Human voices play a fundamental role in social communication, and areas of the adult "social brain" show specialization for processing voices and their emotional content (superior temporal sulcus, inferior prefrontal cortex, premotor cortical regions, amygdala, and insula). However, it is unclear when this specialization develops. Functional magnetic resonance (fMRI) studies suggest that the infant temporal cortex does not differentiate speech from music or backward speech, but a prior study with functional near-infrared spectroscopy revealed preferential activation for human voices in 7-month-olds, in a more posterior location of the temporal cortex than in adults. However, the brain networks involved in processing nonspeech human vocalizations in early development are still unknown. To address this issue, in the present fMRI study, 3- to 7-month-olds were presented with adult nonspeech vocalizations (emotionally neutral, emotionally positive, and emotionally negative) and nonvocal environmental sounds. Infants displayed significant differential activation in the anterior portion of the temporal cortex, similarly to adults. Moreover, sad vocalizations modulated the activity of brain regions involved in processing affective stimuli such as the orbitofrontal cortex and insula. These results suggest remarkably early functional specialization for processing human voice and negative emotions.

Purpose To reduce the sensitivity of echo-planar imaging (EPI) Auto-Calibration Signal (ACS) data to patient respiration and motion in order to improve the image quality and temporal Signal-to-Noise Ratio (tSNR) of accelerated EPI time-series data. Methods ACS data for accelerated EPI are generally acquired using segmented, multi-shot EPI to distortion-match the ACS and time-series data. The ACS data are therefore typically collected over multiple TR periods, leading to increased vulnerability to motion and dynamic B0 changes. The Fast Low-angle Excitation Echo-planar Technique (FLEET) is adopted to reorder the ACS segments so that segments within any given slice are acquired consecutively in time, thereby acquiring ACS data for each slice as rapidly as possible. Results Subject breath-hold and motion phantom experiments demonstrate that artifacts in the ACS data reduce tSNR and produce tSNR discontinuities across slices in the accelerated EPI time-series data. Accelerated EPI data reconstructed using FLEET-ACS exhibit improved tSNR and increased tSNR continuity across slices. Additionally, image quality is improved dramatically when bulk motion occurs during the ACS acquisition. Conclusion FLEET-ACS provides reduced respiration and motion sensitivity in accelerated EPI, which yields higher tSNR and image quality. Benefits are demonstrated in both conventional-resolution 3T and high-resolution 7T EPI time-series data.

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 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 (ConnecomeDB). 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.