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Molecular and functional imaging techniques have been used and combined with pharmacological probes to evaluate the role of dopamine in impulsivity. Overall, strong evidence links striatal dopaminergic function with impulsivity, measured by self-reports and laboratory tests of cognitive control and reward-based decision-making. The combination of molecular imaging using positron emission tomography (PET) with functional magnetic resonance imaging (fMRI) specifically implicates striatal D2-type dopamine receptors (i.e., D2 and D3) and corticostriatal connectivity in cognitive control. Low levels of striatal and midbrain D2-type receptor availability correlate with self-reported impulsivity, whereas striatal D2-type receptor availability shows positive correlation with motor response inhibition and cognitive flexibility. Impulsive choice on reward-based decision-making tasks also is related to deficits in striatal D2-type dopamine receptor availability, and there is evidence for an inverted U-shaped function in this relationship, reflecting an optimum of striatal dopaminergic activity. Findings from studies of clinical populations that present striatal dopamine D2-type receptor deficits as well as healthy control research participants identify D2-type receptors as therapeutic targets to improve cognitive control.

Nicotinic acetylcholinergic receptors (nAChR's) have been implicated in several brain disorders, including addiction, Parkinson's disease, Alzheimer's disease and schizophrenia. Here we report in vitro selectivity and functional properties, toxicity in rats, in vivo evaluation in humans, and comparison across species of [18 F]Nifene, a fast acting PET imaging agent for α4β2* nAChRs. Nifene had subnanomolar affinities for hα2β2 (0.34 nM), hα3β2 (0.80 nM) and hα4β2 (0.83 nM) nAChR but weaker (27-219 nM) for hβ4 nAChR subtypes and 169 nM for hα7 nAChR. In functional assays, Nifene (100 μM) exhibited 14% agonist and >50% antagonist characteristics. In 14-day acute toxicity in rats, the maximum tolerated dose (MTD) and the no observed adverse effect level (NOAEL) were estimated to exceed 40 μg/kg/day (278 μg/m2 /day). In human PET studies, [18 F]Nifene (185 MBq; 15% than males. No significant aging effect was observed in [18 F]Nifene binding over 5 decades. In all species (mice, rats, monkeys, and humans) thalamus showed highest [18 F]Nifene binding with reference region ratios >2 compared to extrathalamic regions. Our findings suggest that [18 F]Nifene PET may be used to study α4β2* nAChRs in various CNS disorders and for translational research.

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.

The neuroscience of music and music-based interventions (MBIs) is a fascinating but challenging research field. While music is a ubiquitous component of every human society, MBIs may encompass listening to music, performing music, music-based movement, undergoing music education and training, or receiving treatment from music therapists. Unraveling the brain circuits activated and influenced by MBIs may help us gain better understanding of the therapeutic and educational values of MBIs by gathering strong research evidence. However, the complexity and variety of MBIs impose unique research challenges. This article reviews the recent endeavor led by the National Institutes of Health to support evidence-based research of MBIs and their impact on health and diseases. It also highlights fundamental challenges and strategies of MBI research with emphases on the utilization of animal models, human brain imaging and stimulation technologies, behavior and motion capturing tools, and computational approaches. It concludes with suggestions of basic requirements when studying MBIs and promising future directions to further strengthen evidence-based research on MBIs in connections with brain circuitry.SIGNIFICANCE STATEMENTMusic and music-based interventions (MBI) engage a wide range of brain circuits and hold promising therapeutic potentials for a variety of health conditions. Comparative studies using animal models have helped in uncovering brain circuit activities involved in rhythm perception, while human imaging, brain stimulation, and motion capture technologies have enabled neural circuit analysis underlying the effects of MBIs on motor, affective/reward, and cognitive function. Combining computational analysis, such as prediction method, with mechanistic studies in animal models and humans may unravel the complexity of MBIs and their effects on health and disease.

In human brain imaging studies, researchers have predominantly used the correlation method to estimate the “functional connectivity”. However, spurious correlations can be measured between two brain regions without even having direct connection or interaction between them. This high correlation can be due to the strong interactions of the two brain regions with common input from a third region which may or may not be observed. One previously proposed solution to this problem is to use a sparse-regularized inverse covariance matrix or precision matrix (SRPM) when all the regions are observed. The SRPM method yields partial correlations to measure strong direct interactions between pairs of observed regions while simultaneously removing the influence of the rest of the observed regions thus identifying observed regions that are conditionally independent. However, in most brain studies, especially in electrocorticography (ECoG), simultaneously recording all brain regions is a near impossible task using existing imaging technologies. Hence researchers have recently proposed to use a sparse-plus-latent-regularized precision matrix (SLRPM) when there are unobserved or latent regions interacting with the observed regions. The SLRPM method yields partial correlations of the conditional statistics of the observed regions given the latent regions thus identifying observed regions that are conditionally independent of both the observed and latent regions.In the first contribution of the thesis, we evaluate the performance of the SRPM and SLRPM methods using simple artificial networks. In the second contribution of the thesis, we demonstrate the application of the SRPM method for estimating brain connectivity duringstage 2 sleep spindles from human ECoG recordings from a patient with complex partial epilepsy. We only consider sleep spindles occurring in long seizure-free periods. Sleep spindles are automatically detected using delay-differential analysis (DDA), then analyzedwith SRPM and the Louvain method for community detection (LMCD). The performance of the SLRPM method is found to be similar to that of the SRPM method for this application. In the third contribution of the thesis, we demonstrate the application of the SLRPM method to estimate brain connectivity during epileptic seizures from human ECoG recordings. Seizures from 12 patients having complex partial epilepsy were analyzed using SLRPM and brain connectivity has been quantified using modularity index (MI), clustering coefficient (CC), and eigenvector centrality (EC). These applications will enhance our understanding of the global connectivity pattern of the human brain and may help us in finding better treatments in epilepsy.

Background Neurodevelopmental disorders including Tourette’s syndrome (TS) and attention deficit hyperactivity disorder (ADHD) are characterized by significant impairment in attention and cognitive control. These cognitive deficits persist throughout development, contribute significantly to socio-occupational impairment, and are relatively impervious to available treatment. A critical challenge in pro-cognitive drug discovery is translatability of findings across species, underscoring the need for developing valid and reliable cross-species cognitive tasks. New method Here we describe a cross-species 5 choice continuous performance task that was developed to measure cognitive control processes of attention, vigilance, and response inhibition, enabling the translation of findings for pro-cognitive drug discovery across species and delineate neural mechanisms underlying cognitive control construct. Results Construct validity of 5C-CPT has been verified by multiple cross-species studies. Several lines of evidence report consistent findings across species including, deficits resulting from 36-h sleep deprivation studies, engagement of parietal cortex in human brain imaging and rodent lesion studies, and vigilance decrements over time. Comparison with existing method : Unlike the widely used rodent 5 choice serial reaction time task (5CSRTT) and the sustained attention task (SAT), the rodent 5C-CPT includes both target and non-target stimuli that allow measuring of cognitive control elements including response inhibition, an ability to inhibit pre-potent response during non-target trials, detect vigilance decrement and calculate signal detection parameters in rodents analogous to human CPT. Conclusion The cross-species 5C-CPT is a robust translational tool to characterize the neurobiological substrates underlying cognitive control deficits in clinical population including, ADHD and TS and develop targeted pro-cognitive therapeutics.

Recent work with noninvasive human brain imaging has started to investigate the effects of 3,4-methylenedioxymethamphetamine (MDMA) on large-scale patterns of brain activity. MDMA, a potent monoamine-releaser with particularly pronounced serotonin- releasing properties, has unique subjective effects that include: marked positive mood, pleasant/unusual bodily sensations and pro-social, empathic feelings. However, the neurobiological basis for these effects is not properly understood, and the present analysis sought to address this knowledge gap. To do this, we administered MDMA-HCl (100 mg p.o.) and, separately, placebo (ascorbic acid) in a randomized, double-blind, repeated-measures design with twenty-five healthy volunteers undergoing fMRI scanning. We then employed a measure of global resting-state functional brain connectivity and follow-up seed-to-voxel analysis to the fMRI data we acquired. Results revealed decreased right insula/salience network functional connectivity under MDMA. Furthermore, these decreases in right insula/salience network connectivity correlated with baseline trait anxiety and acute experiences of altered bodily sensations under MDMA. The present findings highlight insular disintegration (ie, compromised salience network membership) as a neurobiological signature of the MDMA experience, and relate this brain effect to trait anxiety and acutely altered bodily sensations–both of which are known to be associated with insular functioning.

The human brain consists of multiple interconnected large scale networks. Recent advances in structural and functional neuroimaging techniques have introduced new concepts and approaches to noninvasively assess brain networks, which has attracted enormous research interest. To examine human brain function, traditional neuroimaging research typically focuses on localizing neural responses to varying external stimuli or task demands. However, task-induced phasic neuronal responses account for less than 5% of the brain's energy budget, while the majority of the brain's energy is used for intrinsic spontaneous brain activities. Although the number of human brain imaging studies and the knowledge obtained from these studies are growing exponentially, there are many more unanswered questions regarding the structural organization and the function of brain networks. In this special issue, we have invited a few papers that studied structural and functional brain networks using various neuroimaging methods including resting-state fMRI, diffusion tensor imaging (DTI), arterial spin labeling (ASL) perfusion MRI, functional near-infrared spectroscopy (fNIRS), magnetoencephalogram (MEG), and imaging genetics. These studies involve healthy populations as well as multiple clinical populations, including patients with cirrhosis, mild cognitive impairment (MCI) and Alzheimer's disease (AD), attention deficit hyperactivity disorder (ADHD), and epilepsy. This specific issue includes several studies focusing on clinical populations. In one study by G. Zheng and colleagues, resting-state fMRI was used to examine functional connectivity changes in the progression of minimal hepatic encephalopathy and found continuously impaired intra- and intermodular connectivity in patients with cirrhosis. In another study, Y.-Y. Wai and colleagues used tract-based spatial statistics of DTI to characterize white matter integrity in different subtypes of MCI, including amnestic MCI, dysexecutive MCI, AD, and healthy controls. They found that amnestic MCI and AD patients share significant white matter damage, whereas dysexecutive MCI patients appear to have a distinct pathogenesis. In the third study, A. dos Santos Siqueira and colleagues used graph theory and analyzed a resting-state fMRI data set from healthy children and ADHD patients in the ADHD-200 database. Distinct patterns of network dysfunction were evident for both inattentive and combined ADHD subtypes. However, the authors observed that the classification scores for discriminating between ADHD and healthy subjects were close to chance and that functional connectivity estimations were strongly dependent on the sample characteristics. In the final study, H. Zhu and colleagues used resting-state MEG to identify the epileptogenic and other abnormal regions in patients with left temporal lobe epilepsy (TLE) and provided further evidence supporting the notion that TLE is not a focal focus but a multifocus disease. A few other studies in this specific issue examined brain networks and function in normal population. In one study by H. Lei and colleagues, resting-state fMRI was integrated with genetics to examine the effect of genetic variation of the MAOA gene on resting brain activity in healthy male adolescents. Those with the low-activity MAOA genotype exhibited lower amplitude of low-frequency fluctuation (ALFF) in the pons compared to those with the high-activity MAOA genotype. Moreover, there was a significant genotype by ALFF interaction effect on impulsivity scores. In another study, H. Niu and colleagues examined the effects of learning on resting brain activity using fNIRS and demonstrated that 5 days of visual perceptual learning significantly altered resting oxygenated hemoglobin functional connectivity between visual cortex and frontal/central areas. In the study by C. Xu and colleagues, the regional homogeneity (ReHo) approach was used as an index in the resting-state fMRI to investigate gender differences in spontaneous brain activity among a large sample of young healthy adults. This study found significant ReHo differences in multiple cortical regions and resting-state networks between male and female subjects. This specific issue also includes two studies on neuroimaging methods and one review paper. In one study, X. Li and colleagues compared FAIR and PICORE sequences and systematically optimized ASL imaging parameters for measuring cerebellum grey matter and white matter perfusion. In another study, M. Gorges and colleagues demonstrated a potential framework for multiparametric functional connectivity mapping through combination of resting-state fMRI with DTI. Finally, in the review paper, T. K. Das and colleagues highlighted the structure-function relationship of the brain networks using the Ising model and graph theory. In summary, studies published in this specific issue demonstrate a few examples of continuous efforts to integrate structural and functional neuroimaging methods as well as other research modalities in order to elucidate brain networks and their function in both normal healthy population and clinical patients. Hengyi Rao Danny Jiongjiong Wang Yihong Yang Yong He

A new paradigm for human brain imaging, mobile brain/body imaging (MoBI), involves synchronous collection of human brain activity (via electroencephalography, EEG) and behavior (via body motion capture, eye tracking, etc.), plus environmental events (scene and event recording) to study joint brain/body dynamics supporting natural human cognition supporting performance of naturally motivated human actions and interactions in 3-D environments (Makeig et al., 2009)⁠. Processing complex, concurrent, multi-modal, multi-rate data streams requires a signal-processing environment quite different from one designed to process single-modality time series data. Here we describe MoBILAB (more details available at sccn.ucsd.edu/wiki/MoBILAB), an open source, cross platform toolbox running on MATLAB (The Mathworks, Inc.) that supports analysis and visualization of any mixture of synchronously recorded brain, behavioral, and environmental time series plus time-marked event stream data. MoBILAB can serve as a pre-processing environment for adding behavioral and other event markers to EEG data for further processing, and/or as a development platform for expanded analysis of simultaneously recorded data streams.