Neural Mechanisms of Facial Emotion Recognition in Autism: Distinct Roles for Anterior Cingulate and dlPFC.

OBJECTIVE: The present study sought to measure and internally validate neural markers of facial emotion recognition (FER) in adolescents and young adults with ASD to inform targeted intervention. METHOD: We utilized fMRI to measure patterns of brain activity among individuals with ASD (N = 21) and matched controls (CON; N = 20) 2 s prior to judgments about the identity of six distinct facial emotions (happy, sad, angry, surprised, fearful, disgust). RESULTS: Predictive modeling of fMRI data (support vector classification; SVC) identified mechanistic roles for brain regions that forecasted correct and incorrect identification of facial emotion as well as sources of errors over these decisions. BOLD signal activation in bilateral insula, anterior cingulate (ACC) and right dorsolateral prefrontal cortex (dlPFC) preceded accurate FER in both controls and ASD. Predictive modeling utilizing SVC confirmed the utility of ACC in forecasting correct decisions in controls but not ASD, and further indicated that a region within the right dlPFC was the source of a type 1 error signal in ASD (i.e. neural marker reflecting an impending correct judgment followed by an incorrect behavioral response) approximately two seconds prior to emotion judgments during fMRI. CONCLUSIONS: ACC forecasted correct decisions only among control participants. Right dlPFC was the source of a false-positive signal immediately prior to an error about the nature of a facial emotion in adolescents and young adults with ASD, potentially consistent with prior work indicating that dlPFC may play a role in attention to and regulation of emotional experience.

MRI brain templates of the male Yucatan minipig.

The pig is growing in popularity as an experimental animal because its gyrencephalic brain is similar to humans. Currently, however, there is a lack of appropriate brain templates to support functional and structural neuroimaging pipelines. The primary contribution of this work is an average volume from an iterative, non-linear registration of 70 five- to seven-month-old male Yucatan minipigs. In addition, several aspects of this study are unique, including the comparison of linear and non-linear template generation, the characterization of a large and homogeneous cohort, an analysis of effective resolution after averaging, and the evaluation of potential in-template bias as well as a comparison with a template from another minipig species using a "left-out" validation set. We found that within our highly homogeneous cohort, non-linear registration produced better templates, but only marginally so. Although our T1-weighted data were resolution limited, we preserved effective resolution across the multi-subject average, produced templates that have high gray-white matter contrast and demonstrate superior registration accuracy compared to an alternative minipig template.

The Cerebellum and Implicit Sequencing: Evidence from Cerebellar Ataxia.

The cerebellum recognizes sequences from prior experiences and uses this information to generate internal models that predict future outcomes in a feedforward manner [Front Hum Neurosci 8: 475, 2014; Cortex 47: 137-44, 2011; Cerebellum 7: 611-5, 2008; J Neurosci 26: 9107-16, 2006]. This process has been well documented in the motor domain, but the cerebellum's role in cognitive sequencing, within the context of implicit versus explicit processes, is not well characterized. In this study, we tested individuals with cerebellar ataxia and healthy controls to clarify the role of the cerebellum sequencing using variations on implicit versus explicit and motor versus cognitive demands across five experiments. Converging results across these studies suggest that cerebellar feedforward mechanisms may be necessary for sequencing in the implicit domain only. In the ataxia group, rhythmic tapping, rate of motor learning, and implicit sequence learning were impaired. However, for cognitive sequencing that could be accomplished using explicit strategies, the cerebellar group performed normally, as though they shifted to extra-cerebellar mechanisms to compensate. For example, when cognitive and motor functions relied on cerebellar function simultaneously, the ataxia group's motor function was unaffected, in contrast to that of controls whose motor performance declined as a function of cognitive load. These findings indicated that the cerebellum is not critical for all forms of sequencing per se. Instead, it plays a fundamental role for sequencing within the implicit domain, whether functions are motor or cognitive. Moreover, individuals with cerebellar ataxia are generally able to compensate for cognitive sequencing when explicit strategies are available in order to preserve resources for motor function.

Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing.

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye's orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method's ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

Reinforcer pathology: Common neural substrates for delay discounting and snack purchasing in prediabetics.

Reinforcer pathology theory stipulates that individuals with both (a) high preference for smaller, immediate over larger, delayed rewards; and (b) high demand for unhealthy commodities are uniquely susceptible to poor health outcomes. Specifically, two behavioral economic tasks (delay discounting, assessing preference for smaller, immediate or larger, delayed rewards; and purchasing, assessing purchases of commodities over changes in price) have been independently associated with conditions such as overweight/obesity and problem substance use. In the present study, we examined possible shared neural regions involved in the processes of delay discounting and demand for snack foods in a prediabetic sample. Fifty-four participants completed both of these tasks. Conjunction between delay discounting and purchasing task results indicates substantial common neural substrates recruited during these two tasks, consistent with interpretations of executive control, interoception, and attention, in the prefrontal cortex, insula, and frontoparietal cortex (superior/middle frontal cortex and superior/inferior parietal lobules), respectively. Collectively, these results suggest possible neural substrates in which the two behavioral risk factors of reinforcer pathology may interact during real-world decision-making in prediabetes.

An adaptive, individualized fMRI delay discounting procedure to increase flexibility and optimize scanner time.

Research on the rate at which people discount the value of future rewards has become increasingly prevalent as discount rate has been shown to be associated with many unhealthy patterns of behavior such as drug abuse, gambling, and overeating. fMRI research points to a fronto-parietal-limbic pathway that is active during decisions between smaller amounts of money now and larger amounts available after a delay. Researchers in this area have used different variants of delay discounting tasks and reported various contrasts between choice trials of different types from these tasks. For instance, researchers have compared 1) choices of delayed monetary amounts to choices of the immediate monetary amounts, 2) 'hard' choices made near one's point of indifference to 'easy' choices that require little thought, and 3) trials where an immediate choice is available versus trials where one is unavailable, regardless of actual eventual choice. These differences in procedure and analysis make comparison of results across studies difficult. In the present experiment, we designed a delay discounting task with the intended capability of being able to construct contrasts of all three comparisons listed above while optimizing scanning time to reduce costs and avoid participant fatigue. This was accomplished with an algorithm that customized the choice trials presented to each participant with the goal of equalizing choice trials of each type. We compared this task, which we refer to here as the individualized discounting task (IDT), to two other delay discounting tasks previously reported in the literature (McClure et al., 2004; Amlung et al., 2014) in 18 participants. Results show that the IDT can examine each of the three contrasts mentioned above, while yielding a similar degree of activation as the reference tasks. This suggests that this new task could be used in delay discounting fMRI studies to allow researchers to more easily compare their results to a majority of previous research while minimizing scanning duration.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

Detecting Chronic Post-Traumatic Osteomyelitis of Mouse Tibia via an IL-13Rα2 Targeted Metallofullerene Magnetic Resonance Imaging Probe.

Differential diagnosis of chronic post-traumatic osteomyelitis (CPO) from aseptic inflammation remains challenging, since both pathological processes share similar clinical symptoms. Here we utilized a novel targeted metallofullerene nanoparticle based magnetic resonance imaging (MRI) probe IL-13-TAMRA-Gd3N@C80(OH)30(CH2CH2COOH)20 to detect CPO in mouse tibia via overexpressed IL-13Rα2 receptors. The functionalized metallofullerene was characterized by X-ray photoelectron spectroscopy. Upon lipopolysaccharide (LPS) stimulation, macrophage Raw 264.7 cells showed elevated IL-13Rα2 expression via immunofluorescence staining and increased MRI probe binding via built-in TAMRA fluorescence imaging. Trauma was induced in both tibia of mice and bacteria soaked suture was inserted into the right tibia to initiate infection. During the acute phase (1.5 weeks), luminol-bioluminescence imaging revealed much higher myeloperoxidase activity in the infected tibia compared to the sham. In the chronic phase (4 weeks), X-ray radiography illustrated bone deformation in the infected tibia compared to the sham. With T1 weighted sequences, the probe clearly exhibited hyperintensity in the infection foci at both acute and chronic phases, which was not observed in the sham tibia. Histological analysis revealed severe bone structural destruction and massive inflammatory cell infiltration in the infected tibia. Immunohistochemistry confirmed abundant expression of IL-13Rα2 in the infection site. In summary, we developed a noninvasive imaging approach to detect and differentiate CPO from aseptic inflammation using a new IL-13Rα2 targeted metallofullerene MRI probe. In addition, for the first time, IL-13Rα2 was investigated as a unique biomarker in the context of osteomyelitis. Our data established a foundation for the translational application of this MRI probe in the clinical differentiation of CPO.

Episodic Future Thinking: Expansion of the Temporal Window in Individuals with Alcohol Dependence.

BACKGROUND: Episodic future thinking (EFT) requires an individual to vividly pre-experience a realistic future event. Inspired by previous reports of reducing delay discounting following EFT in other populations, we examined the effects of engaging alcohol-dependent individuals in EFT or episodic recent thinking (ERT; control) to examine its effects on delay discounting and alcohol purchasing. METHODS: Participants (n = 50) with alcohol dependence were allocated into EFT or ERT groups and asked to generate positive future or recent past events for each of 5 time points. Participants then completed a delay-discounting task, during which event cues were displayed, and a hypothetical alcohol purchase task. RESULTS: EFT significantly increased valuation of future monetary rewards, while decreasing initial consumption (Q0 ) of alcoholic drinks indicative of lower demand intensity. Two additional findings suggest potential moderators of this effect. EFT more readily influenced individuals with lower Alcohol Use Disorders Identification Test scores, and self-reported cue valence differed between groups. CONCLUSIONS: Together, these results suggest a widening of alcohol-dependent individuals' temporal window following engagement of EFT. While our data suggest that EFT may be moderated by certain susceptibility criteria, exercises such as EFT could be easily adaptable as a potential therapeutic tool for use in rehabilitation programs.

Brain-computer interfaces increase whole-brain signal to noise.

Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

A New Interleukin-13 Amino-Coated Gadolinium Metallofullerene Nanoparticle for Targeted MRI Detection of Glioblastoma Tumor Cells.

The development of new nanoparticles as next-generation diagnostic and therapeutic ("theranostic") drug platforms is an active area of both chemistry and cancer research. Although numerous gadolinium (Gd) containing metallofullerenes as diagnostic magnetic resonance imaging (MRI) contrast agents have been reported, the metallofullerene cage surface, in most cases, consists of negatively charged carboxyl or hydroxyl groups that limit attractive forces with the cellular surface. It has been reported that nanoparticles with a positive charge will bind more efficiently to negatively charged phospholipid bilayer cellular surfaces, and will more readily undergo endocytosis. In this paper, we report the preparation of a new functionalized trimetallic nitride template endohedral metallofullerene (TNT EMF), Gd3N@C80(OH)x(NH2)y, with a cage surface bearing positively charged amino groups (-NH3(+)) and directly compare it with a similar carboxyl and hydroxyl functionalized derivative. This new nanoparticle was characterized by X-ray photoelectron spectroscopy (XPS), dynamic light scattering (DLS), and infrared spectroscopy. It exhibits excellent (1)H MR relaxivity. Previous studies have clearly demonstrated that the cytokine interleukin-13 (IL-13) effectively targets glioblastoma multiforme (GBM) cells, which are known to overexpress IL-13Rα2. We also report that this amino-coated Gd-nanoplatform, when subsequently conjugated with interleukin-13 peptide IL-13-Gd3N@C80(OH)x(NH2)y, exhibits enhanced targeting of U-251 GBM cell lines and can be effectively delivered intravenously in an orthotopic GBM mouse model.

Effect of mechanical optical clearing on near-infrared spectroscopy.

Near-infrared Spectroscopy (NIRS) is a broadly utilized technology with many emerging applications including clinical diagnostics, sports medicine, and functional neuroimaging, to name a few. For functional brain imaging NIR light is delivered at multiple wavelengths through the scalp and skull to the brain to enable spatial oximetry measurements. Dynamic changes in brain oxygenation are highly correlated with neural stimulation, activation, and function. Unfortunately, NIRS is currently limited by its low spatial resolution, shallow penetration depth, and, perhaps most importantly, signal corruption due to light interactions with superficial non-target tissues such as scalp and skull. In response to these issues, we have combined the non-invasive and rapidly reversible method of mechanical tissue optical clearing (MOC) with a commercially available NIRS system. MOC utilizes a compressive loading force on tissue, causing the lateral displacement of blood and water, while simultaneously thinning the tissue. A MOC-NIRS Breath Hold Test displayed a ∼3.5-fold decrease in the time-averaged standard deviation between channels, consequentially promoting greater channel agreement. A Skin Pinch Test was implemented to negate brain and muscle activity from affecting the recorded signal. These results displayed a 2.5-3.0 fold increase in raw signal amplitude. Existing NIRS instrumentation has been further integrated within a custom helmet device to provide a uniform force distribution across the NIRS sensor array. These results showed a gradual decrease in time-averaged standard deviation among channels with an increase in applied pressure. Through these experiments, and the development of the MOC-NIRS helmet device, MOC appears to provide enhancement of NIRS technology beyond its current limitations.

Gd3N@C84(OH)x: a new egg-shaped metallofullerene magnetic resonance imaging contrast agent.

Water-soluble derivatives of gadolinium-containing metallofullerenes have been considered to be excellent candidates for new magnetic resonance imaging (MRI) contrast agents because of their high relaxivity and characteristic encapsulation of the lanthanide ions (Gd(3+)), preventing their release into the bioenvironment. The trimetallic nitride template endohedral metallofullerenes (TNT EMFs) have further advantages of high stability, high relative yield, and encapsulation of three Gd(3+) ions per molecule as illustrated by the previously reported nearly spherical, Gd3N@I(h)-C80. In this study, we report the preparation and functionalization of a lower-symmetry EMF, Gd3N@C(s)-C84, with a pentalene (fused pentagons) motif and an egg-shaped structure. The Gd3N@C84 derivative exhibits a higher (1)H MR relaxivity compared to that of the Gd3N@C80 derivative synthesized the same way, at low (0.47 T), medium (1.4 T), and high (9.4 T) magnetic fields. The Gd3N@C(s)-C84 derivative exhibits a higher hydroxyl content and aggregate size, as confirmed by X-ray photoelectron spectroscopy (XPS) and dynamic light scattering (DLS) experiments, which could be the main reasons for the higher relaxivity.

Predicting intrinsic brain activity.

Multivariate supervised learning methods exhibit a remarkable ability to decode externally driven sensory, behavioral, and cognitive states from functional neuroimaging data. Although they are typically applied to task-based analyses, supervised learning methods are equally applicable to intrinsic effective and functional connectivity analyses. The obtained models of connectivity incorporate the multivariate interactions between all brain regions simultaneously, which will result in a more accurate representation of the connectome than the ones available with standard bivariate methods. Additionally the models can be applied to decode or predict the time series of intrinsic brain activity of a region from an independent dataset. The obtained prediction accuracy provides a measure of the integration between a brain region and other regions in its network, as well as a method for evaluating acquisition and preprocessing pipelines for resting state fMRI data. This article describes a method for learning multivariate models of connectivity. The method is applied in the non-parametric prediction accuracy, influence, and reproducibility-resampling (NPAIRS) framework, to study the regional variation of prediction accuracy and reproducibility (Strother et al., 2002). The resulting spatial distribution of these metrics is consistent with the functional hierarchy proposed by Mesulam (1998). Additionally we illustrate the utility of the multivariate regression connectivity modeling method for optimizing experimental parameters and assessing the quality of functional neuroimaging data.

Harnessing graphics processing units for improved neuroimaging statistics.

Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.

Development of a Minipig Model of BINT From Blast Exposure Using a Repeatable Mobile Shock Expansion Tube.

INTRODUCTION: The Office of Naval Research (ONR) sponsored the Blast Load Assessment Sense and Test (BLAST) program to provide an approach to operationally relevant monitoring and analysis of blast exposure for optimization of service member performance and health. Of critical importance in this effort was the development of a standardized methodology for preclinical large animal studies that can reliably produce outcome measures that cannot be measured in human studies to support science-based guidelines. The primary advantage of this approach is that, because animal studies report physiological measures that correlate with human neuropathology, these data can be used to evaluate potential risks to service members by accounting for the anatomical and physiological differences between humans and large animal models. This article describes the methodology used to generate a comprehensive outcome measure dataset correlated with controlled blast exposure. METHODS AND MATERIALS: To quantify outcomes associated with a single exposure to blast, 23 age- and weight-matched Yucatan minipigs were exposed to a single blast event generated by a large-bore, compressed gas shock tube. The peak pressure ranged from 280 to 525 kPa. After a post-exposure 72-hour observation period, the physiological response was quantified using a comprehensive set of neurological outcome measures that included neuroimaging, histology, and behavioral measures. Responses of the blast-exposed animals were compared to the sham-treated cohort to identify statistically significant and physiologically relevant differences between the two groups. RESULTS: Following a single exposure, the minipigs were assessed for structural, behavioral, and cellular changes for 3 days after exposure. The following neurological changes were observed: Structural-Using Diffusion Tensor Imaging, a statistically significant decrement (P < .001) in Fractional Anisotropy across the entire volume of the brain was observed when comparing the exposed group to the sham group. This finding indicates that alterations in brain tissue following exposure are not focused at a single location but instead a diffuse brain volume that can only be observed through a systematic examination of the neurological tissue. Cellular-The histopathology results from several large white matter tract locations showed varied cellular responses from six different stains. Using standard statistical methods, results from stains such as Fluoro-Jade C and cluster of differentiation 68 in the hippocampus showed significantly higher levels of neurodegeneration and increased microglia/macrophage activation in blast-exposed subjects. However, other stains also indicated increased response, demonstrating the need for multivariate analysis with a larger dataset. Behavioral-The behavior changes observed were typically transient; the animals' behavior returned to near baseline levels after a relatively short recovery period. Despite behavioral recovery, the presence of active neurodegenerative and inflammatory responses remained. CONCLUSIONS: The results of this study demonstrate that (1) a shock tube provides an effective tool for generating repeatable exposures in large animals and (2) exposure to blast overpressure can be correlated using a combination of imaging, behavioral, and histological analyses. This research demonstrates the importance of using multiple physiological indicators to track blast-induced changes in minipigs. The methodology and findings from this effort were central to developing machine-learning models to inform the development of blast exposure guidelines.

Longitudinal Associations Between Blood Biomarkers and White Matter MRI in Sport-Related Concussion: A Study of the NCAA-DoD CARE Consortium.

BACKGROUND AND OBJECTIVES: To study longitudinal associations between blood-based neural biomarkers (including total tau, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], and ubiquitin C-terminal hydrolase-L1) and white matter neuroimaging biomarkers in collegiate athletes with sport-related concussion (SRC) from 24 hours postinjury to 1 week after return to play. METHODS: We analyzed clinical and imaging data of concussed collegiate athletes in the Concussion Assessment, Research, and Education (CARE) Consortium. The CARE participants completed same-day clinical assessments, blood draws, and diffusion tensor imaging (DTI) at 3 time points: 24-48 hours postinjury, point of becoming asymptomatic, and 7 days after return to play. DTI probabilistic tractography was performed for each participant at each time point to render 27 participant-specific major white matter tracts. The microstructural organization of these tracts was characterized by 4 DTI metrics. Mixed-effects models with random intercepts were applied to test whether white matter microstructural abnormalities are associated with the blood-based biomarkers at the same time point. An interaction model was used to test whether the association varies across time points. A lagged model was used to test whether early blood-based biomarkers predict later microstructural changes. RESULTS: Data from 77 collegiate athletes were included in the following analyses. Among the 4 blood-based biomarkers, total tau had significant associations with the DTI metrics across the 3 time points. In particular, high tau level was associated with high radial diffusivity (RD) in the right corticospinal tract (ß = 0.25, SE = 0.07, p FDR-adjusted = 0.016) and superior thalamic radiation (ß = 0.21, SE = 0.07, p FDR-adjusted = 0.042). NfL and GFAP had time-dependent associations with the DTI metrics. NfL showed significant associations only at the asymptomatic time point (|ß|s > 0.12, SEs <0.09, psFDR-adjusted < 0.05) and GFAP showed a significant association only at 7 days after return to play (ßs > 0.14, SEs <0.06, psFDR-adjusted < 0.05). The p values for the associations of early tau and later RD were not significant after multiple comparison adjustment, but were less than 0.1 in 7 white matter tracts. DISCUSSION: This prospective study using data from the CARE Consortium demonstrated that in the early phase of SRC, white matter microstructural integrity detected by DTI neuroimaging was associated with elevated levels of blood-based biomarkers of traumatic brain injury. Total tau in the blood showed the strongest association with white matter microstructural changes.

Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery.

Functional MRI neurofeedback (fMRI NF) is an emerging technique that trains subjects to regulate their brain activity while they manipulate sensory stimulus representations of fMRI signals in "real-time". Here we report an fMRI NF study of brain activity associated with kinesthetic motor imagery (kMI), analyzed using partial least squares (PLS), a multivariate analysis technique. Thirteen healthy young adult subjects performed kMI involving each hand separately, with NF training targeting regions of interest (ROIs) in the left and right primary motor cortex (M1). Throughout, subjects attempted to maximize a laterality index (LI) of brain activity-the difference in activity between the contralateral ROI (relative to the hand involved in kMI) and the ipsilateral M1 ROI-while receiving real-time updates on a visual display. Six of 13 subjects were successful in increasing the LI value, whereas the other 7 were not successful and performed similarly to 5 control subjects who received sham NF training. Ability to suppress activity in the ipsilateral M1 ROI was the primary driver of successful NF performance. Multiple PLS analyses depicted activated networks of brain regions involved with imagery, self-awareness, and feedback processing, and additionally showed that activation of the task positive network was correlated with task performance. These results indicate that fMRI NF of kMI is capable of modulating brain activity in primary motor regions in a subset of the population. In the future, such methods may be useful in the development of NF training methods for enhancing motor rehabilitation following stroke.

Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks.

Existing spatial independent component analysis (ICA) methods for multi-subject fMRI datasets have mainly focused on detecting common components across subjects, under the assumption that all the subjects in a group share the same (identical) components. However, as a data-driven approach, ICA could potentially serve as an exploratory tool at multi-subject level, and help us uncover inter-subject differences in patterns of connectivity (e.g., find subtypes in patient populations). In this work, we propose a methodology named gRAICAR that exploits the data-driven nature of ICA to allow discovery of sub-groupings of subjects based on reproducibility of their ICA components. This technique allows us not only to find highly reproducible common components across subjects but also to explore (without a priori subject groupings) components that could classify all subjects into sub-groups. gRAICAR generalizes the reproducibility framework previously developed for single subjects (Ranking and averaging independent component analysis by reproducibility-RAICAR-Yang et al., Hum Brain Mapp, 2008) to multiple-subject analysis. For each group-level component, gRAICAR generates its reproducibility matrix and further computes two metrics, inter-subject consistency and intra-subject reliability, to characterize inter-subject variability and reflect contributions from individual subjects. Nonparametric tests are employed to examine the significance of both the inter-subject consistency and the separation of subject groups reflected in the component. Our validations based on simulated and experimental resting-state fMRI datasets demonstrated the advantage of gRAICAR in extracting features reflecting potential subject groupings. It may facilitate discovery of the underlying brain functional networks with substantial potential to inform our understandings of development, neurodegenerative conditions, and psychiatric disorders.

Decoding the Brain's Surface to Track Deeper Activity.

Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex's connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.