Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity

Functional neuroimaging using MRI relies on measurements of blood oxygen level-dependent (BOLD) signals from which inferences are made about the underlying neuronal activity. This is possible because neuronal activity elicits increases in blood flow via neurovascular coupling, which gives rise to the BOLD signal. Hence, an accurate interpretation of what BOLD signals mean in terms of neural activity depends on a full understanding of the mechanisms that underlie the measured signal, including neurovascular and neurometabolic coupling, the contribution of different cell types to local signalling, and regional differences in these mechanisms. Furthermore, the contributions of systemic functions to cerebral blood flow may vary with ageing, disease and arousal states, with regard to both neuronal and vascular function. In addition, recent developments in non-invasive imaging technology, such as high-field fMRI, and comparative inter-species analysis, allow connections between non-invasive data and mechanistic knowledge gained from invasive cellular-level studies. Considered together, these factors have immense potential to improve BOLD signal interpretation and bring us closer to the ultimate purpose of decoding the mechanisms of human cognition. This theme issue covers a range of recent advances in these topics, providing a multidisciplinary scientific and technical framework for future work in the neurovascular and cognitive sciences

Non-signalling energy use in the developing rat brain

Energy use in the brain constrains its information processing power, but only about half the brain's energy consumption is directly related to information processing. Evidence for which non-signalling processes consume the rest of the brain's energy has been scarce. For the first time, we investigated the energy use of the brain's main non-signalling tasks with a single method. After blocking each non-signalling process, we measured oxygen level changes in juvenile rat brain slices with an oxygen-sensing microelectrode and calculated changes in oxygen consumption throughout the slice using a modified diffusion equation. We found that the turnover of the actin and microtubule cytoskeleton, followed by lipid synthesis, are significant energy drains, contributing 25%, 22% and 18%, respectively, to the rate of oxygen consumption. In contrast, protein synthesis is energetically inexpensive. We assess how these estimates of energy expenditure relate to brain energy use in vivo, and how they might differ in the mature brain

Extinction of cue-evoked food seeking recruits a GABAergic interneuron ensemble in the dorsal medial prefrontal cortex of mice

Animals must quickly adapt food-seeking strategies to locate nutrient sources in dynamically changing environments. Learned associations between food and environmental cues that predict its availability promote food-seeking behaviors. However, when such cues cease to predict food availability, animals undergo 'extinction' learning, resulting in the inhibition of food-seeking responses. Repeatedly activated sets of neurons, or 'neuronal ensembles', in the dorsal medial prefrontal cortex (dmPFC) are recruited following appetitive conditioning and undergo physiological adaptations thought to encode cue-reward associations. However, little is known about how the recruitment and intrinsic excitability of such dmPFC ensembles are modulated by extinction learning. Here, we used in vivo 2-Photon imaging in male Fos-GFP mice that express green fluorescent protein (GFP) in recently behaviorally-activated neurons to determine the recruitment of activated pyramidal and GABAergic interneuron mPFC ensembles during extinction. During extinction, we revealed a persistent activation of a subset of interneurons which emerged from a wider population of interneurons activated during the initial extinction session. This activation pattern was not observed in pyramidal cells, and extinction learning did not modulate the excitability properties of activated neurons. Moreover, extinction learning reduced the likelihood of reactivation of pyramidal cells activated during the initial extinction session. Our findings illuminate novel neuronal activation patterns in the dmPFC underlying extinction of food-seeking, and in particular, highlight an important role for interneuron ensembles in this inhibitory form of learning

An open-source pipeline for analysing changes in microglial morphology

Changes in microglial morphology are powerful indicators of the inflammatory state of the brain. Here, we provide an open-source microglia morphology analysis pipeline that first cleans and registers images of microglia, before extracting 62 parameters describing microglial morphology. It then compares control and 'inflammation' training data and uses dimensionality reduction to generate a single metric of morphological change (an 'inflammation index'). This index can then be calculated for test data to assess inflammation, as we demonstrate by investigating the effect of short-term high-fat diet consumption in heterozygous Cx3CR1-GFP mice, finding no significant effects of diet. Our pipeline represents the first open-source microglia morphology pipeline combining semi-automated image processing and dimensionality reduction. It uses free software (ImageJ and R) and can be applied to a wide variety of experimental paradigms. We anticipate it will enable others to more easily take advantage of the powerful insights microglial morphology analysis provides

Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience

Cognitive neuroscience depends on the use of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to probe brain function. Although commonly used as a surrogate measure of neuronal activity, BOLD signals actually reflect changes in brain blood oxygenation. Understanding the mechanisms linking neuronal activity to vascular perfusion is, therefore, critical in interpreting BOLD. Advances in cellular neuroscience demonstrating differences in this neurovascular relationship in different brain regions, conditions or pathologies are often not accounted for when interpreting BOLD. Meanwhile, within cognitive neuroscience, increasing use of high magnetic field strengths and the development of model-based tasks and analyses have broadened the capability of BOLD signals to inform us about the underlying neuronal activity, but these methods are less well understood by cellular neuroscientists. In 2016, a Royal Society Theo Murphy Meeting brought scientists from the two communities together to discuss these issues. Here we consolidate the main conclusions arising from that meeting. We discuss areas of consensus about what BOLD fMRI can tell us about underlying neuronal activity, and how advanced modelling techniques have improved our ability to use and interpret BOLD. We also highlight areas of controversy in understanding BOLD and suggest research directions required to resolve these issues

Choice of method of place cell classification determines the population of cells identified

Place cells, spatially responsive hippocampal cells, provide the neural substrate supporting navigation and spatial memory. Historically most studies of these neurons have used electrophysiological recordings from implanted electrodes but optical methods, measuring intracellular calcium, are becoming increasingly common. Several methods have been proposed as a means to identify place cells based on their calcium activity but there is no common standard and it is unclear how reliable different approaches are. Here we tested four methods that have previously been applied to two-photon hippocampal imaging or electrophysiological data, using both model datasets and real imaging data. These methods use different parameters to identify place cells, including the peak activity in the place field, compared to other locations (the Peak method); the stability of cells’ activity over repeated traversals of an environment (Stability method); a combination of these parameters with the size of the place field (Combination method); and the spatial information held by the cells (Information method). The methods performed differently from each other on both model and real data. In real datasets, vastly different numbers of place cells were identified using the four methods, with little overlap between the populations identified as place cells. Therefore, choice of place cell detection method dramatically affects the number and properties of identified cells. Ultimately, we recommend the Peak method be used in future studies to identify place cell populations, as this method is robust to moderate variations in place field within a session, and makes no inherent assumptions about the spatial information in place fields, unless there is an explicit theoretical reason for detecting cells with more narrowly defined properties

Gradual not sudden change: multiple sites of functional transition across the microvascular bed

In understanding the role of the neurovascular unit as both a biomarker and target for disease interventions, it is vital to appreciate how the function of different components of this unit change along the vascular tree. The cells of the neurovascular unit together perform an array of vital functions, protecting the brain from circulating toxins and infection, while providing nutrients and clearing away waste products. To do so, the brain’s microvasculature dilates to direct energy substrates to active neurons, regulates access to circulating immune cells, and promotes angiogenesis in response to decreased blood supply, as well as pulsating to help clear waste products and maintain the oxygen supply. Different parts of the cerebrovascular tree contribute differently to various aspects of these functions, and previously, it has been assumed that there are discrete types of vessel along the vascular network that mediate different functions. Another option, however, is that the multiple transitions in function that occur across the vascular network do so at many locations, such that vascular function changes gradually, rather than in sharp steps between clearly distinct vessel types. Here, by reference to new data as well as by reviewing historical and recent literature, we argue that this latter scenario is likely the case and that vascular function gradually changes across the network without clear transition points between arteriole, precapillary arteriole and capillary. This is because classically localized functions are in fact performed by wide swathes of the vasculature, and different functional markers start and stop being expressed at different points along the vascular tree. Furthermore, vascular branch points show alterations in their mural cell morphology that suggest functional specializations irrespective of their position within the network. Together this work emphasizes the need for studies to consider where transitions of different functions occur, and the importance of defining these locations, in order to better understand the vascular network and how to target it to treat disease

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the US Food and Drug Administration (FDA)-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat COVID-19

Using H-alpha Morphology and Surface Brightness Fluctuations to Age-Date Star Clusters in M83

We use new WFC3 observations of the nearby grand design spiral galaxy M83 to develop two independent methods for estimating the ages of young star clusters. The first method uses the physical extent and morphology of Halpha emission to estimate the ages of clusters younger than tau ~10 Myr. It is based on the simple premise that the gas in very young (tau < few Myr) clusters is largely coincident with the cluster stars, is in a small, ring-like structure surrounding the stars in slightly older clusters (e.g., tau ~5 Myr), and is in a larger ring-like bubble for still older clusters (i.e., ~5-10 Myr). The second method is based on an observed relation between pixel-to-pixel flux variations within clusters and their ages. This method relies on the fact that the brightest individual stars in a cluster are most prominent at ages around 10 Myr, and fall below the detection limit (i.e., M_V < -3.5) for ages older than about 100 Myr. These two methods are the basis for a new morphological classification system which can be used to estimate the ages of star clusters based on their appearance. We compare previous age estimates of clusters in M83 determined from fitting UBVI Halpha measurements using predictions from stellar evolutionary models with our new morphological categories and find good agreement at the ~95% level. The scatter within categories is ~0.1 dex in log tau for young clusters (10 Myr) clusters. A by-product of this study is the identification of 22 "single-star" HII regions in M83, with central stars having ages ~4 Myr.Comment: 33 pages, 10 figures, 3 tables; published in March Ap

CO‐MRSA Infections in Australia Cost $3.5B Per Annum

Introduction The health and economic burdens of community-onset methicillin resistant Staphylococcus aureus (CO-MRSA) infections are needed to inform policy, planning and evidence-based practice. We aimed to synthesise data from a range of public sources to generate the first estimate of the national incidence and cost of CO-MRSA infections. Methods Incidences of CO-MRSA skin and soft tissue (SSTI), lower respiratory tract (LRTI) and bloodstream (BSI) infections were calculated for regions of Australia using data from existing literature and correspondence with specialists. Simulations estimated costs using treatment models developed for children and adults in primary or tertiary care settings and including bed-stay, diagnostics, procedures, mortalities and loss of productivity. Results Annually, in Australia there were found to be 3702 CO-MRSA SSTIs, 559 CO-MRSA BSIs and 425 CO-MRSA LRTIs, occupying 147,000 bed-days, including 1600 bed-days in intensive care. Incidence ranged from 4 /100,000 person-years in Tasmania to 243 /100,000 person-years in central Australia. CO-MRSA cost $3.5b annually in Australia. The higher incidence of SSTIs resulted in costs greater than summing the costs of BSIs and LRTIs. The greatest cost was mortality. The cost to the health system was found to be$1.9b, with bed occupancies accounting for ≥94%. Conclusion This was the first evaluation of the health and economic burden of CO-MRSA in Australia. We found a need for increased and more consistent data collection for a significant and expensive disease. Disclosure of Interest Statement: This research was funded by NHMRC grant GNT1027589