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Positrons emission tomography is a nuclear imaging technics using nuclear decays. It is used both in clinical and preclinical studies. The later requires the use of small animals such as the mouse. The objective is to obtain the best signal with the best spatial resolution. Yet, a weight ratio between humans and mice indicates the need of a sub-millimeter resolution. A conventional scanner is based on detection modules surrounding the object to image and arranged perpendicularly. This implies a strong relationship between efficiency and spatial resolution. This work focuses on the axial geometry in which detection modules are arranged parallel to the object. This limits the relationship between the figures of merit, leading to both high spatial resolution and efficiency. The simulations of prototypes showed great perspectives in term of sub-millimeter resolution with efficiencies of 15 or 40% according to the scanner’s axial extension. These results indicate great perspectives for both clinical and preclinical imaging., La tomographie par émission de positrons est une technique d’imagerie nucléaire utilisant des noyaux radioactifs. Elle est utilisée dans le domaine clinique et préclinique. Cette dernière nécessite l’utilisation de petits animaux, comme la souris. Comme en imagerie clinique, l’objectif est d’obtenir le meilleur signal avec la meilleure précision spatiale possible. Cependant, un rapport d’échelle homme-souris suggère une résolution inférieure à 1 mm3. Un imageur conventionnel est constitué de modules de détection entourant le patient, orientés radialement. Cette approche lie efficacité et résolution spatiale. Ce travail concerne l’étude de la géométrie axiale. Les éléments de détection sont ici orientés parallèlement à l’objet. Ceci limite la corrélation entre efficacité de détection et résolution spatiale, et ainsi permet d’obtenir une haute résolution et haute sensibilité. La simulation de prototypes a permis d’envisager une résolution spatiale moyenne inférieure au millimètre et une efficacité de 15 ou 40% selon l’extension axiale. Ces résultats permettent de présager de bonnes perspectives en imageries préclinique et clinique.

This chapter describes the study of tinnitus in humans by means of neuroimaging to measure brain function and structure. Here, “neuroimaging” is defined to mean any of a variety of noninvasive techniques, from scalp recordings of electrical activity, to images permitting quantification of the gray and white matter of the cerebral cortex, to neurally coupled changes in blood flow. This chapter focuses specifically on the use of neuroimaging to understand chronic, subjective tinnitus, that is, tinnitus that is lasting and cannot be explained by either an external sound source, or a source within the body (Eggermont and Zeng, Chapter 1). Although defined as the perception of sound lacking a physical sound source, the clinical condition of tinnitus is more than the percept. The tinnitus patient often presents with depression, anxiety, difficulties concentrating, and/or difficulties sleeping. These nonperceptual aspects of the condition are what make tinnitus a clinical problem for approximately 5–10% of the population (Coles, 1984; Shargorodsky et al., 2010).

OBJECTIVE: The mesocoelic recess (MCR) is found in the brain of human embryos and fetuses. The mesocoelic recess seems to be functionally related to the subcommissural organ that is one of neurosecretory organs involved in osmoregulation on the basis of data from other species. Subsequently, recent speculation as to the importance of the subcommissural organ in the development of congenital hydrocephalus has been raised. Yet unlike other mammals, MCR is known to be a vestigial structure in the adult human brain. Here, we performed the in vivo imaging identification of this space to investigate functional and clinical correlations. METHODS: We studied adult human brains using a 7.0-T magnetic resonance imaging (MRI). Twenty healthy individuals aged 22 to 30 years were selected, and they were all volunteers. The parasagittal images through the intercommissural line were examined. We determined the type of shape of the MCR; a triangular C shape was classified as type 1, and a trapezoidal concave shape was classified as type 2. RESULTS: In 14 brains, the recesses were radiologically demonstrated just rostral to the tectal plate of the midbrain and covered the ventral aspect of the posterior commissure and pointed the opening into the aqueduct. The average size of the circumference of the MCR measured from the end point of the C-shaped cup was 6.82 mm. CONCLUSIONS: This study on the anatomy of the MCR of adult brains in vivo is the first of its kind, thanks to the availability of 7.0-T MRI because it has been barely discernible even in autopsy specimens as well as in radiology owing to the resolution limit of the currently available imaging system. The current study raises awareness of the MCR, an important but little-known anatomic structure in adult human brain. This visualization of MCR in human in vivo with ultrahigh-field MRI will certainly provide us important clues including the functional information of MCR, a mystery of modern neurological science.

A new method of non-uniform image correction is proposed. Image non-uniformity is originated from the spatial distribution of RF transmission and reception fields, represented as B(1)(+) and B(1)(-), respectively. In our method, B(1)(+) mapping was performed invivo by a phase method. In B(1)(-) mapping, images with multiple TEs were acquired with a multi-echo adiabatic spin echo (MASE) sequence which enables homogeneous excitation. By T(2) fitting of these images an M(0) map (M(0)(MASE)) was obtained, in which signal intensity was expressed as the product of B(1)(-) and M₀(1-e⁻(TR/T¹)) . The ratio of this M(0)(MASE) map to the B(1)(+) map showed a similar spatial pattern in different human brains. These ratios of M(0)(MASE) to B(1)(+) in 24 subjects were averaged and then fitted with a spatially polynomial function to obtain a ratio map of B(1)(-)/B(1)(+)(α). Uniform image was achieved in spin echo (SE), MASE and inversion recovery turboFLASH (IRTF) images using measured B(1)(+) and calculated B(1)(-) by αB(1)(+). Water fractions in gray and white matters obtained from the M(0) images corrected by this method were in good agreement with previously reported values. From these experimental results, the proposed method of non-uniformity correction is validated at 4.7 T imaging.

Leading brain researcher P. Read Montague will join the Virginia Tech Carilion Research Institute on Nov. 15, announced institute director Michael Friedlander.

Magnetic susceptibility varies among brain structures and provides insights into the chemical and molecular composition of brain tissues. However, the determination of an arbitrary susceptibility distribution from the measured MR signal phase is a challenging, ill-conditioned inverse problem. Although a previous method named calculation of susceptibility through multiple orientation sampling (COSMOS) has solved this inverse problem both theoretically and experimentally using multiple angle acquisitions, it is often impractical to carry out on human subjects. Recently, the feasibility of calculating the brain susceptibility distribution from a single-angle acquisition was demonstrated using morphology enabled dipole inversion (MEDI). In this study, we further improved the original MEDI method by sparsifying the edges in the quantitative susceptibility map that do not have a corresponding edge in the magnitude image. Quantitative susceptibility maps generated by the improved MEDI were compared qualitatively and quantitatively with those generated by calculation of susceptibility through multiple orientation sampling. The results show a high degree of agreement between MEDI and calculation of susceptibility through multiple orientation sampling, and the practicality of MEDI allows many potential clinical applications.

Parkinson's disease (PD) is a neurodegenerative disorder resulting from progressive loss of dopaminergic neurons in the substantia nigra (SN) pars compacta. Therefore, imaging of the SN has been regarded to hold greatest potential for use in the diagnosis of PD. At the 7.0T magnetic resonance imaging (MRI), it is now possible to delineate clearly the shapes and boundaries of the SN. We scanned eight early and two advanced PD patients, along with nine age-matched control subjects, using a 7.0T MRI in an attempt to directly visualize the SN and quantify the differences in shape and boundaries of SN between PD subjects in comparison with the normal control subjects. In the normal controls, the boundaries between the SN and crus cerebri appear smooth, and clean "arch" shapes that stretch ventrally from posterior to anterior. In contrast, these smooth and clean arch-like boundaries were lost in PD subjects. The measured correlation analyses show that, in PD patients, there is age-dependent correlation and substantially stronger UPDRS motor score-dependent correlation. These results suggest that, by using 7.0T MRI, it appears possible to use these visible and distinctive changes in morphology as a diagnostic marker of PD.

Assorted technologies such as; EEG, MEG, fMRI, BEM, MRI, TMS and BCI are being integrated to understand how human visual cortical areas interact during controlled laboratory and natural viewing conditions. Our focus is on the problem of separating signals from the spatially close early visual areas. The solution involves taking advantage of known functional anatomy to guide stimulus selection and employing principles of spatial and temporal response properties that simplify analysis. The method also unifies MEG and EEG recordings and provides a means for improving existing boundary element head models. In going beyond carefully controlled stimuli, in natural viewing with scanning eye movements, assessing brain states with BCI is a most challenging task. Frequent eye movements contribute artifacts to the recordings. A linear regression method is introduced that is shown to effectively characterize these frequent artifacts and could be used to remove them. In free viewing, saccadic landings initiate visual processing epochs and could be used to trigger strictly time based analysis methods. However, temporal instabilities indicate frequency based analysis would be an important adjunct. The class of Cauchy filter functions is introduced that have narrow time and frequency properties well matched to the EEG/MEG spectrum for avoiding channel leakage.

Because subjective tinnitus is typically localized to the ear with hearing loss, tinnitus was traditionally thought to originate from neural hyperactivity in the damaged ear. However, most studies have found that hearing loss reduces the neural outputs from the damaged cochlea. These negative findings led to the hypothesis that tinnitus arises from aberrant neural activity in the central auditory system. Positron emission tomography imaging studies performed on tinnitus patients that could modulate their tinnitus provide evidence showing that the aberrant neural activity that gives rise to tinnitus resides in the central auditory pathway. To investigate the biological basis of tinnitus in more detail, an animal model was developed that allowed behavioral measures of tinnitus to be obtained from individual rats after inducing tinnitus with high doses of salicylate or high-intensity noise. This behavioral model was used to test the efficacy of memantine, an N-methyl-D-aspartate antagonist, and scopolamine, an anticholinergic, in suppressing salicylate-induced tinnitus. Neither drug completely suppressed salicylate-induced tinnitus. To detect the physiological changes associated with tinnitus, chronic microwire electrodes were implanted in the auditory cortex and measurements were obtained from the auditory cortex before and after salicylate and noise exposures known to induce tinnitus. High doses of salicylate or high-level noise exposure generally resulted in sound-evoked hyperactivity in the electrophysiological responses recorded from the auditory cortex of awake-animals. However, anesthetic tended to suppress or abolish the hyperactivity.

Two eight-channel MRI receive-only coils were developed to provide whole-brain coverage at 1.5 T and 3.0 T field strength, respectively. Objectives were an image signal-to-noise ratio superior to standard designs throughout the human brain, as well as high parallel imaging performance. Electromagnetic field simulations were used to determine array diameter and inter-element coil gap. Low mutual inductive coupling was achieved at 1.5 and 3.0 T using high-impedance pre-amplifiers. Coils show an average SNR improvement over commercial birdcage coils of 2.4 and 2.3 for the 1.5 T and 3.0 T design, respectively. The mean of the noise-amplification factor related to reconstruction of under-sampled data (g-factor) was 1.03 for 2-fold under-sampled data (rate-2) and 1.22 for rate-3 at 1.5 T. For data acquired with the 3.0 T coil array, these values were respectively 1.06 for rate-2 and 1.37 for rate-3.

There are numerous methods designed to monitor brain neuropathologies resulting from a wide arsenal of insults. Regardless of the cause of neuronal death, reactive glial cells always appear at and around the site of degeneration. These cells are distinguished by the exceptional abundance of peripheral benzodiazepine receptors, particularly compared with surrounding neurons. Measuring the binding of specific ligands to these peripheral benzodiazepine receptors offers a unique indirect marker for reliable damage assessment in the CNS and a faithful indicator for the accompanying cognitive deficits.

For many years AI researchers have sought to understand the nature of intelligence primarily by creating artificially intelligent computer systems. Studies of human intelligence have had less influence on AI, partly because of the great difficulty in directly observing human brain activity. In recent years, new methods for observing brain activity have become available, notably functional Magnetic Resonance Imaging (fMRI) which allows us to safely, non-invasively capture images of activity across the brain once per second, at millimeter spatial resolution. The advent of fMRI has already produced dramatic new insights into human brain activity, and how it varies with cognitive task. This breakthrough in instrumentation (and others as well) shifts the balance of utility between building artificial intelligent systems and studying natural intelligence. As a result, we should expect a growing synergy in the future between studies of artificial and natural intelligence. One intriguing open question regarding fMRI is whether it is possible to decode instantaneous cognitive states of human subjects based on their observed fMRI activity. If this were feasible, it would open the possibility of directly observing the sequence of hidden cognitive states a person passes through while performing cognitive tasks such as language comprehension, problem solving, etc. We present initial results showing that it is indeed possible to distinguish among a variety of cognitive states of human subjects based on their observed fMRI data. In particular, we have developed machine learning algorithms that can be trained to discriminate among a variety of cognitive states based on the observed fMRI data of the subject at a particular time or time interval. These machine learning algorithms, including Bayesian classifiers, support vector machines, logistic regression, and other methods, use the training data to discover the spatial-temporal patterns of fMRI activity associated with different cognitive states. They can then classify new fMRI observations to distinguish among these states. This talk will describe results in which our machine learning methods were able to successfully discriminate between states such as "the subject is reading a sentence" versus "the subject is viewing a picture"; "the sentence is ambiguous" versus "the sentence is unambiguous"; and "the word is a noun" versus "the word is a verb." These classifiers are typically trained separately for each human subject, but in one case we were able to train a classifier that applies to new human subjects outside the training set. We will describe these results, the machine learning methods used to achieve them, and a number of directions for future research.

Regional spatial normalization is an important preliminary step in the analysis of 3-D brain images. The goal is to remove anatomical differences by warping each brain image to match corresponding features in a standard brain atlas. We are developing a very efficient regional spatial normalization algorithm based on octree volume decomposition. The original Octree Spatial Normalization (OSN) algorithm was shown to perform regional spatial normalization in binary brain phantoms in less then 8 minutes with accuracy similar to previously published methods. Several modifications were made in OSN algorithm to optimize it for use with human brain images including automated brain tissue segmentation for tissue classification and feature matching methods with fast cross-correlation. Even with these modifications spatial normalization can still be done in less then 15 minutes for 256 arrays.

Human neuroimaging methods such as positron emission tomography and functional magnetic resonance imaging have made possible the study of large-scale distributed networks in the behaving human brain. Although many imaging studies support and extend knowledge gained from other experimental modalities such as animal single-cell recordings, there have also been a substantial number of experiments that appear to contradict the animal studies. Part of the reason for this is that neuroimaging is an indirect measure of neuronal firing activity, and thus interpretation is difficult. Computational modeling can help to bridge the gap by providing a substrate for making explicit the assumptions and constraints provided from other sources such as anatomy, physiology and behavior. We describe a large-scale model of working memory that we have used to examine a number of issues relating to the interpretation of imaging data. The gating mechanism that regulates engagement and retention of short-term memory is revised to better reflect hypothesized underlying neuromodulatory mechanisms. It is shown that in addition to imparting better performance for the memory circuit, this mechanism also provides a better match to imaging data from working memory studies.

We propose a model that draws together experimental evidence from anatomical, electrophysiological and imaging experiments in order to understand better the neural substrate of human imaging studies using positron electron tomography (PET) and functional magnetic resonance imaging (fMRI). First, we define a simple local circuit that reflects the major role that local connectivity plays in producing PET and fMRI data, which are thought to mainly reflect synaptic activity. Second, in order to account for the role of varying behaviors during the course of a typical imaging experiment, we propose a local circuit that can perform a delayed match-to-sample task. The elements of this circuit behave very much like neurons that have been found in the prefrontal cortex during similar tasks in monkeys. One subpopulation responds selectively only when stimuli are present. Two different populations show the two types of delay-period activity that have been identified, one with high activity both during the cue and the delay period, the other with a rise during the delay period only. Last, a subpopulation shows a brief response only if the second stimulus matches the first, thus mediating the decision about whether the stimuli match. We show that in addition to performing the task, the integrated summed synaptic activities of the model are similar to experimental PET data.

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.

Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. We propose the co‐clustering‐based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co‐clustering‐based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs‐fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs‐fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres., A novel method was proposed for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. The proposed the co‐clustering‐based method not only concurrently parcellates two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also estimates the connectivity between these subregions from the two brain ROIs. Validation results have demonstrated that our method could yield more meaningful, interpretable, and homogenous connectional topography than existing methods.

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., 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. 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.

Psychedelics are reawakening interest from psychiatry, cognitive neuroscience and the general public with impressive outcomes in small-scale clinical trials, intriguing human brain imaging work and high-impact journalism.This brief opinion piece offers a perspective on how psychedelics work in the brain that may help contextualize these developments. It attempts to link various scales of action, from the molecular (serotonin 2A receptor agonism) through to the anatomical and functional (heightened plasticity) and up to the dynamic (increased brain entropy), systems level (network disintegration and desegregation) and experiential.It is proposed that psychedelics initiate a cascade of neurobiological changes that manifest at multiple scales and ultimately culminate in the relaxation of high-level beliefs. The purpose of psychedelic therapy is to harness the opportunity afforded by this belief-relaxation to achieve a healthy revision of pathological beliefs.

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.

Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.

Neuroscience has become one of the most exciting contemporary research areas with major breakthroughs expected in the coming decades. Modern imaging techniques have enabled scientific understanding of the neural system by revealing anatomical, functional, metabolic, and molecular information about the brain. Among these techniques, photoacoustic tomography (PAT), drawing more and more attention, is playing an increasingly important role in brain studies, thanks to its rich optical absorption contrast, high spatiotemporal resolution, and deep penetration. More importantly, PAT’s unique scalability empowers neuroscientists to examine the brain at multiple spatial scales using the same contrast mechanism, bridging microscopic insights to macroscopic observations of the brain. In this chapter, we review the principles of PAT, present the major implementations, and summarize the representative neuroscience applications. We also discuss challenges in translating PAT to human brain imaging and envision its potential promise.

The motivation to seek and consume water is an essential component of human fluid–electrolyte homeostasis, optimal function, and health. This review describes the evolution of concepts regarding thirst and drinking behavior, made possible by magnetic resonance imaging, animal models, and novel laboratory techniques. The earliest thirst paradigms focused on single factors such as dry mouth and loss of water from tissues. By the end of the 19th century, physiologists proposed a thirst center in the brain that was verified in animals 60 years later. During the early- and mid-1900s, the influences of gastric distention, neuroendocrine responses, circulatory properties (i.e., blood pressure, volume, concentration), and the distinct effects of intracellular dehydration and extracellular hypovolemia were recognized. The majority of these studies relied on animal models and laboratory methods such as microinjection or lesioning/oblation of specific brain loci. Following a quarter century (1994–2019) of human brain imaging, current research focuses on networks of networks, with thirst and satiety conceived as hemispheric waves of neuronal activations that traverse the brain in milliseconds. Novel technologies such as chemogenetics, optogenetics, and neuropixel microelectrode arrays reveal the dynamic complexity of human thirst, as well as the roles of motivation and learning in drinking behavior.

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.

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

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., 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.

A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

INTRODUCTION AND OBJECTIVES:The cerebrum has been considered to regulate micturition reflex. Recently, human brain imaging studies revealed that the ACC (anterior cingulate cortex) may regulate mic...

Contains fulltext : 204554.pdf (Publisher’s version ) (Open Access) The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard. 5 p.

It has been nearly 10 years since Ghazanfar and Schroeder (2006) proposed that the neocortex is essentially multisensory in nature. However, it is only recently that sufficient and hard evidence that supports this proposal has accrued. We review evidence that activity within the human primary visual cortex plays an active role in multisensory processes and directly impacts behavioural outcome. This evidence emerges from a full pallet of human brain imaging and brain mapping methods with which multisensory processes are quantitatively assessed by taking advantage of particular strengths of each technique as well as advances in signal analyses. Several general conclusions about multisensory processes in primary visual cortex of humans are supported relatively solidly. First, haemodynamic methods (fMRI/PET) show that there is both convergence and integration occurring within primary visual cortex. Second, primary visual cortex is involved in multisensory processes during early post-stimulus stages (as revealed by EEG/ERNERFs as well as TMS). Third, multisensory effects in primary visual cortex directly impact behaviour and perception, as revealed by correlational (EEG/ERPs/ERFs) as well as more causal measures (TMS/tACS). While the provocative claim of Ghazanfar and Schroeder (2006) that the whole of neocortex is multisensory in function has yet to be demonstrated, this can now be considered established in the case of the human primary visual cortex. (C) 2015 Elsevier Ltd. All rights reserved.

Human brain imaging can help improve our understanding of mechanisms underlying brain function and how they drive behavior in health and disease. Such knowledge may eventually help us to devise better treatments for psychiatric disorders. However, the brain imaging literature in psychiatry and especially eating disorders has been inconsistent, and studies are often difficult to replicate. The extent or severity of extremes of eating and state of illness, which are often associated with differences in, for instance hormonal status, comorbidity, and medication use, commonly differ between studies and likely add to variation across study results. Those effects are in addition to the well-described problems arising from differences in task designs, data quality control procedures, image data preprocessing and analysis or statistical thresholds applied across studies. Which of those factors are most relevant to improve reproducibility is still a question for debate and further research. Here we propose guidelines for brain imaging research in eating disorders to acquire valid results that are more reliable and clinically useful.

Background Although previous studies have suggested that insulin plays a role in brain function, it still remains unclear whether or not insulin has a region-specific association with neuronal and synaptic activity in the living human brain. We investigated the regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism (CMglu), a proxy for neuronal and synaptic activity, in older adults. Method A total of 234 nondiabetic, cognitively normal (CN) older adults underwent comprehensive clinical assessment, resting-state 18F-fluodeoxyglucose (FDG)-positron emission tomography (PET) and blood sampling to determine overnight fasting blood insulin and glucose levels, as well as apolipoprotein E (APOE) genotyping. Results An exploratory voxel-wise analysis of FDG-PET without a priori hypothesis demonstrated a positive association between basal blood insulin levels and resting-state CMglu in specific cerebral cortices and hippocampus, rather than in non-specific overall cerebral regions, even after controlling for the effects of APOE e4 carrier status, vascular risk factor score, body mass index, fasting blood glucose, and demographic variables. Particularly, a positive association of basal blood insulin with CMglu in the right posterior hippocampus and adjacent parahippocampal region as well as in the right inferior parietal region remained significant after multiple comparison correction. Conversely, no region showed negative association between basal blood insulin and CMglu. Conclusions Our finding suggests that basal fasting blood insulin may have association with neuronal and synaptic activity in specific cerebral regions, particularly in the hippocampal/parahippocampal and inferior parietal regions., Highlights • We investigated regional pattern of association between basal blood insulin and resting-state cerebral glucose metabolism. • Significant clusters with positive associations were found mainly in the hippocampal and inferior parietal regions. • Our finding suggests a region-specific association of basal blood insulin with resting-state cerebral glucose metabolism. • Further studies to elucidate underlying mechanism and implication of this region-specific association will be necessary.

This work sets out to evaluate the potential benefits and pit-falls in using a priori information to help solve the Magnetoencephalographic (MEG) inverse problem. In chapter one the forward problem in MEG is introduced, together with a scheme that demonstrates how a priori information can be incorporated into the inverse problem. Chapter two contains a literature review of techniques currently used to solve the inverse problem. Emphasis is put on the kind of a priori information that is used by each of these techniques and the ease with which additional constraints can be applied. The formalism of the FOCUSS algorithm is shown to allow for the incorporation of a priori information in an insightful and straightforward manner. In chapter three it is described how anatomical constraints, in the form of a realistically shaped source space, can be extracted from a subject���s Magnetic Resonance Image (MRI). The use of such constraints relies on accurate co-registration of the MEG and MRI co-ordinate systems. Variations of the two main co-registration approaches, based on fiducial markers or on surface matching, are described and the accuracy and robustness of a surface matching algorithm is evaluated. Figures of merit introduced in chapter four are shown to given insight into the limitations of a typical measurement set-up and potential value of a priori information. It is shown in chapter five that constrained dipole fitting and FOCUSS outperform unconstrained dipole fitting when data with low SNR is used. However, the effect of errors in the constraints can reduce this advantage. Finally, it is demonstrated in chapter six that the results of different localisation techniques give corroborative evidence about the location and activation sequence of the human visual cortical areas underlying the first 125ms of the visual magnetic evoked response recorded with a whole head neuromagnetometer.

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.

Investigating human brain function in response to environmental stimuli requires freely moving humans without attached instrumentation. The RoomPET concept provides a feasible solution for human brain imaging. A RoomPET system is a room that contains an optical head tracking system and PET detectors that move to keep the head in the PET detector field-of-view. As a proof of principle, a small box-size prototype was built to demonstrate feasibility of RoomPET with gantry mechanics, tracking capabilities, and control systems.

Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.

The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion-induced signal attenuation in a twice-refocused spin-echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion-weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b-values (0-4000 s/mm2 ) from a total of 10 healthy human subjects at 3 T, yielding three FM parameter maps based on anomalous diffusion coefficient Dφ, ψ , diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE-based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b-values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal-Tanner sequence. The TRSE-based FM model also produced consistent GM-WM contrast (p < 0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal-Tanner-based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy-current perturbations at high b-values.

Some of the health risks of inhaling fine and ultrafine pollutant particles are well-established, such as asthma, lung cancer, and, most recently, heart disease. But a growing body of evidence suggests that such exposure can also harm the brain, accelerating cognitive aging, and may even increase risk of Alzheimer9s disease and other forms of dementia. The link between air pollution and dementia remains controversial—even its proponents warn that more research is needed to confirm a causal connection and work out just how the particles might enter the brain and make mischief there. But a growing number of epidemiological studies from around the world, new findings from animal models and human brain imaging studies, and increasingly sophisticated techniques for modeling PM2.5 exposures have raised alarms. Indeed, in an 11-year epidemiological study to be published next week in Translational Psychiatry, University of Southern California (USC) researchers will report that living in places with PM2.5 exposures higher than the Environmental Protection Agency9s standard of 12 μg/m3 nearly doubled dementia risk in older women. If the finding holds up in the general population, air pollution could account for roughly 21% of dementia cases worldwide, says the study9s lead author, epidemiologist Jiu-Chiuan Chen of the Keck School of Medicine at USC.

The mammalian prefrontal cortex (PFC) serves as a functional nexus point controlling subcortical neural reward and motivational pathways. The process of addiction involves the aberrant amplification of reward signals associated with drugs of abuse and the formation of powerful associative memories linked to the drug-taking experience. Our understanding of how the mammalian PFC regulates subcortical neural reward pathways has revealed remarkable insights into the complexity of addiction-related neuropathological and psychological factors. In this chapter, we review evidence from both animal models of addiction, as well as human brain imaging studies that have identified various functional roles for PFC dysfunction in the etiology and persistence of addiction-related behavioral and neural pathology. Specifically, disturbances in the ability of the PFC to modulate and control subcortical neural systems involved in signaling reward and impulse control may ultimately lead to the aberrant motivational salience, dependence, and chronic relapse associated with drugs of abuse.

We present a concept for integrating focused ultrasound (FUS) with electrophysiological neuroimaging, in order to achieve high spatio-temporal resolution brain imaging. This approach, which we are tentatively calling acousto-electrophysiological neuroimaging, leverages on the spatial focality and noninvasiveness of FUS and may potentially lead to a noninvasive human brain imaging modality with high resolution in both space and time domains. By use of modulated FUS, spatial selectivity can be accomplished for high resolution electrophysiological neuroimaging. Frequency shifting in resulting magnetic signals (using modulated focused ultrasound) may potentially open the door for a room temperature magnetoencephalography (MEG) device.

Human brain imaging has identified structural changes in gray and white matter that occur with learning. However, ascribing imaging measures to underlying cellular and molecular events is challenging. Here we review human neuroimaging findings of structural plasticity and then discuss cellular and molecular level changes that could underlie observed imaging effects. Greater dialog between researchers in these different fields would help to facilitate cross-talk between cellular and systems level explanations of how learning sculpts brain structure. © 2012 Nature America, Inc. All rights reserved.