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Background: Methylation of serotonin-related genes has been proposed as a plausible gene-by-environment link which may mediate environmental stress, depressive and anxiety symptoms. DNA methylation is often measured in blood cells, but little is known about the association between this peripheral epigenetic modification and brain serotonergic architecture. Here, we evaluated the association between whole-blood-derived methylation of four CpG sites in the serotonin transporter (SLC6A4) and six CpG sites of the tryptophan hydroxylase 2 (TPH2) gene and in-vivo brain levels of serotonin transporter (5-HTT) and serotonin 4 receptor (5-HT4) in a cohort of healthy individuals (N = 254) and, for 5-HT4, in a cohort of unmedicated patients with depression (N = 90). To do so, we quantified SLC6A4/TPH2 methylation using bisulfite pyrosequencing and estimated brain 5-HT4 and 5-HTT levels using positron emission tomography. In addition, we explored the association between SLC6A4 and TPH2 methylation and measures of early life and recent stress, depressive and anxiety symptoms on 297 healthy individuals. Results: We found no statistically significant association between peripheral DNA methylation and brain markers of serotonergic neurotransmission in patients with depression or in healthy individuals. In addition, although SLC6A4 CpG2 (chr17:30,236,083) methylation was marginally associated with the parental bonding inventory overprotection score in the healthy cohort, statistical significance did not remain after accounting for blood cell heterogeneity. Conclusions: We suggest that findings on peripheral DNA methylation in the context of brain serotonin-related features should be interpreted with caution. More studies are needed to rule out a role of SLC6A4 and TPH2 methylation as biomarkers for environmental stress, depressive or anxiety symptoms. [ABSTRACT FROM AUTHOR]

Alterations to cerebral white matter tracts have been associated with cognitive decline in aging and Alzheimer's disease (AD). In particular, the fornix has been implicated as especially vulnerable given that it represents the primary outflow tract of the hippocampus. Despite this, little work has focused on the fornix using a potential early marker of white matter degeneration-myelin water fraction (MWF; an in vivo marker of myelin content). Therefore, we sought to (1) clarify associations between MWF in the fornix and memory functioning, and (2) examine whether fornix MWF relates to memory performance above and beyond hippocampal volume and conventional imaging measures of white matter that may not be as specific to alterations in myelin content. Forty nondemented older adults (mean age = 72.9 years) underwent an MRI exam and neuropsychological assessment. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) was used to quantify fornix MWF and diffusion tensor imaging (DTI) was used to measure fornix fractional anisotropy (FA). Adjusting for age, sex, education, and vascular risk factors, linear regression models revealed that, lower fornix MWF was significantly associated with poorer memory functioning (β = 0.405, p = .007) across our sample of older adults. Notably, fornix MWF remained a significant predictor of memory functioning (β = 0.380, p = .015) even after adjusting for fornix DTI FA and hippocampal volume (in addition to the above covariates). Given the observed associations between myelin and memory in older adults without dementia, MWF may be a useful early marker of dementia risk.

Cerebellar hypoplasia can be caused by congenital or post-natal viral infection or a genetic mutation, and leads to an underdeveloped cerebellum, motor dysfunction, and ataxia. Cerebellar dysfunction can also originate from neurodegenerative diseases. This affects the functional coupling of the cerebellum and multiple sensorimotor cerebral networks. Using resting-state fMRI, correlational studies have mapped cerebellar lobules to cerebral cortices and yielded models of cerebral-cerebellar interactions. This study investigated functional connectivity between cerebellar lobules focusing on sensorimotor regions by comparing task-based fMRI data from participants of an existing open-source dataset. To process and visualize the fMRI data, this study referenced anatomical and functional network atlases and used various statistical and computational software packages. Handedness was inferred from first-level functional connectivity analysis to create groups and performed second-level functional connectivity between-groups analysis. Statistically significant differences in functional connectivity between sensorimotor intracerebellar regions were identified, which may be attributable to differences in handedness. The present study showed that when compared to participants inferred to be left-handed or ambidextrous, participants inferred to be right-hand dominant had statistically significant differences in functional connectivity in specific sensorimotor regions, including lobules IV/V left to IV/V right when performing right-hand tasks, and lobules VIII left to III left when performing left-hand tasks. This finding informed the creation of a proposed right-hand sensorimotor intracerebellar model which may be visualized using various platforms. Through integrating causal and correlational imaging data to model intracerebellar connectivity, this study furthers research about the diagnosis and treatment of cerebellar hypoplasia and cerebellar dysfunction. Abbreviations: FC – Functional Connectivity; fMRI – Functional Magnetic Resonance Imaging; gPPI – Generalized Psychophysiological Interactions; GUI – Graphical User Interface; MVPA – MultiVariate Pattern Analysis; ROI –Region of Interest

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals’ distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull’s acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

Cerebellar hypoplasia can be caused by congenital or post-natal viral infection or a genetic mutation, and leads to an underdeveloped cerebellum, motor dysfunction, and ataxia. Cerebellar dysfunction can also originate from neurodegenerative diseases. This affects the functional coupling of the cerebellum and multiple sensorimotor cerebral networks. Using resting-state fMRI, correlational studies have mapped cerebellar lobules to cerebral cortices and yielded models of cerebral-cerebellar interactions. This study investigated functional connectivity between cerebellar lobules focusing on sensorimotor regions by comparing taskbased fMRI data from participants of an existing open-source dataset. To process and visualize the fMRI data, this study referenced anatomical and functional network atlases and used various statistical and computational software packages. Handedness was inferred from first-level functional connectivity analysis to create groups and performed second-level functional connectivity between-groups analysis. Statistically significant differences in functional connectivity between sensorimotor intracerebellar regions were identified, which may be attributable to differences in handedness. The present study showed that when compared to participants inferred to be left-handed or ambidextrous, participants inferred to be right-hand dominant had statistically significant differences in functional connectivity in specific sensorimotor regions, including lobules IV/V left to IV/V right when performing right-hand tasks, and lobules VIII left to III left when performing left-hand tasks. This finding informed the creation of a proposed right-hand sensorimotor intracerebellar model which may be visualized using various platforms. Through integrating causal and correlational imaging data to model intracerebellar connectivity, this study furthers research about the diagnosis and treatment of cerebellar hypoplasia and cerebellar dysfunction. [ABSTRACT FROM AUTHOR]

To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) measures the small magnetic fields generated by current flow in neural networks, providing a noninvasive metric of brain function. MEG is well established as a powerful neuroscientific and clinical tool. However, current instrumentation is hampered by cumbersome cryogenic field‐sensing technologies. In contrast, MEG using optically pumped magnetometers (OPM‐MEG) employs small, lightweight, noncryogenic sensors that provide data with higher sensitivity and spatial resolution, a natural scanning environment (including participant movement), and adaptability to any age. However, OPM‐MEG is new and the optimum way to design a system is unknown. Here, we construct a novel, 90‐channel triaxial OPM‐MEG system and use it to map motor function during a naturalistic handwriting task. Results show that high‐precision magnetic field control reduced background fields to ∼200 pT, enabling free participant movement. Our triaxial array offered twice the total measured signal and better interference rejection compared to a conventional (single‐axis) design. We mapped neural oscillatory activity to the sensorimotor network, demonstrating significant differences in motor network activity and connectivity for left‐handed versus right‐handed handwriting. Repeatability across scans showed that we can map electrophysiological activity with an accuracy ∼4 mm. Overall, our study introduces a novel triaxial OPM‐MEG design and confirms its potential for high‐performance functional neuroimaging. [ABSTRACT FROM AUTHOR]

In this letter, we evaluate antenna designs for ultra-high frequency and field (UHF) human brain magnetic resonance imaging (MRI) at 10.5 tesla (T). Although MRI at such UHF is expected to provide major signal-to-noise gains, the frequency of interest, 447 MHz, presents us with challenges regarding improved B1+ efficiency, image homogeneity, specific absorption rate (SAR), and antenna element decoupling for array configurations. To address these challenges, we propose the use of both monopole and dipole antennas in a novel hybrid configuration, which we refer to as a mono-dipole hybrid antenna (MDH) array. Compared to an 8-channel dipole antenna array of the same dimensions, the 8-channel MDH array showed an improvement in decoupling between adjacent array channels, as well as ∼18% higher B1+ and SAR efficiency near the central region of the phantom based on simulation and experiment. However, the performances of the MDH and dipole antenna arrays were overall similar when evaluating a human model in terms of peak B1+ efficiency, 10 g SAR, and SAR efficiency. Finally, the concept of an MDH array showed an advantage in improved decoupling, SAR, and B1+ near the superior region of the brain for human brain imaging. [ABSTRACT FROM AUTHOR]

Super-resolution three-dimensional (3-D) electromagnetic (EM) inversion for microwave human brain imaging is a typical high contrast EM inverse problem and requires huge computational costs. This work proposes a hybrid neural network electromagnetic inversion scheme (HNNEMIS) which contains shallow and deep neural networks to alleviate the required huge computational costs and solve this high contrast inverse problem. In the proposed scheme, semi-join back propagation neural network (SJ-BPNN) is employed to nonlinearly map the measured scattered electric field to two output channels, namely the permittivity and conductivity of scatterers, respectively. Such a semi-join strategy decreases the computational burden in training and testing processes. Then, a deep learning technique, termed U-Net, is employed to further enhance the imaging quality of the output from SJ-BPNN. To decrease the training cost and make neural networks fast convergent for human brain inversion, a novel training dataset construction strategy which contains the characteristics of human brain is also proposed. Noise-free and noisy numerical examples demonstrate that HNNEMIS has superior super-resolution inversion capabilities for human brain imaging. [ABSTRACT FROM AUTHOR]

Three-dimensional (3-D) super-resolution microwave imaging of human brain is a typical electromagnetic (EM) inverse scattering problem with high contrast. It is a challenge for the traditional schemes based on deterministic or stochastic inversion methods to obtain high contrast and high resolution, and they require huge computational time. In this work, a dual-module 3-D EM inversion scheme based on deep neural network is proposed. The proposed scheme can solve the inverse scattering problems with high contrast and super-resolution in real time and reduce a huge computational cost. In the EM inversion module, a 3-D full convolution EM reconstruction neural network (3-D FCERNN) is proposed to nonlinearly map the measured scattered field to a preliminary image of 3-D electrical parameter distribution of the human brain. The proposed 3-D FCERNN is completely composed of convolution layers, which can greatly save training cost and improve model generalization compared with fully connected networks. Then, the image enhancement module employs a U-Net to further improve the imaging quality from the results of 3-D FCERNN. In addition, a dataset generation strategy based on the human brain features is proposed, which can solve the difficulty of human brain dataset collection and high training cost. The proposed scheme has been confirmed to be effective and accurate in reconstructing the distribution of 3-D super-resolution electrical parameters distribution of human brain through noise-free and noisy examples, while the traditional EM inversion method is difficult to converge in the case of high contrast and strong scatterers. Compared with our previous work, the training of FCERNN is faster and can significantly decrease computational resources.

Three‐dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole‐brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1‐weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1‐weighted images as a reference standard. Scan‐rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within‐subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1‐weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions. [ABSTRACT FROM AUTHOR]

Imagine you were asked to investigate the workings of an engine, but to do so without ever opening the hood. Now imagine the engine fueled the human mind. This is the challenge faced by cognitive neuroscientists worldwide aiming to understand the neural bases of our psychological functions. Luckily, human ingenuity comes to the rescue. Around the same time as the Society for Neuroscience was being established in the 1960s, the first tools for measuring the human brain at work were becoming available. Noninvasive human brain imaging and neurophysiology have continued developing at a relentless pace ever since. In this 50 year anniversary, we reflect on how these methods have been changing our understanding of how brain supports mind. [ABSTRACT FROM AUTHOR]

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals' distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull's acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull., Competing Interests: The authors declare no conflicts of interest., (© 2023 The Authors.)

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person. [ABSTRACT FROM AUTHOR]

Why vestibular compensation (VC) after an acute unilateral vestibular loss is the neuro-otologist's best friend is the question at the heart of this paper. The different plasticity mechanisms underlying VC are first reviewed, and the authors present thereafter the dual concept of vestibulo-centric versus distributed learning processes to explain the compensation of deficits resulting from the static versus dynamic vestibular imbalance. The main challenges for the plastic events occurring in the vestibular nuclei (VN) during a post-lesion critical period are neural protection, structural reorganization and rebalance of VN activity on both sides. Data from animal models show that modulation of the ipsilesional VN activity by the contralateral drive substitutes for the normal push-pull mechanism. On the other hand, sensory and behavioural substitutions are the main mechanisms implicated in the recovery of the dynamic functions. These newly elaborated sensorimotor reorganizations are vicarious idiosyncratic strategies implicating the VN and multisensory brain regions. Imaging studies in unilateral vestibular loss patients show the implication of a large neuronal network (VN, commissural pathways, vestibulo-cerebellum, thalamus, temporoparietal cortex, hippocampus, somatosensory and visual cortical areas). Changes in gray matter volume in these multisensory brain regions are structural changes supporting the sensory substitution mechanisms of VC. Finally, the authors summarize the two ways to improve VC in humans (neuropharmacology and vestibular rehabilitation therapy), and they conclude that VC would follow a 'top-down' strategy in patients with acute vestibular lesions. Future challenges to understand VC are proposed. [ABSTRACT FROM AUTHOR]

BackgroundIncreasing evidence suggests that autism is associated with abnormal white-matter (WM) anatomy and impaired brain ‘connectivity’. While myelin plays a critical role in synchronized brain communication, its aetiological role in autistic symptoms has only been indirectly addressed by WM volumetric, relaxometry and diffusion tensor imaging studies. A potentially more specific measure of myelin content, termed myelin water fraction (MWF), could provide improved sensitivity to myelin alteration in autism.MethodWe performed a cross-sectional imaging study that compared 14 individuals with autism and 14 age- and IQ-matched controls. T1 relaxation times (T1), T2 relaxation times (T2) and MWF values were compared between autistic subjects, diagnosed using the Autism Diagnostic Interview – Revised (ADI-R), with current symptoms assessed using the Autism Diagnostic Observation Schedule (ADOS) and typical healthy controls. Correlations between T1, T2 and MWF values with clinical measures [ADI-R, ADOS, and the Autism Quotient (AQ)] were also assessed.ResultsIndividuals with autism showed widespread WM T1 and MWF differences compared to typical controls. Within autistic individuals, worse current social interaction skill as measured by the ADOS was related to reduced MWF although not T1. No significant differences or correlations with symptoms were observed with respect to T2.ConclusionsAutistic individuals have significantly lower global MWF and higher T1, suggesting widespread alteration in tissue microstructure and biochemistry. Areas of difference, including thalamic projections, cerebellum and cingulum, have previously been implicated in the disorder; however, this is the first study to specifically indicate myelin alteration in these regions. [ABSTRACT FROM PUBLISHER]

Using a new rotating SENSitivity Encoding (rotating-SENSE) algorithm, we have successfully demonstrated that the rotating radiofrequency coil array (RRFCA) was capable of achieving a significant reduction in scan time and a uniform image reconstruction for a homogeneous phantom at 7 Tesla. However, at 7 Tesla the in vivo sensitivity profiles ( B 1 - ) become distinct at various angular positions. Therefore, sensitivity maps at other angular positions cannot be obtained by numerically rotating the acquired ones. In this work, a novel sensitivity estimation method for the RRFCA was developed and validated with human brain imaging. This method employed a library database and registration techniques to estimate coil sensitivity at an arbitrary angular position. The estimated sensitivity maps were then compared to the acquired sensitivity maps. The results indicate that the proposed method is capable of accurately estimating both magnitude and phase of sensitivity at an arbitrary angular position, which enables us to employ the rotating-SENSE algorithm to accelerate acquisition and reconstruct image. Compared to a stationary coil array with the same number of coil elements, the RRFCA was able to reconstruct images with better quality at a high reduction factor. It is hoped that the proposed rotation-dependent sensitivity estimation algorithm and the acceleration ability of the RRFCA will be particularly useful for ultra high field MRI. [ABSTRACT FROM AUTHOR]

Three-dimensional (3-D) super-resolution microwave imaging of human brain is a typical electromagnetic (EM) inverse scattering problem with high contrast. It is a challenge for the traditional schemes based on deterministic or stochastic inversion methods to obtain high contrast and high resolution, and they require huge computational time. In this work, a dual-module 3-D EM inversion scheme based on deep neural network is proposed. The proposed scheme can solve the inverse scattering problems with high contrast and super-resolution in real time and reduce a huge computational cost. In the EM inversion module, a 3-D full convolution EM reconstruction neural network (3-D FCERNN) is proposed to nonlinearly map the measured scattered field to a preliminary image of 3-D electrical parameter distribution of the human brain. The proposed 3-D FCERNN is completely composed of convolution layers, which can greatly save training cost and improve model generalization compared with fully connected networks. Then, the image enhancement module employs a U-Net to further improve the imaging quality from the results of 3-D FCERNN. In addition, a dataset generation strategy based on the human brain features is proposed, which can solve the difficulty of human brain dataset collection and high training cost. The proposed scheme has been confirmed to be effective and accurate in reconstructing the distribution of 3-D super-resolution electrical parameters distribution of human brain through noise-free and noisy examples, while the traditional EM inversion method is difficult to converge in the case of high contrast and strong scatterers. Compared with our previous work, the training of FCERNN is faster and can significantly decrease computational resources.

Quantitative myelin content imaging provides novel and pertinent information related to underlying pathogenetic mechanisms of myelin-related disease or disorders arising from aberrant connectivity. Multicomponent driven equilibrium single pulse observation of T1 and T2 is a time-efficient multicomponent relaxation analysis technique that provides estimates of the myelin water fraction, a surrogate measure of myelin volume. Unfortunately, multicomponent driven equilibrium single pulse observation of T1 and T2 relies on a two water-pool model (myelin-associated water and intra/extracellular water), which is inadequate within partial volume voxels, i.e., containing brain tissue and ventricle or meninges, resulting in myelin water fraction underestimation. To address this, a third, nonexchanging 'free-water' component was introduced to the multicomponent driven equilibrium single pulse observation of T1 and T2 model. Numerical simulations and experimental in vivo data show that the model to perform advantageously within partial volume regions while providing robust and reproducible results. It is concluded that this model is preferable for future studies and analysis. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The study of brain–computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications. [ABSTRACT FROM PUBLISHER]

The article presents research on the effects of main and transmit magnetic fields in multiple component driven equilibrium single-pulse observation. An approach using a multicomponent equilibrium was used in the observation of T1 and T2. It uses two-component relaxation information from spoiled and fully balanced steady-state imaging data acquired over multiple flip angles. Aside from rapid acquisition times and high signal-to-noise ration efficiency, the techniques are also sensitive to main and transmit magnetic field inhomogeneities.

This paper reviews empirical data from monkey neurophysiology and human brain imaging regarding mirror neurons and language. These data are interpreted as suggesting that some of our cognitive intuitions about language and representations may be misleading. For centuries, representations have been conceptualized as secondary, both temporally and spatially, to a primary entity. What if representations have ontological priority? Can we even conceive this idea? [ABSTRACT FROM AUTHOR]

We investigated the neuronal processing of the physiologically particularly important precision grip (opposition of index finger and thumb) by the combination of functional magnetic resonance imaging (fMRI) and an MR-compatible haptic interface. Ten healthy subjects performed isometric precision grip force generation with visual task instruction and real-time visual feedback in a block design. In a 2 × 2 two-factorial design, both the timing and force could be either constant or varying (identical average timing and force). As we expected only small changes in the fMRI response for the different fine-graded motor control conditions, we maximized the sensitivity of the data analysis and implemented a volumes of interest (VOI) restricted general linear model analysis including non-explanatory force regressors to eliminate directly force-related low-level activations. The VOIs were defined based on previous studies. We found significant associations: timing variation (variable vs. constant) and primary motor area (M1) and dorsal premotor area (PMd); force variation (variable vs. constant) and primary somatosensory area (S1), anterior intraparietal area (AIP) and PMd; interaction of timing and force and supplementary motor area (SMA) and AIP. We conclude that SMA and AIP integrate fine-graded higher-level timing and force control during precision grip. M1, S1 and PMd process lower-level timing and force control, yet not their integration. These results are the basis for a detailed assessment of manual motor control in a variety of motor diseases. The detailed behavioural assessment by our MR-compatible haptic interface is particularly valuable in patients due to expected larger inter-individual variation in motor performance. [ABSTRACT FROM AUTHOR]

Abstract: In this review, we integrate recent human and animal studies from the viewpoint of chronic pain. First, we briefly review the impact of chronic pain on society and address current pitfalls of its definition and clinical management. Second, we examine pain mechanisms via nociceptive information transmission cephalad and its impact and interaction with the cortex. Third, we present recent discoveries on the active role of the cortex in chronic pain, with findings indicating that the human cortex continuously reorganizes as it lives in chronic pain. We also introduce data emphasizing that distinct chronic pain conditions impact on the cortex in unique patterns. Fourth, animal studies regarding nociceptive transmission, recent evidence for supraspinal reorganization during pain, the necessity of descending modulation for maintenance of neuropathic behavior, and the impact of cortical manipulations on neuropathic pain is also reviewed. We further expound on the notion that chronic pain can be reformulated within the context of learning and memory, and demonstrate the relevance of the idea in the design of novel pharmacotherapies. Lastly, we integrate the human and animal data into a unified working model outlining the mechanism by which acute pain transitions into a chronic state. It incorporates knowledge of underlying brain structures and their reorganization, and also includes specific variations as a function of pain persistence and injury type, thereby providing mechanistic descriptions of several unique chronic pain conditions within a single model. [Copyright &y& Elsevier]

Transcranial magnetic stimulation (TMS) using a double-cone coil over the medial frontal cortex has the potential to clarify the function of the anterior cingulate cortex (ACC) in cognition, emotion and mood disorders. Following demonstration of disruption of performance on psychological tasks closely linked to cingulate function using this TMS technique, the current study aimed to directly measure the regional distribution of physiological effects of stimulation in the brain with H215O PET. Experiment 1 assessed the effect of increasing numbers of pulse trains of TMS on regional cerebral blood flow (rCBF). Experiment 2 assessed the capacity of medial frontal TMS to modulate brain activity associated with the Stroop task using medial parietal TMS as a control site of stimulation. SPM99 analyses, using the ACC as a region of interest, revealed clusters of increased rCBF during medial frontal TMS in Brodmann area 24 and reduced rCBF in more ventral ACC, the latter occurring in both experiments. In a whole-brain analysis, striking changes in rCBF were observed distal to the ACC following medial frontal TMS. Although TMS reliably affected Stroop task performance in early trials, there was no interaction between TMS and Stroop condition in rCBF. Our results suggest that medial frontal TMS using the double-cone coil can affect ACC activity. However, a number of more distal cortical areas were also affected in these experiments. These additional changes may reflect either ‘downstream’ effects of altered cingulate cortex activity or direct effects of the coil. [ABSTRACT FROM AUTHOR]

Behavioral studies in humans have shown that bimanual coordination imposes specific demands on the central nervous system that exceed unimanual task control. In the present study we used functional magnetic resonance imaging to investigate the neural correlate of this additional coordination effort, i.e. regions responding more strongly to bimanual movements than inferred from summing up the responses to the unimanual subtasks. Subjects were scanned while performing movements along different directions, either uni- or bimanually. During the bimanual condition, trajectories of movement of the left and right hand were spatially incompatible, such that additional effort was required to break away from intrinsically favored mirror-movements and to integrate movements of both limbs into a new spatial pattern. Our main finding was that the execution of spatially complex bimanual coordination as compared with the unimanual subtasks activated the anterior cingulate cortex (posterior part) as well as the dorso-anterior precuneus. We hypothesize that the anterior cingulate exerts its modulatory effect on other motor areas, such as the primary motor cortex and the supplementary motor area, in order to suppress intrinsically favored coordination tendencies. Conversely, the precuneus is likely to be involved in shifting attention between different locations in space, which was necessary for monitoring the trajectories of the left and right wrist when both limbs moved in parallel. Our findings suggest that the coordination effort during bimanual and perhaps other modes of coordinated behavior is mediated by regions contributing to higher order functions, which form an interface between cognition and action. [ABSTRACT FROM AUTHOR]

.Inquiry into religious experience is informed by conceptualizations of emotion. Although a long history of theoretical and empirical work has provided considerable insight into the philosophical, psychological, and (more recently) neurobiological structure of emotion, the role of cognition and feeling in religious emotional states remains poorly conceived, and, hence, so does the concept of religious experience. The lack of a clear understanding of the role of emotion in religious experience is a consequence of a lack of an adequate interdisciplinary account of emotions. Our primary aim here is to examine the consequences of a properly interdisciplinary understanding of emotions for the analysis of religious experience. To this end, we note points of convergence between psychological, philosophical, and neuroscientific accounts of emotion and between such accounts and reports on the neurobiology of religious experience, in particular two recent human brain imaging studies. We conclude that emotions are richer phenomena than either pure feeling or pure thought and that, rightly understood, emotion affords religious experience its distinctive content and quality. Accordingly, we argue that religious experience cannot be reduced to pure feeling or pure thought. Rather, on our analysis, religious experience emerges as“thinking that feels like something.” [ABSTRACT FROM AUTHOR]

We discuss how emerging object-relational database mediator technology can be used to integrate academic freeware and commercial-off-the-shelf software components to create a sequence of gradually more complex and powerful, always semantically and syntactically homogeneous, database centered Image Meta-Analysis Environments. We show how this may be done by definition and utilization of a use-case-based evolutionary design and development process. This process allows subsystems to be produced largely independently by several small specialist subprojects, turning the system integration work into a high-level domain modelling task. [ABSTRACT FROM AUTHOR]