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Parkinson’s disease (PD) is the second most common neurodegenerative disease with mixed motor and non-motor symptoms. Dopaminergic drugs remain the mainstay of therapy for PD. However, some motor symptoms (e.g., gait problems) and a broad range of non-motor symptoms (e.g., cognitive impairment and depression) are often unresponsive to dopaminergic drugs. It is because the pathology of PD is not limited to the loss of midbrain dopamine neurons. Recent in vivo human brain imaging studies have provided novel insights into the dysfunction of multiple non-dopaminergic systems in PD. I review positron emission tomography, single photon emission computed tomography, neuromelanin-sensitive magnetic resonance imaging, and functional magnetic resonance imaging studies demonstrating the parallel alteration of noradrenergic, serotonergic, and cholinergic systems in PD. These studies shed light on the relationship between neuromodulators and vulnerable cognitive, affective, and motor functions. I finally discuss open questions in the field. More human pharmacological imaging studies are needed to reach a mechanistic understanding of the non-dopaminergic modulation of human brains.

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe heterogeneity at the mesoscopic length scale, orders of magnitude smaller than the nominal MRI voxel size, by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring tensor encoded diffusion weighted images (DWIs) and estimating DTD from them for in-vivo human brain imaging. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined gradient pulse duration and mixing/diffusion times in our diffusion preparation, we show that iPFG retains salient features of traditional multiple-PFG (mPFG) sequence while overcoming some of its implementation issues thereby extending its applications beyond DTD MRI. We assume DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite (CNTVD) to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape and orientation distributions to best fit the measured DWIs. From these tensors we obtain the mean diffusivity (MD) spectrum, spectrum of diffusion tensor shapes, microscopic orientation distribution function (µODF), and microscopic fractional anisotropy (µFA) which disentangle the underlying heterogeneity within a voxel. Using DTD derived µODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results obtained in the live human brain showed microscopic anisotropy in various gray and white matter regions and skewed MD distribution in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and identified the source of microscopic anisotropy which may help improve the diagnosis of various neurological diseases and disorders.

Human brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey’s successful tactic selection and decision making. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09702-0.

Recent overarching frameworks propose that various human social interactions are commonly supported by a set of fundamental neuropsychological processes, including social cognition, motivation, and cognitive control. However, it remains unclear whether brain networks implicated in these functional constructs are consistently engaged in diverse social interactions. Based on ample evidence from human brain imaging studies (342 contrasts, 7234 participants, 3328 foci), we quantitatively synthesized brain areas involved in broad domains of social interactions, including social interactions versus non-social contexts, positive/negative aspects of social interactions, social learning, and social norms. We then conducted brain network analysis on the ensuing brain regions and characterized the psychological function profiles of identified brain networks. Our findings revealed that brain regions consistently involved in diverse social interactions mapped onto default mode network, salience network, subcortical network and central executive network, which were respectively implicated in social cognition, motivation and cognitive control. These findings implicate a heuristic integrative framework to understand human social life from the perspective of component process and network integration.

Electromagnetic Tomography (EMT) is a novel imaging modality with clinically viable potentials, being capable for fast functional imaging. The technology, when matured, might present an effective supplement to current imaging technologies for an assessment of functionality of brain tissue with unique time resolution in a range of dozens of msec together with decent spatial resolution. We have re-designed EMT imaging technology towards brain imaging and developed novel EMT scanner for human brain imaging. The scanner was used in initial clinical studies for diagnostic of stroke. First EMT tomographic human brain imaging results are presented here.

Visualization of the subcellular structures deep into the living brain is a major challenge in life science. Miniaturized microendoscopes allow for imaging of deep brain structures in vivo. Conventional approaches use gradient index (GRIN) microlenses, which unfortunately suffer from greater aberrations and restricted fields of view if they become smaller and less invasive. Multimode fiber based endoscopes offer minimal invasive access to deep tissue, and when combined with advanced wavefront engineering techniques, they provide high-resolution imaging. Here, we report auto-fluorescence human brain imaging through a single 50 μm-core multimode fiber probe with a numerical aperture of 0.22 via two approaches: raster-scan imaging by active wavefront shaping and speckle-based compressive imaging enabled by computational image recovery. The compressive imaging approach significantly decreases the acquisition time for an up to three times bigger area of interest while maintaining a high spatial resolution. Accumulation of age-related pigment lipofuscin in Alzheimer’s disease human brain has been visualized with sub-Nyquist–Shannon speed with an improvement of up to 18 times. The proposed technique offers fast, sensitive, and high-resolution endoscopic imaging through a single hair-thin fiber, which would be of broader interest in the fields of neuroimaging and (pre-)clinical research.

Animal and human brain-imaging studies have suggested a role for neurodevelopmental abnormalities in the pathophysiology of dystonia. Variants in neurodevelopmental genes have also been sporadically implicated, although no systematic investigation has been undertaken before the more widespread availability of genome-wide sequencing techniques. Here, we review findings from recent whole-exome and whole-genome sequencing approaches in individuals with dystonic conditions, indicating that more than 50% of molecularly diagnosed cases may have variants in neurodevelopmental disorder-associated genes. We describe how genomic sequencing has contributed to phenotypic expansions of several known hereditary forms of dystonia to include classical neurodevelopmental features. Moreover, we demonstrate that many of the newly reported monogenic neurodevelopmental disorders can manifest with prominent dystonic presentations, including isolated generalized dystonia, paroxysmal dystonia, and dopa-responsive dystonia-parkinsonism. Considering the published evidence, we argue that the clinical feature dystonia might be regarded as an expression of developmental brain dysfunction, a status referring to the common etiological basis of many neurodevelopmental disease traits. Finally, we provide a view into clinical implications, including the necessity to integrate the interrogation of neurodevelopmental disorder-associated genes into the molecular analysis process of patients with dystonia. Recognizing the relationship between dystonia and neurodevelopmental disorders is important to improve patient counseling and management and develop novel therapeutic strategies.

While the functions of sleep remain largely unknown, one of the most exciting and contentious hypotheses is that sleep contributes importantly to memory. A large number of studies offer a substantive body of evidence supporting this role of sleep in what is becoming known as sleep-dependent memory processing. This review will provide evidence of sleep-dependent memory consolidation and sleep-dependent brain plasticity and is divided into five sections: (1) an overview of sleep stages, memory categories, and the distinct stages of memory development; (2) a review of the specific relationships between sleep and memory, both in humans and animals; (3) a survey of evidence describing sleep-dependent brain plasticity, including human brain imaging studies as well as animal studies of cellular neurophysiology and molecular biology. We close (4) with a consideration of unanswered questions as well as existing arguments against the role of sleep in learning and memory and (5) a concluding summary.

1591 Introduction: Molecular imaging of S1P1 expression in the brain provides an important biomarker for elucidating the pathophysiological roles of S1P1 in neuroinflammation and neurodegeneration. We previously reported the radiation dosimetry estimates for [11C]-CS1P1 using PET imaging and ex-vivo biodistribution in rodent. This report provides preliminary safety information from human whole-body biodistribution and dosimetry studies of [11C]-CS1P1 for its potential application in human brain imaging studies. 266.4 and 98.79 MBq of [11C]-CS1P1 was administered intravenously in two normal participants (one male and one female). Shortly after the administration of [11C]-CS1P1, whole body (WB) dynamic acquisition were initiated using Siemens Vision Biograph PET/CT (Siemens, Erlangen, Germany). The emission data were acquired for 180 minutes in 25 WB continuous bed motion sweeps from head to upper thighs. The time per sweeps were 120 s [for WB 1-10], 300 s [for WB 11-15], 600 s [for WB 16 and 20]) and 900 s [for WB 21 and 25]. A low-dose CT was performed before the PET emisision scan for PET attenuation correction and anatomical information. Scans were reconstructed using the ordered subset expectation maximization (OSEM) algorithm (4 iterations and 5 subsets) with a time of flight (TOF) and point spread function (PSF). Three-dimensional volumes of interest (VOIs) over entire organs were manually delineated on PET images using CT for anatomical localization. Eleven organs (lung, brain, kidney, gall bladder, spleen, liver, cardiac, stomach, urinary bladder, intestine and pancreas) were identified as source organs and remainder of the body that significantly took up the tracer and visually assessable amounts over the entire time frame of acquisition. Time-activity curves were obtained for each source organ by calculating the total activity (non decay corrected) in the VOIs and expressing them as the percentage of the total injected activity. Depending on the kinetics, different exponential models reflecting biodistribution and elimination of the tracer were used for different organs. The integrated-time-activity values were entered into a human biodistribution model Internal Dose Assessment by Computer (IDAC-Dose) (Ver. 2.1, Umea University, Sweden). The IDAC-Dose software calculates effective dose (ED) from the individual organ doses with radiation weighting factors and organ weighting factors for each of the source organs based on ICRP Publications 103 for male while ICRP Publications 60 for female. The residence time for all source organs are given in Table 1. The organ with the largest residence time was the liver with 0.064 h while the smallest was the urinary bladder with 0.00030 h. The organ total absorbed doses per MBq were largest for the liver (9.88 ± 0.03 µGy/ MBq), (Table 1). The total effective dose (ED) per MBq of injected activity of [11C]-CS1P1 in human is 1.60 ± 0.15 µSv/MBq. Based on the preliminary results, the radiation doses from PET studies are minimal and any biologically harmful effects on humans are unlikely. The total radiation burden depends largely on the initial organ perfusion and its retention until it clears out (larger effective half-life). Highly perfused organs such as heart, lung, liver, kidney, and brain receive a larger fraction of the absorbed dose due to larger effective half-life; whereas, less perfused organs such as muscle or fat receive a much smaller fraction. Most radiotracer molecules are likely to be cleared via either hepatobiliary (liver, gallbladder) or renal excretion (kidneys, urinary bladder). In our preliminary findings, the liver is a critical dose-limiting organ for [11C]-CS1P1. From our preliminary results, imaging with [11C]-CS1P1 for clinical PET studies is safe. Administration of 10 mCi (370 MBq) of [11C]-CS1P1 is expected to resulting in a total human effective dose of

In the late 1980s, fusions of advances in both neuroscience and information science gave birth to neuroinformatics with a core mission to informationize the complex brain structure and function and build the database. Three decades of neuroinformatics has witnessed the rapid progression on the in-vivo brain mapping technologies for monitoring and recording high-resolution spatial and temporal properties of such a complex informatic system non-invasively. Comparable to “Apollo Lunar Exploration” and “Manhattan Project” in the last century, brain projects became a national-wide arena for scientific frontiers in early 2000s. The most recent decade paralleled the remarkable development of the non-invasive and in-vivo human brain imaging technologies. Magnetic resonance imaging has made significant progresses on the basic research for understanding the human behavior and brain. This generated huge amount of in-vivo neuroimaging data and waved a new cycle of neuroinformatic research frontiers in life sciences, “open neuroscience” or “open brain science” featured by neuroimaging data sharing. In this review, we surveyed the history, state and future of neuroimaging data-sharing and the open brain science.

Low magnetic field systems provide an important opportunity to expand MRI to new and diverse clinical and research study populations. However, a fundamental limitation of low field strength systems is the reduced SNR compared to 1.5 or 3T, necessitating compromises in spatial resolution and imaging time. Most often, images are acquired with anisotropic voxels with low through-plane resolution, which provide acceptable image quality with reasonable scan times, but can impair visualization of subtle pathology.Here, we describe a super-resolution approach to reconstruct high-resolution isotropic TOur approach is demonstrated via phantom and in vivo human brain imaging, with simultaneous 1.5 × 1.5 × 1.5 mmOur multi-orientation and multi-TE approach is a time-efficient method for high-resolution T

Volume estimations are crucial for many neuroscience studies, allowing the evaluation of changes in the size of brain areas that may have relevant functional consequences. Classical histological methods and modern human brain imaging techniques rely on obtaining physical or digital sections, with a known thickness, of the organ to be analyzed. This “slicing” strategy is associated with an ineludible loss of information about the three-dimensional organization of the analyzed structures, especially affecting the precision of volumetric measurements. To overcome this problem, several methods have been developed. One of the most commonly used approaches for volume estimation is the classical Cavalieri’s method.Within this book chapter, we provide first an overview of Cavalieri’s method and propose a new one, named the Truncated Cone Shape method, for the estimation of volumes from tissue sections. Second, we compare the accuracy of both methods using computer-generated objects of different shapes and sizes. We conclude that, more frequently, the TCS method provides a better estimate of real volumes than Cavalieri’s method. And third, we describe a protocol to estimate volumes using a self-developed and freely available tool for ImageJ: VolumestJ. This new tool helps to implement both Cavalieri’s and TCS methods using digital images of tissue sections. We consider that VolumestJ will facilitate the labor of researchers interested in volume estimations.

Understanding the etiology and treatment approaches in schizophrenia is challenged in part by the heterogeneity of this disorder. One encouraging progress is the growing evidence that there are subtypes of schizophrenia. Recent in vitro findings of messenger ribonucleic acid (mRNA) gene expression on postmortem dorsolateral prefrontal cortex (DLPFC) showed that schizophrenia has two subtypes, those with a relatively normal DLPFC transcriptome (Type 1) and those with differentially expressed genes (Type 2). Sphingosine-1-phosphate receptor-1 (S1PR1) is one of the genes that was highly upregulated in Type 2 compared to Type 1 and controls. The impact of that finding is limited because it only can be confirmed through analysis of autopsy tissue, and the clinical characteristics such as symptoms severity or illness duration except for cause of death was not available from that Medical Examiner based autopsy study. However, S1PR1 has great potential because it is a target gene that can be accessed via positron emission tomography (PET) in vivo using specific radioligands (starting with [11C]CS1P1) successfully developed at our center in human brain imaging. As a preliminary study to validate this PET target in schizophrenia, S1PR1 protein expression was assessed by receptor autoradiography (ARG) using [3H]CS1P1 and immunohistochemistry (IHC) in the DLPFC from patients with schizophrenia classified as Type 1 or Type 2 based on their DLPFC transcriptomes and from controls. Our analyses demonstrate that ARG S1PR1 protein expression is significantly higher in Type 2 compared to Type 1 (p < 0.05) and controls (p < 0.05), which was consistent with previous mRNA S1PR1. These findings support the possibility that PET S1PR1 can be used as a future imaging biomarker to distinguish these subgroups of schizophrenic patients during life with obvious implications for both patient management and the design of clinical trials to validate novel pharmacologic therapies.

Introduction Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR. Methods Six [11C]PHNO PET-MR brain datasets were reconstructed with Q.Clear with β100–1000 (in increments of 100) and OSEM. The binding potential relative to non-displaceable volume (BPND) were obtained for the Substantia Nigra (SN), Striatum (St), Globus Pallidus (GP), Thalamus (Th), Caudate (Cd) and Putamen (Pt), using the MIAKAT™ software. Intraclass correlation coefficients (ICC), repeatability coefficients (RC), coefficients of variation (CV) and bias from Bland–Altman plots were reported. Statistical analysis was conducted using a 2-way ANOVA model with correction for multiple comparisons. Results When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43). Conclusion BPND results obtained from quantitative low count brain PET studies using [11C]PHNO and reconstructed with Q.Clear with β 18F]FDG whole-body studies, demonstrate the lowest bias versus the typical iterative reconstruction method OSEM.

The way people make decisions that have direct consequences to the self and others forms the moral foundation of human society. Although humans are at times impacted by social fairness norms and cooperation, economists have supposed that humans generally prioritize maximizing their own self-interest over others’. Recent psychological studies have shown people to be hyperaltruistic in social decisions, such as moral decision-making. Moral decision-making typically involves a trade-off between maintaining personal benefits and preventing harm to others. Previous human brain imaging studies have found similarities in neural activation between moral and economic decision-making under risk situations. Meanwhile, the way that people balance the gains and losses between themselves and others under social risk situations, especially the underlying neural mechanism, remains unclear. Reputational concern is a potentially important factor during human moral decision-making; that is, people are extremely concerned about how others perceive their behavior, which has a significant impact on prosocial behavior and motivation. Thus, this event-realted potential (ERP) study used the dilemma scenario-priming paradigm to explore how both self-relevance and reputational loss risk interact with behavioral and neural responses during moral decision-making. Participants were instructed to accomplish a moral decision-making task under reputational loss risk situations. Specifically, the participants needed to decide whether they were willing to sacrifice their own interests to help a protagonist (friend, acquaintance, or stranger) facing dilemmas of reputational loss risk (low to high risk). In addition to behavioral data and ERP analyses, we conducted spectral analysis to examine the neural oscillations underlying moral decision-making. Behavioral results showed that under low risk of reputational loss conditions, participants demonstrated a differential altruistic tendency, indicating that participants made more helping choices and subsequently reported weaker unpleasant experiences toward friends compared with acquaintances and strangers. Nonetheless, the participants showed an obvious “acquaintance effect”, suggesting that participants took longer time to make decisions and experienced stronger displeasure. Electrophysiological results revealed that moral decision-making toward acquaintances under low reputational loss risk conditions also elicited larger P260-N2-LPP (300–450 ms) effects during emotional and cognitive processing, compared with friends and strangers. Spectral analysis results showed that the delta- (1–4 Hz, 200–300 ms) and alpha-band powers (8–13 Hz, 300–600 ms) for moral decision-making toward acquaintances were significantly larger compared with the case for friends and strangers, under low risk of reputational loss conditions. However, these effects of all indices weakened or disappeared under high risk of reputational loss conditions. The present results suggested that the moral principles that guide interpersonal moral decision-making can best be described as egoistically biased altruism, and that uncertainty in interpersonal relationships brings emotional and cognitive burden to moral decision-making. Moreover, reputational concerns may play a crucial role in limiting selfish tendencies and eliminating uncertainties.

Due to unique characteristics, umami substances have gained much attention in the food industry during the past decade as potential replacers to sodium or fat to increase food palatability. Umami is not only known to increase appetite, but also to increase satiety, and hence could be used to control food intake. Therefore, it is important to understand the mechanism(s) involved in umami taste perception. This review discusses current knowledge of the mechanism(s) of umami perception from receptor level to human brain imaging. New findings regarding the molecular mechanisms for detecting umami tastes and their pathway(s), and the peripheral and central coding to umami taste are reviewed. The representation of umami in the human brain and the individual variation in detecting umami taste and associations with genotype are discussed. The presence of umami taste receptors in the gastrointestinal tract, and the interactions between the brain and gut are highlighted. The review concludes that more research is required into umami taste perception to include not only oral umami taste perception, but also the wider “whole body” signaling mechanisms, to explore the interaction between the brain and gut in response to umami perception and ingestion.

Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning. However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs). To answer this question, we propose a novel computational framework, Synchronized Activations (Sync-ACT), to couple the visual representation spaces and semantics between ANNs and BNNs in human brain based on naturalistic functional magnetic resonance imaging (nfMRI) data. With this approach, we are able to semantically annotate the neurons in ANNs with biologically meaningful description derived from human brain imaging for the first time. We evaluated the Sync-ACT framework on two publicly available movie-watching nfMRI datasets. The experiments demonstrate a) the significant correlation and similarity of the semantics between the visual representations in FBNs and those in a variety of convolutional neural networks (CNNs) models; b) the close relationship between CNN's visual representation similarity to BNNs and its performance in image classification tasks. Overall, our study introduces a general and effective paradigm to couple the ANNs and BNNs and provides novel insights for future studies such as brain-inspired artificial intelligence.

Collaboration between disciplines is necessary when research questions cannot be answered within a single discipline. Joining of forces can produce results that neither discipline could provide alone. Here we exemplify collaboration between a ceramic craft researcher and three neuroscientists working in the field of human brain imaging. In our case study of clay throwing, the researcher–practitioner’s eye gaze, muscular activity and hand acceleration were recorded online, synchronized with video and thermal-camera recordings. We describe the experimental setting and discuss, besides the possible future interests in this kind of research, also the different levels of collaborative work between disciplines. We found that the monitoring methods worked well in the naturalistic setting in a ceramic studio, providing some new perspectives into the craft practice. For neuroscientists, clay throwing – involving accurate sensorimotor hand control, haptics and eye–hand coordination – provides an attractive setup to extend previous neuroscientific and behavioural findings in strictly controlled laboratory experiments into naturalistic situations. The applied monitoring devices might allow practitioner–researchers in crafts to become aware of unconscious steps in the making process. The applied methods could also help to accumulate general craft-making knowledge and build related theory.

Combination of Magnetic Resonance Imaging (MRI) and Dissolution Dynamic Nuclear Polarisation (dDNP) promises faster diagnosis and treatment of cancer. To make the technology clinically viable, radio-frequency receivers with high Signal-to-Noise Ratio (SNR) are needed. This work investigates three aspects of radio-frequency receiver design: theoretical limits of preamplifier decoupling between receiver elements, matching network design, and cryogenic coil arrays. Preamplifier decoupling is a technique used widely to avoid de-tuning coil elements in coil arrays. To investigate preamplifier matching/decoupling, the quantitative relation between preamplifier Noise Figure (NF) and preamplifier decoupling is established using preamplifier parameters. Closed-form design equations for matching circuits with arbitrary preamplifier input impedance are derived. This theory relaxes restrictions on preamplifiers and greatly improves design flexibility. The theory is confirmed by numerical examples and experimentally tested in preamplifiers at 32.13 MHz. The potential of SNR improvement of a single coil is investigated, and compared with the SNR loss of different matching network designs. A mouse coil is matched by two types of matching networks at room and cryogenic temperatures, and compared in regard to output SNR. It is found that loss of matching circuits have higher influence on the SNR than the temperature of coils and matching circuits. Experiments reveal that an improper design of matching network reduces SNR by 7.9 dB, whereas cryogenic cooling only compensates for 2.0–2.6 dB. This stresses the importance of careful matching network designs. Finally, a cryogenic 14-channel array for human brain imaging is constructed to investigate SNR improvement of coil arrays by cryogenic cooling. A cryostat for human head that offers reliable cooling for approximately 1 h and decent thermal insulation is built. Electronic circuits are adapted to cryogenic temperature and strong static magnetic field operation. The MR image SNR when using the array at cryogenic temperature is found 27%–168% higher than at room temperature, with SNR improvement 47% at the image centre. Thus, it is demonstrated that SNR can increase by cryogenically cooling an array.

DNA methylation (DNAM) changes in the FKBP5 gene have been identified as a potential molecular mechanism explaining how environmental adversity may confer long-term health risks. However, the neurobiological correlates of epigenetic signatures in FKBP5 have only recently been explored in human brain imaging research. The present study aims to investigate associations of FKBP5 DNAM and functional network architecture during an implicit emotion regulation task (N = 74 healthy individuals). For this, we applied a data-driven multi-voxel pattern analysis (MVPA) to identify regions, where connectivity values vary as a function of FKBP5 DNAM, which then served as seed regions for functional network architecture analyses. Blood-derived DNA samples were obtained to analyze quantitative DNAM at three CpGs sites in intron 7 of the FKBP5 gene using bisulfite pyrosequencing. MPVA revealed a cluster within the right rostral ACC and the paracingulate ACCs, where connectivity patterns were strongly related to FKBP5 DNAM. Using this cluster as seed region for connectivity analyses, we further identified a functional network, including prefrontal, subcortical, insular, and thalamic regions, where connectivity patterns positively correlated with FKBP5 DNAM. A subsequent behavioral domain analyses to determine the functional specialization of this network revealed highest effect sizes for subdomains that represent affective and cognitive processes. Together, these findings suggest that FKBP5 demethylation predicts a widespread functional disruption in a brain network centrally implicated in emotion regulation and cognition, which may in turn convey increased disease susceptibility.

Dissertação de mestrado integrado em Engenharia Informática, Neurodegenerative diseases impair the functioning of the brain and are characterized by alterations in the morphology of specific brain regions. Some of the main disorders include Alzheimer's, Parkinson's, and Huntington's diseases, and the number of cases increases exponentially since ageing is one of the main risk factors. Trying to identify the areas in which this type of disease appears is something that can have a very positive impact in this area of Medicine and can guarantee a more appropriate treatment or allow the improvement of the quality of life of patients. With the current technological advances, computer tools are capable of performing a structural or functional analysis of neuroimaging data from Magnetic Resonance Images(MRI). Therefore, Medical Informatics uses these techniques to create and manage medical neuroimaging data to improve the diagnosis and management of these patients. MRI is the image type used in the analysis of the brain area and points to a promising and reliable diagnostic tool since it allows high-quality images in various planes or strategies and MRI methods are fundamental diagnostic tools in clinical practice, allowing the diagnosis of pathologic processes such as stroke or brain tumours. However, structural MRI has limitations for the diagnosis of neurodegenerative disorders since it mainly identifies atrophy of brain regions. Currently, there is increased interest in informatics applications capable of monitoring and quantifying human brain imaging alterations, with potential for neurodegenerative disorders diagnosis and monitoring. One of these applications is Radiomics, which corresponds to a methodolog ythat allows the extraction of features from images of a given region of the brain. Specific quantitative metrics from MRI are acquired by this tool, and they correspond to a set of features, including texture, shape, among others. To standardize Radiomics application, specific libraries have been proposed to be used by the bioinformatics and biomedical communities, such as PyRadiomics, which corresponds to an open source Python package for extracting Radiomics of MRIs. Therefore, this dissertation was developed based on magnetic resonance images and the study of Deep Learning (DL) techniques to assist researchers and neuroradiologists in the diagnosis and prediction of neurodegenerative disease development. Two different main tasks were made: first, a segmentation, using FreeSurfer, of different regions of the brain and then, a model was build from radiomic features extracted from each part of the brain and interpreted for knowledge extraction., As doenças neurodegenerativas estão associadas ao funcionamento do cérebro e caracterizam-se pelo facto de serem altamente incapacitantes. São exemplos destas, as doenças de Alzheimer, Parkinson e Huntington, e o seu número de casos tem vindo a aumentar exponencialmente, uma vez que o envelhecimento é um dos principais factores de risco. Tentar identificar quais são as regiões cerebrais que permitem predizer o seu aparecimento e desenvolvimento é algo que, sendo possível, terá um impacto muito positivo nesta área da Medicina e poderá garantir um tratamento mais adequado, ou simplesmente melhorar a qualidade de vida dos pacientes. Com os avanços tecnológicos atuais, foram desenvolvidas ferramentas informáticas que são capazes de efetuar uma análise estrutural ou funcional de Ressonâncias Magnéticas (MRI), sendo essas ferramentas usadas para promover a melhoria e o conhecimento clínico. Deste modo, as constantes evoluções científicas têm realçado o papel da Informática Médica na neuroimagem para criar e gerenciar dados médicos, melhorando o diagnóstico destes pacientes. A MRI é o tipo de imagem utilizada na análise de regiões cerebrais e aponta para uma ferramenta de diagnóstico promissora e fiável, uma vez que permite obter imagens de alta qualidade em vários planos, permitindo assim, o diagnóstico de processos patológicos, tais como acidentes vasculares ou tumores cerebrais. Atualmente, existem inúmeras aplicações informáticas capazes de efetuar análises estruturais e funcionais do cérebro humano, pois é este o principal órgão afetado pelas doenças neurodegenerativas. Uma dessas aplicações é o Radiomics, que permite fazer a extração de features de imagens do cérebro. A biblioteca a utilizar será PyRadiomics, que corresponde a um package open source em Python para a extração de features Radiomics de imagens médicas. As features correspondem a características da imagem. Assim sendo, a presente dissertação foi desenvolvida com base em imagens de ressonância magnética e no estudo das técnicas de Deep Learning para investigar e auxiliar os médicos neurorradiologistas a diagnosticar e a prever o desenvolvimento de doenças neurodegenerativas. Foram feitas duas principais tarefas: primeiro, uma segmentação, utilizando o software FreeSurfer, de diferentes regiões do cérebro e, de seguida, foi construído um modelo a partir das features radiómicas extraídas de cada parte do cérebro que foi interpretado.

Objective:Elevated noise levels in positron emission tomography (PET) images lower image quality and quantitative accuracy and are a confounding factor for clinical interpretation. The objective of this paper is to develop a PET image denoising technique based on unsupervised deep learning.Significance:Recent advances in deep learning have ushered in a wide array of novel denoising techniques, several of which have been successfully adapted for PET image reconstruction and post-processing. The bulk of the deep learning research so far has focused on supervised learning schemes, which, for the image denoising problem, require paired noisy and noiseless/low-noise images. This requirement tends to limit the utility of these methods for medical applications as paired training datasets are not always available. Furthermore, to achieve the best-case performance of these methods, it is essential that the datasets for training and subsequent real-world application have consistent image characteristics (e.g. noise, resolution, etc), which is rarely the case for clinical data. To circumvent these challenges, it is critical to develop unsupervised techniques that obviate the need for paired training data.Approach:In this paper, we have adapted Noise2Void, a technique that relies on corrupt images alone for model training, for PET image denoising and assessed its performance using PET neuroimaging data. Noise2Void is an unsupervised approach that uses a blind-spot network design. It requires only a single noisy image as its input, and, therefore, is well-suited for clinical settings. During the training phase, a single noisy PET image serves as both the input and the target. Here we present a modified version of Noise2Void based on a transfer learning paradigm that involves group-level pretraining followed by individual fine-tuning. Furthermore, we investigate the impact of incorporating an anatomical image as a second input to the network.Main Results:We validated our denoising technique using simulation data based on the BrainWeb digital phantom. We show that Noise2Void with pretraining and/or anatomical guidance leads to higher peak signal-to-noise ratios than traditional denoising schemes such as Gaussian filtering, anatomically guided non-local means filtering, and block-matching and 4D filtering. We used the Noise2Noise denoising technique as an additional benchmark. For clinical validation, we applied this method to human brain imaging datasets. The clinical findings were consistent with the simulation results confirming the translational value of Noise2Void as a denoising tool.

The medial frontal cortex (MFC), especially its ventral part, has long been of great interest with respect to the pathology of mood disorders. A number of human brain imaging studies have demonstrated the abnormalities of this brain region in patients with mood disorders, however, whether it is critically and causally involved in the pathogenesis of such disorders remains to be fully elucidated. In this study, we examined how the suppression of neural activity in the ventral region of the MFC (vMFC) affects the behavioral and physiological states of monkeys by using repetitive transcranial magnetic stimulation (rTMS). By using low-frequency rTMS (LF-rTMS) as an inhibitory intervention, we found that LF-rTMS targeting the vMFC temporarily induced a depression-like state in monkeys, which was characterized by a reduced movement activity level, impaired sociability, and decreased motivation level, as well as increased plasma cortisol level. On the other hand, no such significant changes in behavioral and physiological states were observed when targeting the other MFC regions, dorsal or posterior. We further found that the administration of an antidepressant agent, ketamine, ameliorated the abnormal behavioral and physiological states induced by the LF-rTMS intervention. These findings causally indicate the involvement of the vMFC in the regulation of mood and the validity of the LF-rTMS-induced dysfunction of the vMFC as a nonhuman primate model of depression.

"Phospholipases (PLA2s) are a superfamily of enzymes characterized by the ability to specifically hydrolyze the sn-2 ester bond of phospholipids generating arachidonic acid, utilized in inflammatory responses, and lysophospholipids involved in the control of cell membrane remodeling and fluidity. PLA2s have been so far considered a crucial element in the etiopathogenesis of several neurological diseases such as cerebral ischemia, multiple sclerosis, Parkinson's disease, and Alzheimer's disease (AD). In AD, the role of beta-amyloid (Aβ) fragments is well established although still more elusive are the molecular events of the cascade that from the Aβ accumulation leads to neurodegeneration with its clinical manifestations. However, it is well known that inflammation and alteration of lipid metabolism are common features of AD brains. Findings obtained from in vitro studies, animal models, and human brain imaging analysis point towards cPLA2 as a key molecule in the onset and maintenance of the neurodegenerative mechanism(s) of AD. In this review, we have focused on the molecular and biological evidence of the involvement of cPLA2s in the pathogenesis of AD. An insight into the molecular mechanism(s) underlying the action and the regulation of cPLA2 is of tremendous interest in the pharmaceutical and biotechnology industry in developing selective and potent inhibitors able to modulate the onset and\/or the outcome of AD." Phospholipases (PLA2s) are a superfamily of enzymes characterized by the ability to specifically hydrolyze the sn-2 ester bond of phospholipids generating arachidonic acid, utilized in inflammatory responses, and lysophospholipids involved in the control of cell membrane remodeling and fluidity. PLA2s have been so far considered a crucial element in the etiopathogenesis of several neurological diseases such as cerebral ischemia, multiple sclerosis, Parkinson's disease, and Alzheimer's disease (AD). In AD, the role of beta-amyloid (A beta) fragments is well established although still more elusive are the molecular events of the cascade that from the A beta accumulation leads to neurodegeneration with its clinical manifestations. However, it is well known that inflammation and alteration of lipid metabolism are common features of AD brains. Findings obtained from in vitro studies, animal models, and human brain imaging analysis point towards cPLA2 as a key molecule in the onset and maintenance of the neurodegenerative mechanism(s) of AD. In this review, we have focused on the molecular and biological evidence of the involvement of cPLA2s in the pathogenesis of AD. An insight into the molecular mechanism(s) underlying the action and the regulation of cPLA2 is of tremendous interest in the pharmaceutical and biotechnology industry in developing selective and potent inhibitors able to modulate the onset and/or the outcome of AD.

Learning about potential threats in the environment is indispensable for survival. Deficits in threat learning constitute a key dimension of multiple brain disorders, which include posttraumatic stress disorder and anxiety disorder. While human brain imaging studies have highlighted a reliable engagement of the anterior insular cortex (AIC) in threat learning, its precise role remains elusive partly due to the lack of animal studies that can address causality and mechanistic questions. Filling in this gap, the present mouse study proposes a novel AIC-mediated mechanism underlying the association of temporally discontiguous stimuli during threat learning. We identified that activity of AIC layer 5 (L5) pyramidal neurons is required for associating temporally discontiguous stimuli, specifically during a time interval between them. Notably, the AIC is not required for associating temporally contiguous stimuli during threat learning. The AIC not only sends the essential information, via its L5 pyramidal neurons, to the basolateral amygdala (BLA) during the time interval, but also receives from the BLA. We also identified a modulatory role of AIC dopamine D1 receptor (D1R)-mediated dopamine signaling in associating temporally discontiguous stimuli during the time interval.

For ultra-high field and frequency (UHF) magnetic resonance imaging (MRI), the associated short wavelengths in biological tissues leads to penetration and homogeneity issues at 10.5 tesla (T) and require antenna transmit arrays for efficiently generated 447 MHz B1+ fields (defined as the transmit radiofrequency (RF) magnetic field generated by RF coils). Previously, we evaluated a 16-channel combined loop + dipole antenna (LD) 10.5 T head array. While the LD array configuration did not achieve the desired B1+ efficiency, it showed an improvement of the specific absorption rate (SAR) efficiency compared to the separate 8-channel loop and separate 8-channel dipole antenna arrays at 10.5 T. Here we compare a 16-channel dipole antenna array with a 16-channel LD array of the same dimensions to evaluate B1+ efficiency, 10 g SAR, and SAR efficiency. The 16-channel dipole antenna array achieved a 24% increase in B1+ efficiency in the electromagnetic simulation and MR experiment compared to the LD array, as measured in the central region of a phantom. Based on the simulation results with a human model, we estimate that a 16-channel dipole antenna array for human brain imaging can increase B1+ efficiency by 15% with similar SAR efficiency compared to a 16-channel LD head array.

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit feedforward computational models of visual feature coding to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that feedforward models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to seemingly high-level preferences for scenes and for objects that are large, spatially fixed, and inanimate/manmade while simultaneously yielding low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of feedforward feature coding, and they demonstrate how multifaceted selectivity profiles naturally emerge from the feedforward computations of visual cortex and the natural statistics of images.SIGNIFICANCE STATEMENTVisual neuroscientists characterize cortical selectivity by identifying stimuli that drive regional responses. A perplexing finding is that many higher-order visual regions exhibit selectivity profiles spanning multiple levels of complexity: they respond to highly complex categories, such as scenes and landmarks, but also to surprisingly simplistic features, such as specific contour orientations. Using large-scale computational analyses and human brain imaging, we show how multifaceted selectivity in scene-preferring cortex can emerge from the feedforward, hierarchical coding of visual features. Our work reconciles seemingly divergent findings of selectivity in scene-preferring cortex and suggests that surprisingly simple feedforward feature representations may be central to the category-selective organization of the human visual system.

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.GRAPHICAL ABSTRACTThis paper demonstrates how Monte Carlo Arithmetic, a dataagnostic perturbation technique, can be used for dataset augmentation during the generation of structural connectomes and improve downstream phenotypic prediction.

Activity at rest provides valuable information about functional organization of brain networks. However, lack of cellular resolution in human brain imaging techniques precludes analysis of some of the key properties of resting state networks. Here we used a large-scale c-Fos imaging of resting state neuronal activity in the mouse brain combined with graph analysis methods to get deeper insights into structure and functional significance of resting state brain networks. We compared experimentally identified networks with model networks having the same parameters of edges and nodes: random, scale free and small world networks, using parameters of clustering and global efficiency. Clustering of experimental networks was at the same level as in scale free network – the number of clusters in experimental networks exceeded the random level. However, global efficiency analysis revealed that these clusters have weak interactions with each other or even no interaction at all – thus the global efficiency of experimental networks was at the same level as of a random network. We also found that a single strong traumatic experience can lead to global and long-term changes in the structure of resting state networks. The resting state network of mice which underwent traumatic experience had greater entropy than that of naive mice, especially in the zone of increased influence of strong correlations. We suggest that these changes reflect replay of neuronal assemblies involved in the states of past experience.

Abstract Background Dynamically altered microglia play an important role in the progression of Alzheimer’s disease (AD). Here, we found a close association of the metabolic reconfiguration of microglia with increased hippocampal glucose uptake on [18F]fluorodeoxyglucose (FDG) PET. Methods We used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer’s Disease Neuroimaging Initiative were analyzed. FDG uptake in the hippocampus was compared according to the diagnosis, AD, mild cognitive impairment, and controls. The correlation analysis between hippocampal FDG uptake and soluble TREM2 in cerebrospinal fluid was performed. Results In the animal study, 8- and 12-month-old 5xFAD mice showed higher FDG uptake in the hippocampus than wild-type mice. Cellular FDG uptake tests showed that FDG activity in hippocampal microglia was increased in the AD model, while FDG activity in non-microglial cells of the hippocampus was not different between the AD model and wild-type. scRNA-seq data showed that changes in glucose metabolism signatures including glucose transporters, glycolysis and oxidative phosphorylation, mainly occurred in microglia. A subset of microglia with higher glucose transporters with defective glycolysis and oxidative phosphorylation was increased according to disease progression. In the human imaging study, we found a positive association between soluble TREM2 and hippocampal FDG uptake. FDG uptake in the hippocampus at the baseline scan predicted mild cognitive impairment conversion to AD. Conclusions We identified the reconfiguration of microglial glucose metabolism in the hippocampus of AD, which could be evaluated by FDG PET as a feasible surrogate imaging biomarker for microglia-mediated inflammation.

Q.Clear is a Bayesian penalised likelihood (BPL) reconstruction algorithm available on General Electric (GE) Positron Emission Tomography (PET)-Computed Tomography (CT) and PET-Magnetic Resonance (MR) scanners. This algorithm is regulated by a β value which acts as a noise penalisation factor and yields improvements in signal to noise ratio (SNR) in clinical scans, and in contrast recovery and spatial resolution in phantom studies. However, its performance in human brain imaging studies remains to be evaluated in depth. This pilot study aims to investigate the impact of Q.Clear reconstruction methods using different β value versus ordered subset expectation maximization (OSEM) on brain kinetic modelling analysis of low count brain images acquired in the PET-MR.Six [When comparing a standard OSEM reconstruction of 6 iterations/16 subsets and 5 mm filter with Q.Clear with different β values under low counts, the bias and RC were lower for Q.Clear with β100 for the SN (RC = 2.17), Th (RC = 0.08) and GP (RC = 0.22) and with β200 for the St (RC = 0.14), Cd (RC = 0.18)and Pt (RC = 0.10). The p-values in the 2-way ANOVA model corroborate these findings. ICC values obtained for Th, St, GP, Pt and Cd demonstrate good reliability (0.87, 0.99, 0.96, 0.99 and 0.96, respectively). For the SN, ICC values demonstrate poor reliability (0.43).BP

Open Science is calling for a radical re-thinking of existing scientific practices. Within the neuroimaging community, Open Science practices are taking the form of open data repositories and open lab notebooks. The broad sharing of data that accompanies Open Science, however, raises some difficult ethical and legal issues. With neuroethics as a focusing lens, we explore eight central concerns posed by open data with regard to human brain imaging studies: respect for individuals and communities, concern for marginalized communities, consent, privacy protections, participatory research designs, contextual integrity, fusions of clinical and research goals, and incidental findings. Each consideration assists in bringing nuance to the potential benefits for open data sharing against associated challenges. We combine current understandings with forward-looking solutions to key issues. We conclude by underscoring the need for new policy tools to enhance the potential for responsible open data.

Nearly a century ago it was reported that stimulation of the hypothalamus could evoke profound behavioral state changes coupled with altered autonomic function. Since these initial observations, further studies in animals have revealed that two hypothalamic regions-the dorsomedial and ventromedial hypothalamic nuclei-are critical for numerous behaviors, including those in response to psychological stressors. These behaviors are coupled with changes in autonomic functions, such as altered blood pressure, heart rate, sympathetic nerve activity, resetting of the baroreflex and changes in pituitary function. There is also growing evidence that these two hypothalamic regions play a critical role in thermogenesis, and suggestions they could also be responsible for the hypertension associated with obesity. The aim of this chapter is to review the anatomy, projection patterns, and function of the dorsomedial and ventromedial hypothalamus with a particular focus on their role in autonomic regulation. While most of what is known about these two hypothalamic regions is derived from laboratory animal experiments, recent human studies will also be explored. Finally, we will describe recent human brain imaging studies that provide evidence of a role for these hypothalamic regions in setting resting sympathetic drive and their potential role in conditions such as hypertension.

Attention-deficit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder in children. Although animal models and human brain imaging studies indicate a significant role for glutamatergic dysfunction in ADHD, there is no direct evidence that glutamatergic dysfunction is sufficient to induce ADHD-like symptoms. The glial glutamate transporter GLT1 plays a critical role in glutamatergic neurotransmission. We report here the generation of mice expressing only 20% of normal levels of the GLT1. Unlike conventional GLT1 knockout mice, these mice survive to adulthood and exhibit ADHD-like phenotypes, including hyperactivity, impulsivity and impaired memory. These findings indicate that glutamatergic dysfunction due to GLT1 deficiency, a mechanism distinct from the dopaminergic deficit hypothesis of ADHD, underlies ADHD-like symptoms.

Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging of the human brain requires bulky equipment for the generation of magnetic fields. Photoacoustic computed tomography obviates the need for magnetic fields by using light and sound to measure deoxyhaemoglobin and oxyhaemoglobin concentrations to then quantify oxygen saturation and blood volumes. Yet, the available imaging speeds, fields of view (FOV), sensitivities and penetration depths have been insufficient for functional imaging of the human brain. Here, we show that massively parallel ultrasonic transducers arranged hemispherically around the human head can produce tomographic images of the brain with a 10-cm-diameter FOV and spatial and temporal resolutions of 350 µm and 2 s, respectively. In patients who had a hemicraniectomy, a comparison of functional photoacoustic computed tomography and 7 T BOLD functional magnetic resonance imaging showed a strong spatial correspondence in the same FOV and a high temporal correlation between BOLD signals and photoacoustic signals, with the latter enabling faster detection of functional activation. Our findings establish the use of photoacoustic computed tomography for human brain imaging. Massively parallel ultrasonic transducers arranged hemispherically around the human head enable functional photoacoustic computed tomography of the human brain with a 10-cm-diameter field of view and resolutions of 350 µm and 2 s.

The medial frontal cortex (MFC), especially its ventral part, has long been of great interest with respect to the pathology of mood disorders. A number of human brain imaging studies have demonstrated the abnormalities of this brain region in patients with mood disorders, however, whether it is critically involved in the pathogenesis of such disorders remains to be fully elucidated. In this study, we conducted a causal study to investigate how the suppression of neural activity in the ventral region of the MFC (vMFC) affects the behavioral and physiological states of monkeys by using repetitive transcranial magnetic stimulation (rTMS). By using low-frequency rTMS (LF-rTMS) as an inhibitory intervention, we found that LF-rTMS targeting the vMFC induced a depression-like state in monkeys, which was characterized by a reduced spontaneous behavioral activity, increased plasma cortisol level, impaired sociability, and decreased motivation level. On the other hand, no such significant changes in behavioral and physiological states were observed when targeting the other MFC regions, dorsal or posterior. We further found that the administration of an antidepressant agent, ketamine, ameliorated the abnormal behavioral and physiological states induced by the LF-rTMS intervention. These findings indicate the causal involvement of the vMFC in the regulation of mood and affect and the validity of the LF-rTMS-induced dysfunction of the vMFC as a nonhuman primate model of the depression-like state.

Due to unique characteristics, umami substances have gained much attention in the food industry during the past decade as potential replacers to sodium or fat to increase food palatability. Umami is not only known to increase appetite, but also to increase satiety, and hence could be used to control food intake. Therefore, it is important to understand the mechanism(s) involved in umami taste perception. This review discusses current knowledge of the mechanism(s) of umami perception from receptor level to human brain imaging. New findings regarding the molecular mechanisms for detecting umami tastes and their pathway(s), and the peripheral and central coding to umami taste are reviewed. The representation of umami in the human brain and the individual variation in detecting umami taste and associations with genotype are discussed. The presence of umami taste receptors in the gastrointestinal tract, and the interactions between the brain and gut are highlighted. The review concludes that more research is required into umami taste perception to include not only oral umami taste perception, but also the wider "whole body" signaling mechanisms, to explore the interaction between the brain and gut in response to umami perception and ingestion.

Understanding the etiology and treatment approaches in schizophrenia is challenged in part by the heterogeneity of this disorder. One encouraging progress is the growing evidence that there are subtypes of schizophrenia that may relate to disease duration and premorbid severity. Recent in vitro findings of messenger ribonucleic acid (mRNA) gene expression on postmortem dorsolateral prefrontal cortex (DLPFC) showed that schizophrenia has two subtypes, those with a relatively normal DLPFC transcriptome (Type 1) and those with differentially expressed genes (Type 2). Sphingosine-1-phosphate receptor-1 (S1PR1) is one of the genes that was highly upregulated in Type 2 compared to Type 1 and controls. The impact of that finding is limited because it only can be confirmed through analysis of autopsy tissue, and the clinical characteristics such as symptoms severity or illness duration was not available from that Medical Examiner based autopsy study. However, S1PR1 has great potential because it is a target gene that can be accessed via positron emission tomography (PET) in vivo using specific radioligands (starting with [11C]CS1P1) successfully developed at our center in human brain imaging. As a preliminary study to validate this PET target in schizophrenia, S1PR1 protein expression was assessed by receptor autoradiography (ARG) using [3H]CS1P1 and immunohistochemistry (IHC) in the DLPFC from patients with schizophrenia classified as Type 1 or Type 2 based on their DLPFC transcriptomes and from controls. Our analyses demonstrate that ARG S1PR1 protein expression is significantly higher in Type 2 compared to Type 1 (p < 0.05) and controls (p < 0.05), which was consistent with previous mRNA S1PR1. These findings support the possibility that PET S1PR1 can be used as a future imaging biomarker to distinguish these subgroups of schizophrenic patients during life with obvious implications for both patient management and the design of clinical trials to validate novel pharmacologic therapies.

Background Preventing transition to chronic back pain (CBP) is a long-sought strategy that could rescue patients from prolonged suffering. Recent rodent and human brain imaging studies suggest involvement of sexually dimorphic, dopaminergic-motivational, mesolimbic circuits in the transition to chronic pain (tCBP), and hint that the combination of carbidopa/levodopa and naproxen (LDP + NPX) may block tCBP. Here we evaluated, in people with recent-onset back pain, whether a 3-month treatment with LDP + NPX is safe, blocks tCBP, and whether its efficacy is sex-dependent. Methods A total of 72 participants were enrolled and stratified by risk for tCBP using brain-imaging biomarkers. Low-risk participants entered a no-treatment arm. Others were randomized to placebo + naproxen or LDP + NPX for 3 months. Results Both treatments resulted in more than 50% pain relief for approximately 75% of participants. A strong sex by treatment interaction was observed for daily pain intensity (phone NRS, P = 0.007), replicated on 4-week average pain (Pain/4w, P = 0.00001), and in intent-to-treat analysis (Pain/4w, P = 0.000004). Nucleus accumbens functional connectivity with medial prefrontal cortex, a predefined objective biomarker, showed sex dependence at baseline (P = 0.03) and sex-by-treatment interaction effect 3 months after treatment cessation (P = 0.031). Treatment modified the psychological profile of participants, and disrupted brain modeling-based predicted back pain intensity trajectories. Forty participants were queried 3.3 years from trial start; back pain ratings were similar between end of treatment and at 3.3 years (P = 0.62), indicating persistence of relief for this duration. Conclusions These results provide the first evidence for preventing transition to chronic back pain using sex-specific pharmacotherapy. These provocative observations require confirmation in a larger study. ClinicalTrials.gov identifier: NCT01951105. Supplementary Information The online version contains supplementary material available at 10.1007/s40122-021-00297-2.

PurposeInhomogeneous magnetization transfer (ihMT) is an emerging form of MRI contrast that may offer high specificity for myelinated tissue. Existing ihMT and pulsed MT sequences often use separate radiofrequency pulses for saturation and signal excitation. This study investigates the use of nonselective multiband radiofrequency pulses for simultaneous off‐resonance saturation and on‐resonance excitation specifically for generation of ihMT contrast within rapid steady‐state pulse sequences.Theory and MethodsA matrix‐based signal modeling approach was developed and applied for both balanced steady state free precession and spoiled gradient echo sequences, accounting specifically for multiband pulses. Phantom experiments were performed using a combination of balanced steady state free precession and spoiled gradient echo sequences, and compared with model fits. A human brain imaging exam was performed using balanced steady state free precession sequences to demonstrate the achieved contrast.ResultsA simple signal model derived assuming instantaneous radiofrequency pulses was shown to agree well with full integration of the governing equations and provided fits to phantom data for materials with strong ihMT contrast (PL161 root mean square error = 0.9%, and hair conditioner root mean square error = 2.4%). In vivo ihMT ratio images showed the expected white matter contrast that has been seen by other ihMT investigations, and the observed ihMT ratios corresponded well with predictions.ConclusionsihMT contrast can be generated by integrating multiband radiofrequency pulses directly into both spoiled gradient echo and balanced steady state free precession sequences, and the presented signal modeling approach can be used to understand the acquired signals.

Sirui Li, Lei Gao, Ying Liu, Yawen Ao, Haibo Xu Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, People’s Republic of China Background: The thalamus is an important deep brain structure for the synchronization of brain rhythm and the integration of cortical activity. Human brain imaging and computational modeling have non-invasively revealed its role in maintaining the cortical network architecture and functional hierarchy. Purpose: The objective of this study was to identify the effect of unilateral thalamic damage on the human brain intrinsic functional architecture. Patients and methods: We collected an 8-minute resting-state functional magnetic resonance imaging (R-fMRI) data on a 3.0 T magnetic resonance scanner for all the participants: a preoperative patient with left thalamus destroyed by anaplastic astrocytoma (WHO grade III type of astrocytoma) and 20 matched healthy controls. The R-fMRI data was analyzed for functional connectivity and amplitude of spontaneous fluctuations. Results: The patient showed prominent decrease in functional connectivity within primary sensory networks and advanced cognitive networks, and extensive alterations in between-network coupling. Further analysis of the amplitude of spontaneous activity suggested significant decrease especially in the topographies of default mode network and the Papez circuit. Conclusion: This result provided evidence about the consequences of thalamic destruction on the correlation and landscape of spontaneous brain activity, promoting our understanding of the effects of thalamic damage on large-scale brain networks. Keywords: brain networks, functional connectivity, default mode network, Papez circuit

Background The thalamus is an important deep brain structure for the synchronization of brain rhythm and the integration of cortical activity. Human brain imaging and computational modeling have non-invasively revealed its role in maintaining the cortical network architecture and functional hierarchy. Purpose The objective of this study was to identify the effect of unilateral thalamic damage on the human brain intrinsic functional architecture. Patients and methods We collected an 8-minute resting-state functional magnetic resonance imaging (R-fMRI) data on a 3.0 T magnetic resonance scanner for all the participants: a preoperative patient with left thalamus destroyed by anaplastic astrocytoma (WHO grade III type of astrocytoma) and 20 matched healthy controls. The R-fMRI data was analyzed for functional connectivity and amplitude of spontaneous fluctuations. Results The patient showed prominent decrease in functional connectivity within primary sensory networks and advanced cognitive networks, and extensive alterations in between-network coupling. Further analysis of the amplitude of spontaneous activity suggested significant decrease especially in the topographies of default mode network and the Papez circuit. Conclusion This result provided evidence about the consequences of thalamic destruction on the correlation and landscape of spontaneous brain activity, promoting our understanding of the effects of thalamic damage on large-scale brain networks.

11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) catalyzes enzymatic conversion of cortisone into the stress hormone cortisol. This first-in-human brain imaging study characterizes the kinetic modeling and test–retest reproducibility of [18F]AS2471907, a novel PET radiotracer for 11β-HSD1. Eight individuals underwent one 180-min ( n = 4) or two 240-min ( n = 4) [18F]AS2471907 PET brain scans (12 total) acquired on the high-resolution research tomograph (HRRT) scanner with arterial blood sampling. Imaging data were modeled with 1-tissue (1T) and 2-tissue (2T) compartment models and with multilinear analysis (MA1) to estimate [18F]AS2471907 availability ( VT). [18F]AS2471907 demonstrated high, heterogeneous uptake throughout the brain. Of the compartment models, 2T best described [18F]AS2471907 data. Estimates of VT were highly correlated between 2T and MA1 ( t* = 30 min) with MA1 yielding VT values ranging from 3.2 ± 1.0 mL/cm3 in the caudate to 15.7 ± 4.2 mL/cm3 in the occipital cortex. The median absolute test–retest variability of 16 ± 5% and high intraclass correlation coefficient (ICC) values of 0.67–0.97 across regions indicate fair test–retest reliability but large intersubject variability. VT estimates using 180 min were within 10% of estimates using full acquisition time. In summary, [18F]AS2471907 exhibits reasonable kinetic properties for imaging 11β-HSD1 in human brain.

PURPOSE: To demonstrate the feasibility of microwave-induced thermoacoustic tomography of adult human brain. METHODS: We analyzed the electric field distribution radiated from an antenna to acquire homogeneous illumination. We first imaged the anatomical structures in a rat’s trunk to validate the thermoacoustic contrast in vivo. We then imaged an agar cylinder through an adult human skull ex vivo to demonstrate transcranial penetration of both microwave and ultrasound. We also analyzed the specific absorption rate to show the conformance to the safety standard for human electromagnetic exposure. RESULTS: We successfully acquired cross-sectional images of the rat’s trunk in vivo. Major blood vessels and organs are clearly visible. The transcranial image shows that TAT can image through the adult human skull and reveal an agar enclosed by the skull. CONCLUSIONS: Microwave-induced thermoacoustic tomography of a rat’s trunk in vivo and an agar phantom through an adult human skull ex vivo has been demonstrated experimentally. This study demonstrates both the TAT contrasts in vivo and the capability of transcranial imaging, showing potential of TAT for adult human brain imaging with high contrast and penetration.

Image signal-to-noise ratio in Magnetic Resonance Imaging (MRI) increases with the main magnetic field intensity: Ultra-High Fields (7T and above) can achieve submillimetre resolutions within viable acquisition time. Thus, a whole-body 11.7T MRI was conceived for human brain imaging and is currently being installed at NeuroSpin.However, some MRI sequences like Echo-Planar Imaging (EPI) are very sensitive to magnetic field inhomogeneity caused by susceptibility gradients between biological tissues and air. This inhomogeneity grows linearly with the main magnetic field and causes signal-loss and strong geometric distortions, exacerbated in brain regions close to ear canals and sinuses.Mitigation of these artifacts is performed through the generation of counteracting magnetic fields reducing the field excursions caused by susceptibility gradients. This process, known as shimming, is usually performed with coils correcting field spherical harmonics (SH) up to third degree, but the correction provided by the set of coils with this degree is expected to be insufficient at 11.7T. Increasing the degree and performance of SH systems requires adding many space-consuming coils, building up to a heavy and impractical system.As a compact and versatile solution, shimming systems composed of small circular coils regularly placed over cylindrical surfaces close to the patients head, so-called matrix Multi-Coil Arrays (MCA), have been shown to provide comparable performance to that of high-degree SH systems, while being driven by relatively low currents.In this thesis, we started with the exploration of the application of principal component analysis of subject-optimal stream functions computed from a hundred-brain field-map database. We showed that the first few principal modes could be discretized into windings to build a 3 to 5 channel-count shim system capable of providing equivalent performances to that of 4th and 5th degree SH systems.Then, to provide more flexibility, identification of current flow clusters in the principal stream function modes provided geometry and position of coils to design an optimized MCA. The resulting MCA showed performance superior to that of matrix MCAs with a similar or even reduced amount of channels. Simulations at 7T showed that a 2-layer 36-channel optimized MCA with currents constrained to 3A could provide a performance equivalent to that of unconstrained 6th degree SH systems and was expected to achieve unconstrained 7th degree SH equivalence when equipped with a third layer and a total of 48 channels. A 2-layer 36-channel prototype was built and tested in-vivo at 7T for proof-of-concept, showing consistent results with simulations. It provided high field inhomogeneity reduction and considerable EPI artifact mitigation, expected to be particularly beneficial at 11.7T.Within a more theoretical framework, the intrinsic limits to B0 inhomogeneity correction were studied, and it was shown that no shimming hardware placed outside the human head is capable of zeroing out the magnetic field excursions caused by air/tissue susceptibility gradients throughout the entire brain. Nevertheless, by selecting smaller regions of interest satisfying particular conditions, perfect homogenization is possible., Le rapport signal/bruit des images en imagerie par résonance magnétique (IRM) augmente avec l'intensité du champ magnétique principal: les ultra-hauts champs (7T et plus) peuvent atteindre des résolutions submillimétriques dans un temps d'acquisition viable. Ainsi un scanner IRM corps entier de 11,7T a été conçu pour l'imagerie du cerveau humain et est actuellement en cours d'installation à NeuroSpin.Cependant, certaines séquences IRM comme l’Imagerie Echo-Planaire (EPI) sont très sensibles à l'inhomogénéité du champ magnétique causée par les gradients de susceptibilité entre les tissus biologiques et l'air. Cette inhomogénéité croît linéairement avec le champ magnétique principal et entraîne des pertes de signal et de fortes distorsions géométriques, exacerbées dans les régions du cerveau proches des canaux auditifs et des sinus.L'atténuation de ces artefacts est réalisée par la génération de champs magnétiques antagonistes réduisant les excursions de champ causées par les gradients de susceptibilité. Ce processus, connu sous le nom de shimming, est généralement effectué avec des bobines corrigeant les harmoniques sphériques (SH) du champ jusqu'au troisième degré, mais la correction fournie par l'ensemble des bobines de ce degré devrait être insuffisante à 11,7T. Pour augmenter les degrés et performances des systèmes SH, il faudrait ajouter de nombreuses bobines occupant beaucoup d'espace, qui constitueraient un système lourd et peu pratique.Comme alternative compacte et polyvalente, les systèmes de shim composés de petites bobines circulaires régulièrement placées sur des surfaces cylindriques près de la tête du patient, appelés Multi-Coil Arrays (MCA) matriciels, offrent des performances comparables à celles des systèmes SH à haut degré, tout en étant alimentés par des courants relativement faibles.Dans cette thèse, nous avons commencé par explorer l'application de l'analyse en composantes principales des fonctions de flux adaptées à chaque sujet d'une base de données de 100 cartes de champ de leur cerveau. Nous avons montré que les quelques premiers modes principaux pouvaient être discrétisés en nappes de courant pour construire un système de shim à 3, 4 ou 5 canaux capable de fournir des performances équivalentes à celles de systèmes SH de 4e et 5e degrés.Ensuite, pour plus de flexibilité, l'identification de clusters de flots de courant dans les modes principaux des fonctions de flux a permis de fournir la géométrie et la position de bobines pour concevoir un MCA optimisé. Le MCA résultant a montré des performances supérieures à celles des MCA matriciels avec un nombre de canaux similaire ou même réduit. Les simulations à 7T ont montré qu'un MCA optimisé à 36 canaux et 2 couches avec courants limité à 3A pouvait fournir des performances équivalentes à celles de systèmes SH du 6e degré sans contraintes, et même du 7e degré sans contraintes lorsqu'il était équipé d’une 3ème couche et de 48 canaux. Un prototype à deux couches de 36 canaux a été construit et testé in vivo à 7T pour la preuve de concept, montrant des résultats cohérents avec les simulations. Il a permis de réduire fortement l'inhomogénéité du champ et d'atténuer considérablement les artefacts en EPI, ce qui devrait être particulièrement bénéfique à 11,7T.Dans un cadre plus théorique, les limites intrinsèques de la correction de l'inhomogénéité B0 ont été étudiées, et il a été démontré qu'aucun système de shim placé à l'extérieur de la tête humaine n'est capable de réduire à zéro les excursions de champ magnétique causées par les gradients de susceptibilité air/tissu dans l'ensemble du cerveau. Néanmoins, en sélectionnant des régions d'intérêt plus petites satisfaisant à des conditions particulières, une homogénéisation parfaite est possible.

Ultra-High Field (UHF) Magnetic Resonance Imaging (MRI) advantages, including higher image resolution, reduced acquisition time via parallel imaging, and better signal-to-noise ratio (SNR) have opened new opportunities for various clinical and research projects, including functional MRI, brain connectivity mapping, and anatomical imaging. The advancement of these UHF MRI performance metrics, especially SNR, was the primary motivation of this thesis. Unaccelerated SNR depends on receive array sensitivity profile, receiver noise correlation and static magnetic field strength. Various receive array decoupling technologies, including overlap/inductive and preamplifier decoupling, were previously utilized to mitigate noise correlation. In this dissertation, I developed a novel self-decoupling principle to isolate elements of a loop-based receive array and demonstrated, via full-wave electromagnetic/circuit co-simulations validated by bench measurements, that the self-decoupling technique provides inter-element isolation on par with overlap decoupling while self-decoupling improves SNR. I then designed and constructed the first self-decoupled 32 and 64 channel receiver arrays for human brain MR imaging at 10.5T / 447MHz. Experimental comparisons of these receive arrays with the industry’s gold-standard 7T 32 channel receiver resulted in 1.81 times and 3.53 times more average SNR using the 10.5T 32 and 64 channel receivers I built, respectively. To further improve the SNR of accelerated MR images, I developed a novel data-driven model using a customized conditional generative adversarial network (GAN) architecture for parallel MR image reconstruction and demonstrated that, when applied to human brain images subsampled with rate of 4, the GAN model results in a peak signal-to-noise ratio (PSNR) of 37.65 compared to GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA)’s PSNR of 33.88.In summary, the works presented in this dissertation improved the SNR available for human brain imaging and provided the experimental realization of the advantages anticipated at 10.5T MRI. The insights from this thesis inform future efforts to build self-decoupled transmit arrays and high density, 128 channel loop-based receive arrays for human brain MRI especially at ultra-high field as well as future studies to utilize deep learning techniques for reconstruction and post-processing of parallel MR images.

PURPOSE: Receive array layout, noise mitigation, and B(0) field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields. METHODS: A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry- standard 32- channel receiver at 7 T (7T-32Rx) through experimental phantom measurements. RESULTS: Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration). CONCLUSION: Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.

With balanced spatial resolution, imaging depth, and functional sensitivity, photoacoustic tomography (PAT) hold great promise for human brain imaging. However, the strong acoustic attenuation and aberration of the human skull (∼8 mm thick) are longstanding technical challenges for PAT of the human brain. In this work, we numerically investigated the impacts of the stratified human skull on photoacoustic wave propagation (i.e., the forward model) and PAT image formation (i.e., the inverse model). We simulated two representative transcranial PAT implementations: photoacoustic computed tomography (PACT) and photoacoustic macroscopy (PAMac). In the forward model, we simulated the detailed photoacoustic wave propagation from a point or line source through a digital human skull. The wave attenuation, refraction, mode conversation, and reverberation were thoroughly investigated. In the inverse model, we reconstructed the transcranial PACT and PAMac images of a point or line target enclosed by the human skull. Our results demonstrate that transcranial PAMac suffers mainly from wave reverberation within the skull, leading to prolonged signal duration and reduced axial resolution. Transcranial PACT is more susceptible to the skull’s acoustic distortion, mode conversion, and reverberation, which collectively lead to strong image artifacts and deteriorated spatial resolutions. We also found that PACT with a ring-shaped transducer array shows more tolerance of the skull’s adverse impacts and can provide more accurate image reconstruction. Our results suggest that incorporating the skull’s geometry and acoustic properties can improve transcranial PAT image reconstruction. We expect that our results have provided a more comprehensive understanding of the acoustic impact of the human skull on transcranial PAT.

In magnetic resonance imaging, a birdcage coil is the most commonly used volumetric resonator creating a highly homogeneous radiofrequency magnetic field in a conductive sample. An artificial magnetic radiofrequency shield was recently shown to improve the magnetic field amplitude per unit power (transmit efficiency) of a preclinical birdcage coil by reducing the intrinsic losses in the coil and increasing power absorbed by the sample. In this paper, we propose a new application of an artificial shield in clinical MRI. Thanks to the proposed artificial shield a birdcage coil for human brain imaging operating at 300 MHz (Larmor frequency of protons at static fields of 7 T) can be expanded to increase free space. As a result, the coil becomes more comfortable for the patient and keeping comparable transmit efficiency. The same extended coil with a conventional copper shield would have at least 10% lower efficiency. The proposed artificial shield is implemented as an annular-ring cavity-backed slot in a copper cylinder that tightly surrounds the birdcage. To demonstrate the effect, radiofrequency magnetic field and specific absorption rate distributions were compared numerically and experimentally for the initial and extended coils with different shields.