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.

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.

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.

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.

Future thinking to decrease real-world drinking in alcohol use disorder: Repairing reinforcer pathology in a randomized proof-of-concept trial.

Reinforcer Pathology theory proposes that expanding the temporal window of reinforcement (i.e., reducing delay discounting) using episodic future thinking (EFT) would decrease alcohol consumption. However, evidence of effectiveness in real-world settings is lacking. Using a randomized proof-of-concept field trial, the current study examined the effect of expanding the temporal window of reinforcement, using remotely delivered EFT, on decreasing real-world alcohol consumption among individuals with alcohol use disorder (AUD). Fifty-two individuals (9 females) aged 18-65 years who met the DSM-5 criteria for moderate or severe AUD and aimed to drink in moderation or abstain from drinking completed the study and were included in analysis. EFT significantly (p = .031) reduced alcohol consumption (mean change of consumption pre-post intervention = -2.18 drinks/day) compared to control episodic recent thinking (ERT; mean change of -0.52 drinks/day). Changes in discounting rates pre-post intervention significantly predicted changes in alcohol consumption (coef. = .424, 95% CI [.043-.813], p = .030) even after controlling for age, gender, race, income, education, marital status, and family history of addiction. Overall satisfaction across groups was rated as 3.92 on a 1 to 5-point scale, suggesting that the current remote approach is feasible and acceptable. The current findings were congruent with the theory, Reinforcer Pathology, that EFT expands the temporal window and decreases alcohol consumption, and the remote approach was considered feasible and acceptable. We believe the present study contributes new knowledge with tangible benefits for scientifically understanding and better defining novel interventions that may be clinically deployed to improve treatment outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Quantitative susceptibility mapping of basal ganglia iron is associated with cognitive and motor functions that distinguish spinocerebellar ataxia type 6 and type 3.

Background: In spinocerebellar ataxia type 3 (SCA3), volume loss has been reported in the basal ganglia, an iron-rich brain region, but iron content has not been examined. Recent studies have reported that patients with SCA6 have markedly decreased iron content in the cerebellar dentate, coupled with severe volume loss. Changing brain iron levels can disrupt cognitive and motor functions, yet this has not been examined in the SCAs, a disease in which iron-rich regions are affected. Methods: In the present study, we used quantitative susceptibility mapping (QSM) to measure tissue magnetic susceptibility (indicating iron concentration), structural volume, and normalized susceptibility mass (indicating iron content) in the cerebellar dentate and basal ganglia in people with SCA3 (n = 10) and SCA6 (n = 6) and healthy controls (n = 9). Data were acquired using a 7T Philips MRI scanner. Supplemental measures assessed motor, cognitive, and mood domains. Results: Putamen volume was lower in both SCA groups relative to controls, replicating prior findings. Dentate susceptibility mass and volume in SCA6 was lower than in SCA3 or controls, also replicating prior findings. The novel finding was that higher basal ganglia susceptibility mass in SCA6 correlated with lower cognitive performance and greater motor impairment, an association that was not observed in SCA3. Cerebellar dentate susceptibility mass, however, had the opposite relationship with cognition and motor function in SCA6, suggesting that, as dentate iron is depleted, it relocated to the basal ganglia, which contributed to cognitive and motor decline. By contrast, basal ganglia volume loss, rather than iron content, appeared to drive changes in motor function in SCA3. Conclusion: The associations of higher basal ganglia iron with lower motor and cognitive function in SCA6 but not in SCA3 suggest the potential for using brain iron deposition profiles beyond the cerebellar dentate to assess disease states within the cerebellar ataxias. Moreover, the role of the basal ganglia deserves greater attention as a contributor to pathologic and phenotypic changes associated with SCA.

Effects of White-Matter Tract Length in Sport-Related Concussion: A Tractography Study from the NCAA-DoD CARE Consortium.

Sport-related concussion (SRC) is an important public health issue. White-matter alterations after SRC are widely studied by neuroimaging approaches, such as diffusion magnetic resonance imaging (MRI). Although the exact anatomical location of the alterations may differ, significant white-matter alterations are commonly observed in long fiber tracts, but are never proven. In the present study, we performed streamline tractography to characterize the association between tract length and white-matter microstructural alterations after SRC. Sixty-eight collegiate athletes diagnosed with acute concussion (24-48 h post-injury) and 64 matched contact-sport controls were included in this study. The athletes underwent diffusion tensor imaging (DTI) in 3.0 T MRI scanners across three study sites. DTI metrics were used for tract-based spatial statistics to map white-matter regions-of-interest (ROIs) with significant group differences. Whole-brain white-mater streamline tractography was performed to extract "affected" white-matter streamlines (i.e., streamlines passing through the identified ROIs). In the concussed athletes, streamline counts and DTI metrics of the affected white-matter fiber tracts were summarized and compared with unaffected white-matter tracts across tract length in the same participant. The affected white-matter tracts had a high streamline count at length of 80-100 mm and high length-adjusted affected ratio for streamline length longer than 80 mm. DTI mean diffusivity was higher in the affected streamlines longer than 100 mm with significant associations with the Brief Symptom Inventory score. Our findings suggest that long fibers in the brains of collegiate athletes are more vulnerable to acute SRC with higher mean diffusivity and a higher affected ratio compared with the whole distribution.

Decoding fMRI brain states in real-time.

This article reviews a technological advance that originates from two areas of ongoing neuroimaging innovation-(1) the use of multivariate supervised learning to decode brain states and (2) real-time functional magnetic resonance imaging (rtfMRI). The approach uses multivariate methods to train a model capable of decoding a subject's brain state from fMRI images. The decoded brain states can be used as a control signal for a brain computer interface (BCI) or to provide neurofeedback to the subject. The ability to adapt the stimulus during the fMRI experiment adds a new level of flexibility for task paradigms and has potential applications in a number of areas, including performance enhancement, rehabilitation, and therapy. Multivariate approaches to real-time fMRI are complementary to region-of-interest (ROI)-based methods and provide a principled method for dealing with distributed patterns of brain responses. Specifically, a multivariate approach is advantageous when network activity is expected, when mental strategies could vary from individual to individual, or when one or a few ROIs are not unequivocally the most appropriate for the investigation. Beyond highlighting important developments in rtfMRI and supervised learning, the article discusses important practical issues, including implementation considerations, existing resources, and future challenges and opportunities. Some possible future directions are described, calling for advances arising from increased experimental flexibility, improvements in predictive modeling, better comparisons across rtfMRI and other BCI implementations, and further investigation of the types of feedback and degree to which interface modulation is obtainable for various tasks.