The neurophysiological basis of variability in the latency of evoked neural responses has been of interest for decades. We describe a method to identify white matter pathways that may contribute to inter-individual variability in the timing of neural activity. We investigated the relation of the latency of peak visual responses in occipital cortex as measured by magnetoencephalography (MEG) to fractional anisotropy (FA) in the entire brain as measured with diffusion tensor imaging (DTI) in eight healthy young adults. This method makes no assumptions about the anatomy of white matter connections. Visual responses were evoked during a saccadic paradigm and were time-locked to arrival at a saccadic goal. The latency of the peak visual response was inversely related to FA in bilateral parietal and right lateral frontal white matter adjacent to cortical regions that modulate early visual responses. These relations suggest that biophysical properties of white matter affect the timing of early visual responses. This preliminary report demonstrates a non-invasive, unbiased method to relate the timing information from evoked-response experiments to the biophysical properties of white matter measured with DTI.

Diffusion magnetic resonance imaging (MRI) methods for axon diameter mapping benefit from higher maximum gradient strengths than are currently available on commercial human scanners. Using a dedicated high-gradient 3T human MRI scanner with a maximum gradient strength of 300 mT/m, we systematically studied the effect of gradient strength on in vivo axon diameter and density estimates in the human corpus callosum. Pulsed gradient spin echo experiments were performed in a single scan session lasting approximately 2h on each of three human subjects. The data were then divided into subsets with maximum gradient strengths of 77, 145, 212, and 293 mT/m and diffusion times encompassing short (16 and 25 ms) and long (60 and 94 ms) diffusion time regimes. A three-compartment model of intra-axonal diffusion, extra-axonal diffusion, and free diffusion in cerebrospinal fluid was fitted to the data using a Markov chain Monte Carlo approach. For the acquisition parameters, model, and fitting routine used in our study, it was found that higher maximum gradient strengths decreased the mean axon diameter estimates by two to three fold and decreased the uncertainty in axon diameter estimates by more than half across the corpus callosum. The exclusive use of longer diffusion times resulted in axon diameter estimates that were up to two times larger than those obtained with shorter diffusion times. Axon diameter and density maps appeared less noisy and showed improved contrast between different regions of the corpus callosum with higher maximum gradient strength. Known differences in axon diameter and density between the genu, body, and splenium of the corpus callosum were preserved and became more reproducible at higher maximum gradient strengths. Our results suggest that an optimal q-space sampling scheme for estimating in vivo axon diameters should incorporate the highest possible gradient strength. The improvement in axon diameter and density estimates that we demonstrate from increasing maximum gradient strength will inform protocol development and encourage the adoption of higher maximum gradient strengths for use in commercial human scanners.

Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data.

Functional MRI has been used to map brain activity and functional connectivity based on the strength and temporal coherence of neurovascular-coupled hemodynamic signals. Here, single-vessel fMRI reveals vessel-specific correlation patterns in both rodents and humans. In anesthetized rats, fluctuations in the vessel-specific fMRI signal are correlated with the intracellular calcium signal measured in neighboring neurons. Further, the blood-oxygen-level-dependent (BOLD) signal from individual venules and the cerebral-blood-volume signal from individual arterioles show correlations at ultra-slow (<0.1 Hz), anesthetic-modulated rhythms. These data support a model that links neuronal activity to intrinsic oscillations in the cerebral vasculature, with a spatial correlation length of ∼2 mm for arterioles. In complementary data from awake human subjects, the BOLD signal is spatially correlated among sulcus veins and specified intracortical veins of the visual cortex at similar ultra-slow rhythms. These data support the use of fMRI to resolve functional connectivity at the level of single vessels.

The neural processes that support inhibitory control in the face of stimuli with a history of reward association are not yet well understood. Yet, the ability to flexibly adapt behavior to changing reward-contingency contexts is important for daily functioning and warrants further investigation. This study aimed to characterize neural and behavioral impacts of stimuli with a history of conditioned reward association on motor inhibitory control in healthy young adults by investigating group-level effects as well as individual variation in the ability to inhibit responses to stimuli with a reward history. Participants (N = 41) first completed a reward conditioning phase, during which responses to rewarded stimuli were associated with money and responses to unrewarded stimuli were not. Rewarded and unrewarded stimuli from training were carried forward as No-Go targets in a subsequent go/no-go task to test the effect of reward history on inhibitory control. Participants underwent functional brain imaging during the go/no-go portion of the task. On average, a history of reward conditioning disrupted inhibitory control. Compared to inhibition of responses to stimuli with no reward history, trials that required inhibition of responses to previously rewarded stimuli were associated with greater activity in frontal and striatal regions, including the inferior frontal gyrus, insula, striatum, and thalamus. Activity in the insula and thalamus during false alarms and in the ventromedial prefrontal cortex during correctly withheld trials predicted behavioral performance on the task. Overall, these results suggest that reward history serves to disrupt inhibitory control and provide evidence for diverging roles of the insula and ventromedial prefrontal cortex while inhibiting responses to stimuli with a reward history.

The temporal monocular crescent (TMC) is the most peripheral portion of the visual field whose perception relies solely on input from the ipsilateral eye. According to a handful of post-mortem histological studies in humans and non-human primates, the TMC is represented visuotopically within the most anterior portion of the primary visual cortical area (V1). However, functional evidence of the TMC visuotopic representation in human visual cortex is rare, mostly due to the small size of the TMC representation (~6% of V1) and due to the technical challenges of stimulating the most peripheral portion of the visual field inside the MRI scanner. In this study, by taking advantage of custom-built MRI-compatible visual stimulation goggles with curved displays, we successfully stimulated the TMC region of the visual field in eight human subjects, half of them right-eye dominant, inside a 3 ​T MRI scanner. This enabled us to localize the representation of TMC, along with the blind spot representation (another visuotopic landmark in V1), in all volunteers, which match the expected spatial pattern based on prior anatomical studies. In all hemispheres, the TMC visuotopic representation was localized along the peripheral border of V1, within the most anterior portion of the calcarine sulcus, without any apparent extension into the second visual area (V2). We further demonstrate the reliability of this localization within/across experimental sessions, and consistency in the spatial location of TMC across individuals after accounting for inter-subject structural differences.

Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.

Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.

Physiological noise arising from a variety of sources can significantly degrade the detection of task-related activity in BOLD-contrast fMRI experiments. If whole head spatial coverage is desired, effective suppression of oscillatory physiological noise from cardiac and respiratory fluctuations is quite difficult without external monitoring, since traditional EPI acquisition methods cannot sample the signal rapidly enough to satisfy the Nyquist sampling theorem, leading to temporal aliasing of noise. Using a combination of high speed magnetic resonance inverse imaging (InI) and digital filtering, we demonstrate that it is possible to suppress cardiac and respiratory noise without auxiliary monitoring, while achieving whole head spatial coverage and reasonable spatial resolution. Our systematic study of the effects of different moving average (MA) digital filters demonstrates that a MA filter with a 2 s window can effectively reduce the variance in the hemodynamic baseline signal, thereby achieving 57%-58% improvements in peak z-statistic values compared to unfiltered InI or spatially smoothed EPI data (FWHM = 8.6 mm). In conclusion, the high temporal sampling rates achievable with InI permit significant reductions in physiological noise using standard temporal filtering techniques that result in significant improvements in hemodynamic response estimation.

Deqi response, a psychophysical response characterized by a spectrum of different needling sensations, is essential for Chinese acupuncture clinical efficacy. Previous neuroimaging research works have investigated the neural correlates of an overall deqi response by summating the scores of different needling sensations. However, the roles of individual sensations in brain activity and how they interact with each other remain to be clarified. In this study, we applied fMRI to investigate the neural correlates of individual components of deqi during acupuncture on the right LV3 (Taichong) acupoint. We selected a subset of deqi responses, namely, pressure, heaviness, fullness, numbness, and tingling. Using the individual components of deqi of different subjects as covariates in the analysis of percentage change of bold signal, pressure was found to be a striking sensation, contributing to most of negative activation of a limbic-paralimbic-neocortical network (LPNN). The similar or opposite neural activity in the heavily overlapping regions is found to be responding to different needling sensations, including bilateral LPNN, right orbitofrontal cortex, and bilateral posterior parietal cortex. These findings provide the neuroimaging evidence of how the individual needle sensations interact in the brain, showing that the modulatory effects of different needling sensations contribute to acupuncture modulations of LPNN network.

Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI) is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD) contrast based whole-head inverse imaging (InI). Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

Evidence from human post mortem, in vivo and animal model studies implicates the neuroimmune system and activated microglia in the pathology of amyotrophic lateral sclerosis. The study aim was to further evaluate in vivo neuroinflammation in individuals with amyotrophic lateral sclerosis using [(11)C]-PBR28 positron emission tomography. Ten patients with amyotrophic lateral sclerosis (seven males, three females, 38-68 years) and ten age- and [(11)C]-PBR28 binding affinity-matched healthy volunteers (six males, four females, 33-65 years) completed a positron emission tomography scan. Standardized uptake values were calculated from 60 to 90 min post-injection and normalized to whole brain mean. Voxel-wise analysis showed increased binding in the motor cortices and corticospinal tracts in patients with amyotrophic lateral sclerosis compared to healthy controls (p FWE < 0.05). Region of interest analysis revealed increased [(11)C]-PBR28 binding in the precentral gyrus in patients (normalized standardized uptake value = 1.15) compared to controls (1.03, p < 0.05). In patients those values were positively correlated with upper motor neuron burden scores (r = 0.69, p < 0.05), and negatively correlated with the amyotrophic lateral sclerosis functional rating scale (r = -0.66, p < 0.05). Increased in vivo glial activation in motor cortices, that correlates with phenotype, complements previous histopathological reports. Further studies will determine the role of [(11)C]-PBR28 as a marker of treatments that target neuroinflammation.

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset's utility.

Mesopontine tegmental nuclei such as the cuneiform, pedunculotegmental, oral pontine reticular, paramedian raphe and caudal linear raphe nuclei, are deep brain structures involved in arousal and motor function. Dysfunction of these nuclei is implicated in the pathogenesis of disorders of consciousness and sleep, as well as in neurodegenerative diseases. However, their localization in conventional neuroimages of living humans is difficult due to limited image sensitivity and contrast, and a stereotaxic probabilistic neuroimaging template of these nuclei in humans does not exist. We used semi-automatic segmentation of single-subject 1.1mm-isotropic 7T diffusion-fractional-anisotropy and T2-weighted images in healthy adults to generate an in vivo probabilistic neuroimaging structural template of these nuclei in standard stereotaxic (Montreal Neurological Institute, MNI) space. The template was validated through independent manual delineation, as well as leave-one-out validation and evaluation of nuclei volumes. This template can enable localization of five mesopontine tegmental nuclei in conventional images (e.g. 1.5T, 3T) in future studies of arousal and motor physiology (e.g. sleep, anesthesia, locomotion) and pathology (e.g. disorders of consciousness, sleep disorders, Parkinson's disease). The 7T magnetic resonance imaging procedure for single-subject delineation of these nuclei may also prove useful for future 7T studies of arousal and motor mechanisms.

Glia activation is thought to contribute to neuronal damage in several neurodegenerative diseases based on preclinical and human post-mortem studies, but its role in primary lateral sclerosis (PLS) is unknown.

Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .

The aim of this study was to investigate functional reorganization of motor systems by probing connectivity between motor related areas in chronic stroke patients using functional magnetic resonance imaging (fMRI) in conjunction with a novel MR-compatible hand-induced, robotic device (MR_CHIROD). We evaluated data sets obtained from healthy volunteers and right-hand-dominant patients with first-ever left-sided stroke > or =6 months prior and mild to moderate hemiparesis affecting the right hand. We acquired T1-weighted echo planar and fluid attenuation inversion recovery MR images and multi-level fMRI data using parallel imaging by means of the GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) algorithm on a 3 T MR system. Participants underwent fMRI while performing a motor task with the MR_CHIROD in the MR scanner. Changes in effective connectivity among a network of primary motor cortex (M1), supplementary motor area (SMA) and cerebellum (Ce) were assessed using dynamic causal modeling. Relative to healthy controls, stroke patients exhibited decreased intrinsic neural coupling between M1 and Ce, which was consistent with a dysfunctional M1 to Ce connection. Stroke patients also showed increased SMA to M1 and SMA to cerebellum coupling, suggesting that changes in SMA and Ce connectivity may occur to compensate for a dysfunctional M1. The results demonstrate for the first time that connectivity alterations between motor areas may help counterbalance a functionally abnormal M1 in chronic stroke patients. Assessing changes in connectivity by means of fMRI and MR_CHIROD might be used in the future to further elucidate the neural network plasticity that underlies functional recovery in chronic stroke patients.

During adolescence, rapid development and reorganization of the dopaminergic system supports increasingly sophisticated reward learning and the ability to exert behavioral control. Disruptions in the ability to exert control over previously rewarded behavior may underlie some forms of adolescent psychopathology. Specifically, symptoms of externalizing psychopathology may be associated with difficulties in flexibly adapting behavior in the context of reward. However, the direct interaction of cognitive control and reward learning in adolescent psychopathology symptoms has not yet been investigated. The present study used a Research Domain Criteria framework to investigate whether behavioral and neuronal indices of inhibition to previously rewarded stimuli underlie individual differences in externalizing symptoms in N = 61 typically developing adolescents. Using a task that integrates the Monetary Incentive Delay and Go-No-Go paradigms, we observed a positive association between externalizing symptoms and activation of the left middle frontal gyrus during response inhibition to cues with a history of reward. These associations were robust to controls for internalizing symptoms and neural recruitment during inhibition of cues with no reward history. Our findings suggest that inhibitory control over stimuli with a history of reward may be a useful marker for future inquiry into the development of externalizing psychopathology in adolescence.

In this cross-sectional study, we aimed to evaluate brain structural abnormalities in relation to glial activation in the same cohort of participants.