Person:

Wald, Lawrence

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.

How much of the structure of the human mind and brain is already specified at birth, and how much arises from experience? In this article, we consider the test case of extrastriate visual cortex, where a highly systematic functional organization is present in virtually every normal adult, including regions preferring behaviourally significant stimulus categories, such as faces, bodies, and scenes. Novel methods were developed to scan awake infants with fMRI, while they viewed multiple categories of visual stimuli. Here we report that the visual cortex of 4–6-month-old infants contains regions that respond preferentially to abstract categories (faces and scenes), with a spatial organization similar to adults. However, precise response profiles and patterns of activity across multiple visual categories differ between infants and adults. These results demonstrate that the large-scale organization of category preferences in visual cortex is adult-like within a few months after birth, but is subsequently refined through development.

Incentive delay tasks implicate the striatum and medial frontal cortex in reward processing. However, prior studies delivered more rewards than penalties, possibly leading to unwanted differences in signal-to-noise ratio. Also, whether particular brain regions are specifically involved in anticipation or consumption is unclear. We used a task featuring balanced incentive delivery and an analytic strategy designed to identify activity specific to anticipation or consumption. Reaction time data in two independent samples (n = 13 and n = 8) confirmed motivated responding. Functional magnetic resonance imaging revealed regions activated by anticipation (anterior cingulate) versus consumption (orbital and medial frontal cortex). Ventral striatum was active during reward anticipation but not significantly more so than during consumption. Although the study features several methodological improvements and helps clarify the neural basis of incentive processing, replications in larger samples are needed.

Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network.

Purpose Functional neuroimaging of small cortical patches such as columns is essential for testing computational models of vision, but imaging from cortical columns at conventional 3T fields is exceedingly difficult. By targeting the visual cortex exclusively, we tested whether combined optimization of shape, coil placement, and electronics would yield the necessary gains in signal‐to‐noise ratio (SNR) for submillimeter visual cortex functional MRI (fMRI). Method We optimized the shape of the housing to a population‐averaged atlas. The shape was comfortable without cushions and resulted in the maximally proximal placement of the coil elements. By using small wire loops with the least number of solder joints, we were able to maximize the Q factor of the individual elements. Finally, by planning the placement of the coils using the brain atlas, we were able to target the arrangement of the coil elements to the extent of the visual cortex. Results: The combined optimizations led to as much as two‐fold SNR gain compared with a whole‐head 32‐channel coil. This gain was reflected in temporal SNR as well and enabled fMRI mapping at 0.75 mm resolutions using a conventional GRAPPA‐accelerated gradient echo echo planar imaging. Conclusion: Integrated optimization of shape, electronics, and element placement can lead to large gains in SNR and empower submillimeter fMRI at 3T. Magn Reson Med 76:321–328, 2016. © 2015 Wiley Periodicals, Inc.

A large number of mathematical models have been proposed to describe the measured signal in diffusion‐weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three‐quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion‐based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non‐Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal‐predicting strategies, such as bootstrapping or cross‐validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.

We provide a comprehensive diffusion MRI dataset acquired with a novel biomimetic phantom mimicking human white matter. The fiber substrates in the diffusion phantom were constructed from hollow textile axons (“taxons”) with an inner diameter of 11.8±1.2 µm and outer diameter of 33.5±2.3 µm. Data were acquired on the 3 T CONNECTOM MRI scanner with multiple diffusion times and multiple q-values per diffusion time, which is a dedicated acquisition for validation of microstructural imaging methods, such as compartment size and volume fraction mapping. Minimal preprocessing was performed to correct for susceptibility and eddy current distortions. Data were deposited in the XNAT Central database (project ID: dMRI_Phant_MGH).

A man with 25 years of mild left neck, arm, and leg paresthesias had initial MRI in 1996 identifying a left C3-4 dorsal horn cavernous hemangioma. In 1997, hemorrhage (C3-7) and resection induced left arm > leg proprioceptive loss and clumsiness. Three months after surgical resection, left upper-body pain recurred; 2 years later, disabling colocalizing itch recurred.1 In 2012, ultra-high-resolution 7T MRI (figure) localized hemosiderin to specific dorsal horn laminae and detected rostral (C1-3) hypersignal invisible on conventional MRIs, most likely representing wallerian degeneration.2 These new imaging findings demonstrate the benefit of high-field spinal cord MRI and generate the hypothesis that his late-onset central itch might be related to delayed white matter degeneration.