Sounds provide important information about the spatial environment, including the location of approaching objects. Attention to sounds can be directed through automatic or more controlled processes, which have been well studied in the visual modality. However, little is known about the neural underpinnings of attentional control mechanisms for auditory signals. We studied healthy adults who underwent event-related FMRI while performing a task that manipulated automatic and more controlled auditory orienting by varying the probability that cues correctly predicted target location. Specifically, we examined the effects of uninformative (50% validity ratio) and informative (75% validity ratio) auditory cues on reaction time (RT) and neuronal functioning. The stimulus-onset asynchrony (SOA) between the cue and the target was either 100 or 800 ms. At the 100 ms SOA, RT was faster for valid than invalid trials for both cue types, and frontoparietal activation was greater for invalid than valid trials. At the 800 ms SOA, RT and functional activation depended on whether cues were informative or uninformative, and whether cues correctly or incorrectly predicted the target location. Contrary to our prediction, activation in a frontoparietal network was greater for uninformative than informative cues across several different comparisons and at both SOAs. This finding contrasts with similar research of visual orienting, and suggests that the auditory modality may be more biased toward automatic shifts of attention following uninformative cues.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.