localize with the hubs of the structural connectome. Although many conditions implicate divergent regions of the brain (e.g. Alzheimer's disease and schizophrenia), each condition still tends to affect regions around a brain hub. Crossley and colleagues also reported that this phenomenon generalized to functional network hubs, as the functional connectome shared many of the same topological properties of their structural connectome.Finally, a seminal study from Crossley and colleagues75 Intense metabolic demand has been proposed to contribute to the vulnerability of upper motor neurons to degeneration in amyotrophic lateral sclerosis.82 In a similar way, the propensity for neurological diseases to implicate hubs could be a consequence of the economy of brain network organization, in which the hubs are left vulnerable to metabolic disruption.75What insights into brain pathology do these findings provide? Two non-mutually exclusive hypotheses have been proposed to explain the link between brain disease and connectivity. First, as discussed previously, brain network hubs require a disproportionate amount of energy to maintain, which could leave these areas vulnerable to disease.83–87 Some groups have shown, both in animal models and in patients, that longitudinal brain atrophy can be predicted with moderate accuracy using network-based models of the underlying connectome.88–92 The network spread of brain pathology could also explain the propensity of these pathologies to affect hubs, given that the hubs are, by definition, central to the network.63A second possibility, which may only apply to a subset of neurological diseases, is that the pathology could spread along brain networks. Prion-like transneuronal degeneration has been demonstrated in animal models of neurodegenerative conditions like frontotemporal dementia, Parkinson's disease and Alzheimer's disease.93 These surgeries are often performed on pathological brain tissue surrounding cortical regions crucial for cognition. Accurately mapping and therefore avoiding eloquent tissue is thus an important step in neurosurgical treatment to limit the operation's impact on the patient's quality of life.49 Brain mapping can be performed during the operation through direct electrical stimulation to identify regions important for cognitive processes such as language processing or motor planning.94–96 However, direct electrical stimulation lengthens the procedure, increasing risk for complications, and cannot be easily used to probe executive functions.97At the time of writing, the only standard clinical use of fMRI is in presurgical planning for the treatment of epilepsy and brain tumours.98 As the goal of glioma resection is to maximize survival while minimizing cognitive deficit, fMRI is a potentially valuable tool to identify and preserve neural tissue crucial for cognition. However, task-based fMRI can only identify brain networks important for higher-order cognitive functions by employing complex tasks, which are both time-consuming and difficult for patients to perform in the scanner.29 Several groups have evaluated the feasibility of resting-state functional imaging for presurgical brain mapping.99 Despite issues surrounding mass effect and neurovascular coupling, these groups have shown that the canonical resting-state networks can be identified in patients with brain tumours, highlighting the feasibility of clinical translation for resting-state network mapping. In 101 though its main use in neurosurgery is in guiding the removal of epileptogenic brain tissue.Task-based fMRI is used in many clinics to map eloquent brain regions before neurosurgical operations.103 Since gliomas are tumours originating from glial cells, they often surround white matter tracts crucial for healthy brain function, such as the corticospinal tract or arcuate fasciculus. While peritumoural oedema can complicate diffusion tractography around diffuse gliomas, some groups have developed novel correction algorithms to address this problem.104 Presurgical mapping of white matter pathways using diffusion tractography can be an extremely useful tool in planning glioma resections.However, it should also be noted that current functional imaging approaches do not consistently identify eloquent regions and may be less relevant in certain circumstances compared to the connectional anatomy revealed by diffusion tractography.Connectomics has largely been applied to neurosurgery through its potential to preoperatively map eloquent brain tissue. In this way, network neuroimaging helps neurosurgeons navigate the dilemma between maximizing the resection of malignant tissue, to extend patient survival, while minimizing the impact on cortical regions crucial for cognition. While connectomics has clear relevance for the latter goal, an opportunity also exists to apply it to the former, specifically in the case of neurosurgeries to remove glioma tumours.105—therefore, our growing understanding of brain network organization could prove invaluable for developing novel therapeutic strategies to hinder glioma progression. The next sections review historical developments and current concepts in glioma biology, outlining ways in which brain network mapping can advance this clinically important area of research.Gliomas are tumours originating from glial cells and represent the majority of deadly brain cancers. These tumours are difficult to treat because of their capacity to spread along brain networks107 Virchow's108 primary contribution to science was the development of cell theory, which held as a basic tenet that all cells, including diseased cells, are daughters of other cells. Based on this tenet, he was an early proponent of the now-accepted idea that cancers originate from otherwise normal cells, and so he named gliomas for their presumed glial cell origins. Virchow was also the first to classify gliomas, distinguishing between low-grade tumours of reduced cellular density and high-grade tumours with marked contrast to healthy tissue.The term ‘glioma’ was coined in the mid-19th century by the father of modern pathology, Dr Rudolf Virchow, who invoked the new terminology to distinguish between healthy and diseased, neoplastic brain tissue.109 seminal 1926 study of over 400 gliomas. Bailey and Cushing were the first to systematically correlate histological and anatomical properties of gliomas with patient outcomes, developing a four-tiered grading system for gliomas that continues to be used in the 21st century.110 In their classification, gliomas are classified according to the healthy glial cells they most resemble, leading to glioma subtypes such as ‘astrocytoma’ and ‘oligodendroglioma’. The most lethal and aggressive type of brain tumour was called spongioblastoma multiforme, a term coined to reflect the variegated appearance of cells observed in these tumours, as well as Bailey and Cushing's belief that these tumours had a different cell-of-origin than the healthy glial cells from which gliomas originate.111 The term was amended to glioblastoma multiforme (GBM) after the common cellular origin of astrocytomas and GBMs was established.112Glioma classification became more sophisticated in the beginning of the 20th century with Bailey and Cushing's113, a world-renowned surgeon who popularized the use of radically extensive surgeries to eliminate cancer. Unfortunately, radical surgical strategies were later proven to be of little therapeutic value for most cancers and instead decreased quality of life for cancer patients.114 Radical surgeries are of limited potential because of metastasis. Malignant tumours can spread to different organ systems, rendering futile the extensive targeting of the cancer-originating organ, if the cancer is not caught early.115 Although primary brain tumours rarely metastasize outside of the brain, adult gliomas are nevertheless diffuse and resistant to surgical treatment.105 Even radical hemispherectomies fail to totally eliminate glioma cells, as the cancer can spread along commissural fibres to infiltrate the opposite hemisphere of the brain.116 Following the development of surgical techniques and imaging adjuncts to help minimize morbidity, extensive data now support first-line maximal resection whenever possible and subsequent chemotherapy.117 Nevertheless, these approaches are not curative for diffuse gliomas because of their infiltration throughout the brain. The clear clinical relevance of glioma cell migration therefore motivated researchers like the Belgian neuropathologist Hans Scherer to study this phenomenon, which had been rather neglected until the 1930s.Harvey Cushing was a disciple of William Halsted118 sought to understand the developmental rules that guide the extension of glial neoplasms.1 From observations of the interactions between gliomas and their neural microenvironment, he introduced a distinction between ‘primary structures’—the parts of the glioma that manifest independently of surrounding brain tissue—and ‘secondary structures’, which depend on pre-existing tissue elements for their growth and survival. Secondary structures of gliomas, which are now called Scherer structures,119 can be classified into four categories based on the pre-existing tissue elements they rely upon: neurons, the subarachnoid space, blood vessels and white matter. Scherer also argued for an alternative classification system for gliomas, organizing these tumours based on their developmental patterns instead of their cytology, which he presciently recognized to be quite intratumourally variable.In contrast to Bailey and Cushing's classifications, which largely focused on the cytological characteristics of gliomas, Scherer120 Particularly, prognostic genetic alterations include the isocitrate dehydrogenase (IDH) mutation, which is associated with lower-grade tumours and improved survival, co-deletion of the 1p19q chromosomal arms, which is pathognomonic of oligodendroglioma, and MGMT promoter methylation, which predicts the therapeutic efficacy of temozolomide.120 1p19q co-deletion also predicts responsiveness to radiotherapy as oligodendrogliomas are more radiosensitive than astrocytomas.105Despite Scherer's concerns about histological characterizations of glioma, the Bailey and Cushing classification remained the primary classification system until the updated World Health Organization guidelines in 2016. The recent World Health Organization 2021 guidelines place emphasis on genomic alterations which have proven to be more prognostic and predictive of therapeutic response than tumour grade.Advances in molecular genetic technologies have spurred three main directions of glioma research targeted at better understanding the cancer's cellular heterogeneity, cell-of-origin and relationship with the neural microenvironment. In the next sections, we review the main questions and findings from these areas of research.121 While this prevalence is much lower than many other neurological conditions, such as Alzheimer's disease, the mortality rate of diffuse glioma is very high. Glioblastoma, the most lethal glioma subtype which accounts for over 70% of diagnoses, has a median overall survival of 15–17 months.122 While median survival of glioblastomas has improved since the current standard of care of temozolomide and radiation was established, these improvements have been modest, as today's patients are expected to live only a couple months longer than the GBM patients from decades ago.123 Lower-grade gliomas have a much more favourable prognosis, but they tend to affect younger patients and can convert to glioblastoma; thus, these tumours are also regarded as a research priority.124 Overall, the poor survival rate and lack of therapeutic breakthroughs in the clinical management of diffuse gliomas has motivated the application of a litany of cutting-edge genomic and cellular technologies to better understand this disorder, as will be discussed in the next sections.Before outlining current research in neuro-oncology, we will briefly discuss the epidemiology of glioma to help convey why this condition has attracted substantial attention from biomedical researchers and funding bodies. Approximately 100 000 individuals are diagnosed with diffuse glioma each year.In addition to the development of clinically useful genomic markers, the last decade of research has borne witness to improved RNA sequencing technologies, which have characterized the vast heterogeneity of malignant cancers. This heterogeneity exists not just between individuals with the same cancer type, but even within an individual's tumour, as was recognized by Hans Scherer to be the case for glioblastomas. Tumour heterogeneity poses a major challenge to therapeutic development, contributing to the limited efficacy of most pharmacological treatments for primary brain tumours.120 The most important prognostic alteration is the IDH mutation, which predicts a less aggressive tumour and is a diagnostic criterion for diffuse low-grade gliomas.125 When it occurs, the IDH mutation is an early oncogenic event which results in a hypermethylated phenotype.126 Among IDH-mutated, low-grade gliomas, the next important alteration is the co-deletion of the 1p19q chromosomal arms. IDH-mutated gliomas that feature this co-deletion possess the morphological characteristics that define oligodendrogliomas, whereas those that lack the co-deletion are classified as astrocytomas  population in glioma is strongly supportive of the cancer stem cell hypothesis, which maintains that a relatively small subset of stem-like cells in a given tumour is the main driver of cancer cell proliferation.129–131 The BTSCs have emerged as a popular treatment target, given their putative role in driving glioma progression.IDH-mutated gliomas typically consist of a dominant cell type that is either astrocyte-like or oligodendrocyte-like, yet the tumours will commonly contain a substantial portion of both cell types.133 However, it was soon recognized that bulk RNA sequencing results generally recapitulate the transcriptomic profile of the most dominant cell type in a given sample, thus obscuring the diversity of cell types existing in the tumour.135 Single-cell RNA sequencing studies have since discovered that each of the canonical transcriptional subtypes can be observed in the same GBM tumour.134Robust intertumoural classification of IDH-wildtype glioblastomas has proven much more challenging. In the early 2010s, many attempts were made to classify GBMs by their bulk transcriptomic features, which eventually settled on three subtypes—proneural, mesenchymal and classical.136 The largest axis of variation within these cell types can be viewed as a spectrum between a neurodevelopmental state  and an immunological state .136 This cellular heterogeneity contributes to the resistance of GBM tumours to treatments targeted at any single cell state. Responding to this research, new therapeutic strategies in development seek to convert GBM cells to a state that is more responsive to treatment (such as an astrocyte-like state) before delivering therapy.130Currently, the consensus view is that glioblastoma cells can reside in any of four cell states—astrocyte-like (AC-like), neural progenitor cell-like (NPC-like), oligodendrocyte precursor cell (OPC)-like and mesenchymal—with some flexibility to transition between states .130,136 138 Multiple GBM cell types, including the mesenchymal state, can initiate tumours when transplanted into a model organism.118 Interestingly, even if an isolated cell type is xenografted into a different organism, the resulting tumour displays a heterogeneous distribution of cell states, implying that each GBM cell type is capable of transforming into any other GBM cell type.136 Therefore, the original cancer stem cell hypothesis does not perfectly describe GBM, since each GBM cell appears capable of acting like a stem cell.130 Nevertheless, the tremendous plasticity of GBM cells has implications for theories regarding the cell-of-origin of the cancer, given that the tumour-initiating cell should, in health, share many characteristics of the resulting tumour.Finally, BTSCs have been identified in GBMs as well, although they are less of a distinct cell population within these highly malignant tumours.109 proposed that gliomas originate from mature astrocytes and oligodendrocytes, thereby deriving the names ‘astrocytoma’ and ‘oligodendroglioma’ in reference to the putative cells-of-origin for these tumours.106 However, the neuroscience of their day had yet to acknowledge the existence of stem and progenitor cells in the adult human brain. Currently, it is accepted that at least two types of stem-like cells exist in the adult mammalian brain: neural stem cells (NSCs) and OPCs.140 Both of these cell types have been proposed as possible cells-of-origin for glioma—the evidence supporting each possibility will be now reviewed.Given the vast cellular heterogeneity of diffuse gliomas, it is unsurprising that the search for the cell-of-origin for this cancer has proven exceedingly difficult. Bailey and Cushing140 In development, NSCs  both differentiate into neurons and glia and form the scaffold to allow newborn neurons to migrate from the ventricular zone to populate the six cortical layers.141 While traditionally, it was thought that neurogenesis ends prenatally, it is now accepted that NSCs continue to divide throughout the lifespan in the dentate gyrus of the hippocampus and the subventricular zone (SVZ).142 Adult neurogenesis in the hippocampus has been extensively characterized given its important role in learning and memory.143 However, the SVZ represents the largest proliferative niche in the adult mammalian brain,140 and several studies have linked gliomagenesis to this location.NSCs are defined by their potential to differentiate into any neural cell, including neurons, astrocytes and oligodendrocytes.144 Subsequent studies demonstrated that intraventricular injections of avian sarcoma virus could induce neoplasms which migrate out to the surrounding white matter, supporting the idea that gliomas observed outside of the SVZ may nevertheless have originated from cells within this neurogenic niche.146 This result has been further validated by more recent studies using retroviral injections to introduce combinations of oncogenic mutations to restricted brain areas, finding that malignant transformation is possible within the proliferative niche but exceedingly difficult outside of this area.146–148In general, studies investigating the anatomical location of gliomagenesis do so by observing the frequency of tumour formation after oncogenic mutations are introduced to select brain locations in model organisms. The earliest experiments invoking this approach introduced carcinogens into the SVZ versus the peripheral cortex of rodents revealing that gliomas form much more frequently when the SVZ is targeted.150 Comprising the largest population of dividing cells in the adult mammalian brain,152 OPCs are a natural candidate as a cell-of-origin for glioma, with several lines of experimental evidence supporting this idea. First, human glioma samples often express histological and genetic markers of OPCs.153–155 While the cellular landscape of gliomas is vastly heterogeneous,129 glioma cells characterized by OPC markers have been shown to be synaptogenic, proliferative and plastic, supporting the notion that these cells could drive glioma development.156 Second, in vitro and in vivo experiments have demonstrated that OPCs are capable of malignant transformation using the same combinations of mutations which induce gliomas in NSCs.157–159 Finally, using an innovative transgenic mouse model, Zong and colleagues160 found that even when the initial oncogenic mutations occur in NSCs, aberrant cell growth leading to malignancy is first observed in the cells that have differentiated into OPCs.OPCs are a recently discovered neural cell type which play a major role in regulating myelin plasticity in response to motor and cognitive experiences.162 The SVZ of adult humans, on the other hand, is composed of four distinct layers: (i) Layer I, composed of ependymal cells that line the lateral ventricles; (ii) Layer II, a hypocellular gap; (iii) Layer III, an astrocytic ribbon composed of NSCs; and (iv) Layer IV, a transitional zone into the brain parenchyma which includes some myelinated axons.164 Unlike in rodents, NSCs in the adult human astrocytic ribbon do not partake in a chain migration toward the olfactory bulb, as migration from the neurogenic niche declines massively following infancy.165Animal models have been useful in elucidating the likely molecular mechanisms underlying early oncogenic events in primary brain cancers. However, it is worth noting that neural proliferative niches are organized differently in humans compared to mice, which poses a limitation for transgenic mouse models to identify the origins of human glioma. In the SVZ of adult mice, NSCs align within a rostral migratory stream directly adjacent to the ependymal zone, and these cells continuously populate the olfactory bulb.ex vivo human tissue, either from neurosurgery cases or post-mortem, can be especially informative. Such studies have confirmed that adult SVZ tissue is indeed proliferative and plastic, capable of forming neurospheres in culture and differentiating into astrocytes, neurons and oligodendrocytes.163 Studies of resected human glioma samples have revealed the existence of stem cells in brain tumours as well.166–168 These BTSCs are thought to be the drivers of glioma malignancy, given that transplantation of these cells into a living organism can induce a glioma on its own.170 BTSCs share many characteristics with NSCs, including multipotency, self-renewal and migratory capabilities—a finding which supports the hypothesis that gliomas originate from NSCs.171Given the cytoarchitectural and functional differences between the human and rodent SVZ, studies that examine 172 While gliomas commonly contact the lateral ventricles ,174 it had been unclear whether glioblastomas that do not appear to involve the SVZ nevertheless harbour tumour-originating cells in that region. Lee and colleagues172 obtained samples from the tumour mass and tumour-free SVZ tissue in 14 GBM patients, and in 9 of their subjects, they observed oncogenic mutations in the visually healthy SVZ that were apparent at higher levels within the tumour  has been thoroughly characterized as a biological mechanism of learning and memory.181 epilepsy,182 and, most notably for this review, glioma.183 Adopting a similar optogenetic model mentioned previously,177 Venkatesh and colleagues184 demonstrated that neuronal activity in the premotor cortex can also stimulate glioma cell proliferation and growth. Strikingly, gliomas fail to progress in the absence of growth factors  secreted by neurons and OPCs during neuronal activity.183 Gliomas are dependent on growth factors like neuroligin-3 because they regulate the formation of functional synapses between neurons and glioma cells.185 Interestingly, depolarization of the glioma cells themselves by optogenetics can trigger their proliferation, and conversely, blocking glutamatergic neurotransmission significantly diminishes glioma cell division.183 Recent work has elucidated in further detail how glioma cell invasion resembles features of normal neural development, and how neuronal activity increases the speed of invasion.186Myelin plasticity appears to be a double-edged sword, with an integral role in disease as well. Aberrant myelin plasticity has been implicated as a contributor to several neurological conditions, including chemotherapy-induced cognitive impairments,156 reported elevated high gamma power in tumour-infiltrated compared to tumour-free tissue, confirming that gliomas are electrically active. A currently unpublished study of 14 GBM patients presented evidence of glioma tumours integrating into cognitive circuits responsive to a lexical retrieval task.6 Krishna and colleagues6 collected intracranial brain recordings of tumour-infiltrated tissue and the inferior frontal gyrus and found task-induced changes beyond canonical language regions into the tumour, suggesting that the glioma had integrated into the language circuit. This functional integration related to worse language performance and poorer overall survival. In summary, the above studies suggest that neuronal networks contribute to the pathogenesis of diffuse gliomas, though further research is needed to establish their causal role in tumour development in patients.Given that most of the described experiments were performed using animal models, it is natural to ask next whether these principles also apply in the human condition. Applying electrode grids to the tumours of three glioblastoma patients, Venkatesh and colleagues187 Some investigators have proposed that molecular properties specific to this region, like high glutamatergic flux, may support IDH-mediated gliomagenesis.188 More direct testing of the relationship between gliomas and human brain networks, however, requires multimodal interrogation of the complex interactions between the glioma microenvironment and surrounding neural circuitry.While the fields of brain mapping and glioma biology each have a decades-long history, research that has synthesized both fields to examine the pathophysiology of glioma has emerged relatively recently. Because neuroimaging and tissue biopsies are both routinely acquired during standard clinical care for patients with glioma, there is tremendous opportunity to triangulate mechanisms of glioma pathogenesis by leveraging tools from brain mapping and molecular biology. For example, using retrospective brain imaging data and molecular genetic subtyping, it has been shown that IDH-mutated gliomas predominantly appear in the frontal lobe.189–191 Other work has proposed mechanisms for how tumour infiltration interferes with cognitive processing, demonstrating that gliomas can alter the information content of electrical brain activity.192 However, the relationship between brain networks and brain tumours is bidirectional—tumours have an impact on both local and global brain circuits, while in turn, brain networks  influence the development and spread of diffuse gliomas.Several prospective research studies have examined how brain tumours affect functional organization, finding that the presence of a tumour disrupts many graph theoretical properties of brain networks.193 asked whether the non-random spatial distribution of diffuse gliomas could be explained by various biological factors hypothesized to relate to gliomagenesis, including functional hubness, frequency of stem-like cells and spatial transcription patterns of genetic drivers of glioma. Consistent with a prior research finding that neurological disorders tend to affect brain hubs,81 a robust correspondence was observed between glioma frequency and functional hubness. In particular, glioma frequency was higher in association cortex compared to primary cortex and was most correlated with graph theoretical measures of connector hubs, brain regions that link together multiple subsystems.The latter half of this cycle was explored in a few recent studies utilizing a diversity of methods including rs-fMRI, diffusion tractography, transcriptomics and MEG. Mandal and colleagues194 In line with these results, another study found that malignant gliomas preferentially occur in regions with high intrinsic electrical activity as assessed by MEG.195 Finally, Mandal and colleagues196 identified intrinsic structural and functional networks that coincide with patterns of glioma localization, specifically implicating brain networks connecting neurogenic niches in the SVZ with the cortex. Taken together, these studies are consistent with a model wherein periventricular brain connectivity guides glioma development from the SVZ into distributed cortical locations  MEG study found an association between functional connected tumour regions and synaptogenic factors like thrombospondin-3.6These findings also suggest that non-invasive measures of brain networks could hold prognostic information regarding the spread and malignancy of glioma tumours. Indeed, some studies have found that measures of functional connectivity are predictive of clinical outcomes for glioma patients, independent of other known prognostic factors.11 recently demonstrated that diffusion MRI can detect subtle regional disturbances in the structural connectome that are suggestive of distant glioma invasion and predictive of poor survival outcomes, yet invisible on conventional MRI. Connectomics could develop into a crucial tool to predict and ultimately improve prognosis for patients with diffuse gliomas.Gaining a better understanding of the molecular factors guiding tumour progression along specific brain networks will be critical for the design of personalized therapies for glioma. For example, clinical trials are currently being run to determine if personalized radiation therapies guided by predictive modelling of recurrence can improve clinical outcomes in glioblastoma . An understanding of where a glioma will migrate next can inform pre-emptive strategies to target brain areas that may appear healthy on conventional imaging, but nevertheless harbour malignant cells. Indeed, Wei and colleaguesin vivo access to brain network function in patients. The current research in this area suggests that the analysis of such networks holds prognostic value and describes the common locations of gliomas. Future work should further develop the synthesis between network neuroscience and neuro-oncology, focusing on better defining molecular targets to curtail glioma progression.A better understanding of the neural pathways by which gliomas migrate within the brain could prove critical in the development of novel therapeutic strategies for patients with diffuse glioma. Network neuroscience is poised to tackle this unmet need in the study of glioma pathophysiology by providing non-invasive,


These results suggest that schematic cognitive models of body dissatisfaction and eating disorders body may also be applicable to men, however more research is needed.Over the past 30 years, attentional bias for body shape and weight content has been implicated in the precipitation and maintenance of body dissatisfaction and eating disorders. Although the existence of this bias toward body stimuli is well-established in female populations, it is comparatively understudied in men. This review aimed to examine the nature of this visual attentional bias toward male bodies in male samples across a range of different attentional paradigms, including eye-tracking, dot-probe, and the visual search task. Results were heterogenous, finding some evidence that men with higher body dissatisfaction and eating disorder symptoms demonstrated an attentional bias toward desirable bodies of  Body dissatisfaction can be defined as a negative subjective evaluation of one’s body as a whole or relating to specific aspects of one’s body such as body size, shape, muscularity/muscle tone, and weight Grogan, . Over thCooper , 2005 suVitousek and Hollon’s  cognitivWilliamson et al.  built upAttentional bias can be defined as the tendency of one’s perception to be affected by recurring, salient thoughts  stimuli and body dissatisfaction in women has been examined in many studies through a variety of paradigms, however very few have dealt with men. This may be problematic as body weight and shape concerns, and indeed psychopathology of male eating disorders may differ from females  examining the evidence for attentional biases toward desirable male bodies of others and (2) toward undesirable features of one’s own body across a range of different paradigms for measuring attention. Results are summarized in Table  examininBias toward desirable body features of others. Nine studies were identified showing that men who are dissatisfied with their bodies tend to display attentional biases toward desirable body features of others. Using eye tracking, Stephen and colleagues , and (2) dissatisfied men tend to have attentional biases toward perceived negative or nonideal features of their own body. However, there are at least four studies which provide evidence counter to this proposed attentional model. So, what does this mean for schematic cognitive models of body dissatisfaction and eating disorders? Effectively, these results suggest some evidence that existing cognitive models . A further example is seen in that typical measures of attention such as the dot probe task are unable to differentiate between attentional engagement and attentional disengagement biases, and as such are measuring different facets of attentional bias , a relatively low number compared with the magnitude of research that has been conducted on their female counterparts and hardly large enough to conclusively generalize the nature of these biases to the male population. Additionally, many of these participants were nonclinical and/or university students (approximately 65%). Of the remaining 35% of participants, 108 (18%) were Chinese gym attendees (Jin et al., A further limitation of the existing examination of attentional bias in men is the sheer lack of empirical data. All existing studies identified for this review, of which there were only 11, returned a combined sample of 618 men (A final limitation of the extant literature is that to date, there are no studies investigating whether body-related attentional biases play a causal role in body image and eating disturbances in men. Causality could sufficiently be examined through the use of ABM paradigms (Renwick et al., Based on these limitations, several recommendations for future studies are proposed. First, future studies should determine the ideal paradigm for best examining attentional bias toward body stimuli. Although on face-value eye-tracking may present as the most comprehensive measure of attentional patterns, the field would benefit from an experimental comparison of the sensitivity for detecting attentional bias across the four paradigms. Notably, paradigms that provide measures of attentional engagement and attentional disengagement, such as the ARDPEI, should be utilized (Dondzilo et al.,


Cells entitled “Neuroglobin from Brain Protection to Cancer Progression,” we collect both reviews and articles aimed to identify the molecular mechanisms at the root of the biological functions of NGB that will bear fundamental and translational significance, especially in the development of therapeutics against neurological disorders.Since its discovery in 2000  and char2 buffer and transport, whereas at low concentrations (~1 µM) NGB can display only potential enzymatic activities and cell signaling in most tissues and organs. In this review, a role for NGB is highlighted as a compensatory protein responding to hypoxic, ischemic or oxidative injuries by activating survival/antiapoptotic pathways [The brain was believed to lack any system able to promote the storage and/or the diffusion of oxygen similar to what occurs in the muscle system with myoglobin (MB). However, the discovery of NGB as a monomeric intracellular globin has raised the possibility of a myoglobin-like function of NGB . In the pathways . The piopathways  first evpathways . The pappathways  providespathways .Data accumulated to date suggest that NGB is a stress-inducible protein whose overexpression and mitochondrial localization could provide a compensatory response to injuring stimuli protecting against neurodegenerative diseases. Moreover, stably transfected neuroblastoma cells overexpressing endogenous NGB suggested that the role in neuroprotection played by NGB is reliable only through interaction with mitochondrial lipid raft-associated complexes ,9. ConseThe paper of Manganelli and colleagues  has inveIn their paper, Solar Fernandez and colleagues  evaluateAs a whole, this Special issue highlights the dual role played by NGB overexpression in human diseases as well as some of involved mechanisms. Data sustain that NGB has shown enormous potential to develop therapies against inflammation and oxidative stress due to its common pathological features across many neurological diseases, including stroke, Alzheimer’s disease, and traumatic brain injury, among others. On the other hand, NGB overexpression shows similar protective effects on estrogen-related cancer. Perhaps one of the greatest current challenges is to seek strategies that aim to selectively increase both the levels and cellular expression of NGB in the brain, reducing or preventing the globin accumulation in estrogen-related cancer. This strategy could be reached with the use of plant-derived compounds with estrogenic activity, that differently activating estrogen receptor subtypes reduces NGB levels in cancer increasing the globin levels in brain tissues . Investi


Statins are one of the standard treatments to prevent cardiovascular events such as coronary artery disease and heart failure (HF). However, data on the use of statins to improve clinical outcomes in patients with established HF remains controversial. We summarized available clinical studies which investigated the effects of statins on clinical outcomes in patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Statins possess many pleiotropic effects in addition to lipid-lowering properties that positively affect the pathophysiology of HF. In HFrEF, data from two large randomized placebo-controlled trials did not show benefits of statins on mortality of patients with HFrEF. However, more recent prospective cohort studies and meta-analyses have shown decreased risk of mortality as well as cardiovascular hospitalization with statins treatment. In HFpEF, most prospective and retrospective cohort studies as well as meta analyses have consistently reported positive effects of statins, including reducing mortality and improving other clinical outcomes. Current evidence also suggests better outcomes with lipophilic statins in patients with HF. In summary, statins might be effective in improving survival and other clinical outcomes in patients with HF, especially for patients with HFpEF. Lipophilic statins might also be more beneficial for HF patients. Based on current evidence, statins did not cause harm and should be continued in HF patients who are already taking the medication. Further randomized controlled trials are needed to clarify the benefits of statins in HF patients. Statins also upregulate the expression of low-density lipoprotein (LDL) receptors on the cell membrane. Clinically, statins can reduce LDL by 20–60% and triglyceride by 10-40%, and increase high-density lipoprotein (HDL) by 5%-15%. Statins have a dramatic effect on reducing cardiovascular events and the data from one clinical trial has shown that the risk for major vascular events is reduced by 22% for each 1 mmol/L reduction in LDL.Statins are blood-cholesterol-lowering drugs that are used worldwide to prevent cardiovascular diseases. They inhibit the 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) enzyme and thereby reduce the synthesis of cholesterol in the liver, irrespective of patients’ cholesterol level. Robust evidence also exists supporting the role of statins in the prevention of new incident heart failure (HF). Given their many pleiotropic effects, statins were hypothesized to also be effective in improving outcomes in patients with established HF. However, the evidence for the use of statins in patients with HF is controversial, so the role of statins in this population remains unclear. Current guidelines have not recommended the routine use of statins in most patients with HF without other indications for their use , but suggest that statins can be continued for patients who were already on treatment. More recent evidence is emerging suggesting potential benefits of statins in patients with HF to improve clinical outcomes.The role of statins in the primary and secondary prevention of coronary artery disease is well establishedIn this review, we summarized potential mechanisms of statins that might be beneficial for patients with HF and available clinical studies which investigated the effects of statins on clinical outcomes in patients with HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF).. Statins stabilize atheromatous plaques and possess antiatherogenic properties that help reduce atheroma volume and prevent formation of new atherosclerotic lesions, which in turn reduces ischemic burden and results in less cardiac tissue damage. Statins promote left ventricular healing after myocardial infarction by improving left ventricular remodeling and modulating endothelial function. Statins have also been found to possess anti-inflammatory properties leading to the reduction of oxidative stress in vascular and myocardial tissues. Other important mechanisms of statins have been reported, including enhancement of endothelial function, inhibition of the action of angiotensin II, reduction of sympathetic activities, delaying apoptosis, and suppression of arrhythmogenesis. In addition, statins may exert their pleiotropic effects through epigenetic modifications as they can act as histone deacetylase inhibitors. Despite two large randomized controlled trials (RCTs) exploring the effects of statins in systolic HF showing that statins did not improve all-cause mortality or other major cardiovascular outcomes, some meta-analyses and observational studies have shown the contrary. This is discussed in detail below for HFrEF and HFpEF, respectively.Multiple mechanisms have been described to explain the potential benefits, as well as harms, of statins in HF. Various experimental and clinical studies have described pleiotropic effects of statins independent of their lipid-lowering ability that might improve the outcomes in patients with HF. Current evidence suggest that the benefits of statins also depend on their lipophilicity, thus the effects in populations with HF should not be expected to be identical across all statin drug class.There are two types of statins: hydrophilic  and lipophilic . Each type has different properties. Hydrophilic statins employ active transport into hepatocytes, whereas lipophilic statins enter cells by passive diffusion and therefore are more easily absorbed by peripheral tissues, including myocardial cells; however, it is unclear if low cholesterol is an independent risk factor for poor outcomes or is merely a surrogate marker for cardiac cachexia in patients with more severe disease. It has also been hypothesized that hepatic and intestinal congestion in patients with HF can impair hepatic cholesterol synthesis and cholesterol absorption, respectively, therefore resulting in lower plasma cholesterol level. In a South Korean cohort study analyzing 2,797 patients with HF, those with lower serum total cholesterol had more markers of severe diseases , but low cholesterol itself did not affect the clinical outcomes in a propensity score matched analysis. Another mechanism through which statins might adversely affect HF is that statins have been shown to reduce serum coenzyme Q10 level, an important enzyme in myocardial bioenergetics. Studies have reported that low serum coenzyme Q10 level was associated with increased mortality in patients with HF. Nevertheless, two large randomized trials have shown that statin use was not associated with significant worsening of clinical outcomes or adverse events in HF populations.Multiple mechanisms have also been proposed that raise concerns for the adverse effects of statins in patients with HF. Low serum total cholesterol has been associated with higher mortality in patients with HF for instanceIn this non-systematic review, we searched Ovid MEDLINE for randomized controlled trials on the use of statins in heart failure patients. The review covers from current clinical evidence to pathophysiology review accrued from bench research. The search terms used are depicted in and GISSI-HF. Neither of these trials found benefits of statin therapy in patients with HF. The CORONA trial recruited 5,011 patients with ischemic HFrEF (mean age 73 years and mean left ventricular ejection fraction [LVEF] 31%) and randomized the subjects to either rosuvastatin 10 mg daily or a placebo. After the median follow-up of 32.8 months, there was no significant reduction in the primary endpoints, including the composite of death from cardiovascular causes, nonfatal myocardial infarction, and nonfatal stroke, and no significant difference between groups. However, a post-hoc analysis of this trial showed that, when comparing outcomes by N-terminal pro-B-type natriuretic peptide (NT-proBNP) tertile, patients in the lowest NT-proBNP tertile (< 103 pmol/L) had a significantly lower risk of the primary outcomes. In addition, rosuvastatin also reduced the incidence of hospitalization for cardiovascular causes which has also been found by other researchers. Secondly, the GISSI-HF trial randomized 4,574 patients with HF from any cause  to receive either 10 mg daily of rosuvastatin or a placebo. Similar to the result of the CORONA trial, there were no significant differences in the co-primary outcomes of all-cause mortality or in the combined endpoints of death or hospitalization for cardiovascular causes.A list of relevant clinical studies on the use of statins in HFpEF patients is presented in. The observation from the CORONA trial that the patients in the lowest NT-proBNP tertile had a significantly lower risk of the primary adverse outcomes suggested that patients with less severe HF might benefit from statin therapy. Another study also found that the benefits of simvastatin toward reduction in major vascular events was relatively smaller in patients with higher NT-proBNP level. Moreover, the results of the GISSI-trial might have been impacted by compliance issues, as about one third of the study participants discontinued therapy for various reasons. Furthermore, the CORONA and GISSI-HF trials also assessed rosuvastatin at a low dose, and therefore the results cannot be inferred to a higher dose or different types of statins. It is worth recalling that rosuvastatin is a hydrophilic statin, which has poor uptake by cardiac muscles. On the other hand, lipophilic statins , which have better penetration into cardiac muscle cells and therefore possibly better influence the myocardium through pleiotropic effects, leading to the improvement in outcomes of patients with HF. In addition, other small randomized studies have shown that statins can improve certain surrogate endpoints in HF , but not major outcomes , although it appears that most of the studies might not be adequately powered to assess major clinical outcomes.Despite the above results, certain concerns regarding the generalizability of the trial results to the entire HFrEF population have been raised. The study population in both trials had a mean age of 73 years and most of the participants were already in advanced HF stages, namely New York Heart Association classification III and IV. The results concur with other observational studies that were reported both before and after the publications of CORONA and GISSI-HF trials. In addition, a recent meta-analysis including 88,100 patients from 17 studies  found that statin therapy was associated with reduced all-cause mortality and other secondary endpoints in patients with HF, similarly to other observational studies. The benefit was also consistent in HFrEF patients in the subgroup analysis. It has been suggested that the reason for the different results between large randomized trials and real-life cohort studies is that the randomized cohorts might not be representative of those in clinical practice as well as the dose and type of statins chosen in randomized trials, among other reasons. Therefore, additional randomized trials using statins other than rosuvastatin with more generalized inclusion in HFrEF populations may be warranted.A more recent, albeit observational, nationwide prospective study, propensity score matched 21,864 patients with HFrEF from 86% of all Swedish hospitals. Statins were associated with reduced all-cause mortality , cardiovascular mortality, cardiovascular hospitalization, combined all-cause mortality, and HF hospitalization. A list of relevant clinical studies of statins in patients with HFpEF is presented in. An observational prospective study following 9,140 patients with HFpEF in the Swedish Heart Failure Registry found that statin therapy was associated with decreased 1-year all-cause mortality , as well as decreased cardiovascular and composite all-cause mortality or cardiovascular hospitalization. Another multicenter prospective observational study in Japan with 3,124 patients with HFpEF also showed a similar decrease in mortality in patients with statins at three years . In the United States, a retrospective cohort study of 13,440 patients with HF, of which 7,563 had HFpEF (LVEF ≥ 50%) showed that statin use was associated with decreased mortality  in patients with HFpEF but not in patients with HFrEF or mid-range ejection fraction (HFmrEF). Several meta-analyses have been conducted to assess the effects of statins on mortality in patients with HFpEF and have suggested similar findings of reduced mortality rate.Unlike HFrEF, no large RCTs have been conducted to assess the benefits of statins in patients with HFpEF. However, many studies have suggested the benefits of statins in this population. Moreover, the subgroup analyses also showed consistent benefits of statins across all range of blood cholesterol and HF severity. This study further suggested that statins might be beneficial for HFpEF patients even in those without coronary artery disease. Interestingly, it has been suggested that the findings where benefits of statins were consistently observed in HFpEF whereas in HFrEF the results were mixed might be due to greater effects of pleiotropic properties of statins on the pathophysiology of HFpEF and patients’ comorbidities. While these findings appear promising, it is important to emphasize that these observational studies are only hypothesis-generating. Well-designed RCTs are needed to confirm the effects of statin therapy in HFpEF patients.The studies mentioned above had included patients with coronary artery disease. In a most recent multicenter observational prospective cohort study in Japan evaluating 414 HFpEF patients without coronary artery disease, the use of statins was associated with a substantial decrease in 3-year mortality both in the entire and propensity score-matched cohorts. Experimental studies have shown that statins can improve diastolic dysfunction and attenuated left ventricle stiffness, both of which are important underlying pathogeneses of HFpEF. Anti-inflammatory and anti-oxidant effects of statins may be beneficial in HFpEF as systemic inflammatory state plays a role in the pathogenesis of HFpEF. The pleiotropic effects of statins are illustrated in, as current evidence suggests that statins have been shown to be beneficial in these conditions as well. Lipophilic statins may have a favorable effect on sympathetic activity in HFpEF, as increased sympathetic activity is associated with the pathogenesis of HFpEF. Further mechanistic studies focusing on the underlying cellular and molecular changes in HFpEF caused by statins are needed. Possible roles of collagen synthesis, matrix metalloproteinases inhibition, Rho kinase 1 gene expression in relation to diastolic dysfunction in HFpEF may be an area of further studies. The mechanism of stains and regulation of the Rho GTPase cycle is illustrated inPossible unique pleiotropic effects of statins in patients with HFpEF have not been clearly defined. Statins may help prevent left ventricular hypertrophy and fibrosis in HFpEF. Icosapent ethyl, a form of highly purified omega-3 eicosapentaenoic acid ethyl ester (EPA), is used to lower triglyceride levels. EPA has been shown to effectively reduce ischemic events and cardiovascular death among patients who have high triglyceride levels and are already on statin therapy. EPA may also reduce new HF hospitalization if a high level of on-treatment EPA in the blood is achieved.Proprotein convertase subtilisin–kexin type 9 inhibitors (PCSK9i) have not been shown to reduce death or hospitalizations from worsening HF in two large RCTs. Application of artificial intelligence may improve efficacy of RCTs by improving the patient’s selection process, minimizing measurement errors when assessing endpoints, or even providing trials with synthetic control groups.The current evidence we have for statins in the treatment of established HF is far from satisfactory. Our review shows contradicting evidence for statin therapy in HFrEF. Despite negative results from two large RCTs, many real word data do suggest benefits of statins in HFrEF. Thus, an adequately powered randomized placebo-controlled trial to determine the effects of statins other than rosuvastatin with more generalized inclusion criteria, preferably with long term follow up, is needed to clarify the benefits of statins in HFrEF. On the other hand, the benefits of statins in improving clinical outcomes among HFpEF patients were consistently reported by observational studies both in the United States and international institutions, even in patients without coronary artery disease. Nevertheless, there exists a dire need for new treatment options for patients with HFpEF as the current choice of pharmacological treatments is very limited. A future RCT comparing the treatment outcomes of statin therapy in HFpEF is also needed and would contribute a significant impact in closing the knowledge gap of HF management. Moreover, a direct head-to-head comparison of hydrophilic versus lipophilic statins in HF will also help elucidate the hypothesis that lipophilic statins might be more appropriate for patients with HF. To generate this much needed evidence, artificial intelligence - a rapidly developing field in medicine - has the potential to be incorporated in the design and execution of future RCTs. This concept has been applied in cardiovascular medicine and may also provide the insights into spatio-temporal statin-mediated mechanisms of statins in patients with HF. For example, a group of investigators has conducted a network analysis integrating myocardial infarction drugs, drugs interactors, drug targets, and myocardial infarction disease genes onto the human interactome. By integrating gene-disease associations, they were able to identify “drug-target-disease modules”, which provide a better understanding of the drug actions and mechanisms. This approach should also be pursued to elucidate the mechanisms of statins in patients with HF in addition to the previously existing clinical evidence from RCTs.More recently, network medicine is a novel discipline that studies and integrates heterogenous interconnected molecular and genetic data as a network and identifies perturbations in these networks that ultimately causes diseaseAll data underlying the results are available as part of the article and no additional source data are required. This manuscript presents a much needed review of the existing literature regarding the effects of statins on clinical outcomes of HF patients with preserved or reduced ejection fraction. The article is clear, informative, and correctly organised. It cites the most relevant clinical studies in the field, such as the CORONA and GISSI-HF, while giving relevant context on the strengths and limitations of each study. The important and many times overlooked behaviour of lipophilic vs. hydrophilic statins was also included in this review. Although the discussion could go slightly deeper, particularly regarding other outcomes that could add to the utility of statin therapy in HF patients, the conclusions seem correct and in adequacy with the findings of the reviewed evidence.  Other impression that I feel obliged to add to this review is that these findings seem to be in line with my own impressions from both clinical practice with HF patients and studies I conducted or participated in.  The article is also very informative and written in a pedagogical fashion. Figures 1 and 2 were tastefully added and are useful illustrations to the implied mechanisms.  In face of my review, I recommend the indexing of this manuscript.  My congratulations to the authors.Is the review written in accessible language?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn appropriate in the context of the current research literature?YesIs the topic of the review discussed comprehensively in the context of the current literature?YesReviewer Expertise:Heart failure; frailty; sarcopenia; nutritional and functional status in heart failure; muscle strength; medication affecting muscle function in heart failure.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a comprehensive review on the role of statins in heart failure both with reduced and preserved ejection fraction. The paper is well-written, informative and easy to read. The authors reported the most important studies and the meta-analysis on the topic in a very exhaustive manner. Although some data are not analyzed in-depth the manuscript deserves indexing because it is very educational and useful.Is the review written in accessible language?YesAre all factual statements correct and adequately supported by citations?YesAre the conclusions drawn appropriate in the context of the current research literature?YesIs the topic of the review discussed comprehensively in the context of the current literature?YesReviewer Expertise:Clinical CardiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for your time and comment.