Test‐retest reliability of amygdala response to emotional faces

In the current study, we evaluated the test‐retest reliability of amygdala response using an emotional face‐matching task that has been widely used to examine pathophysiology and treatment mechanisms in psychiatric populations. Activation within the fusiform face area ( FFA ) was also examined. Twenty‐seven healthy volunteers completed a variation of the face‐matching paradigm developed by Hariri et al. (2000) at two time points approximately 90 days apart. Estimates of test‐retest reliability of amygdala response to fearful faces were moderate, whereas angry and happy faces showed poor reliability. Test‐retest reliability of the FFA was moderate to strong, regardless of facial affect. Collectively, these findings indicate that the reliability of the BOLD MR signal in the amygdala varies substantially by facial affect. Efforts to improve measurement precision, enlarge sample sizes, or increase the number of assessment occasions seem warranted.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100342/1/psyp12129.pd

Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain

General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA.We investigated prediction of general cognitive ability (GCA) based on fMRI task activation patterns with 15 task contrasts in the Human Connectome Project dataset. The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in ten10‐fold cross‐validation analysis. Additionally, we found that task contrasts that produce greater fronto‐parietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in GCA prediction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/2/hbm25007.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/1/hbm25007_am.pd

Altered Activation Of The Rostral Anterior Cingulate Cortex In The Context Of Emotional Face Distractors In Children And Adolescents With Anxiety Disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109274/1/da22289.pd

Automated brain masking of fetal functional MRI with open data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing

Childhood poverty is associated with altered hippocampal function and visuospatial memory in adulthood

Childhood poverty is a risk factor for poorer cognitive performance during childhood and adulthood. While evidence linking childhood poverty and memory deficits in adulthood has been accumulating, underlying neural mechanisms are unknown. To investigate neurobiological links between childhood poverty and adult memory performance, we used functional magnetic resonance imaging (fMRI) during a visuospatial memory task in healthy young adults with varying income levels during childhood. Participants were assessed at age 9 and followed through young adulthood to assess income and related factors. During adulthood, participants completed a visuospatial memory task while undergoing MRI scanning. Patterns of neural activation, as well as memory recognition for items, were assessed to examine links between brain function and memory performance as it relates to childhood income. Our findings revealed associations between item recognition, childhood income level, and hippocampal activation. Specifically, the association between hippocampal activation and recognition accuracy varied as a function of childhood poverty, with positive associations at higher income levels, and negative associations at lower income levels. These prospective findings confirm previous retrospective results detailing deleterious effects of childhood poverty on adult memory performance. In addition, for the first time, we identify novel neurophysiological correlates of these deficits localized to hippocampus activation

Dissecting structural connectivity of the left and right inferior frontal cortex in children who stutter

Inferior frontal cortex pars opercularis (IFCop) features a distinct cerebral dominance and vast functional heterogeneity. Left and right IFCop are implicated in developmental stuttering. Weak left IFCop connections and divergent connectivity of hyperactive right IFCop regions have been related to impeded speech. Here, we reanalyzed diffusion magnetic resonance imaging data from 83 children (41 stuttering). We generated connection probability maps of functionally segregated area 44 parcels and calculated hemisphere-wise analyses of variance. Children who stutter showed reduced connectivity of executive, rostral-motor, and caudal-motor corticostriatal projections from the left IFCop. We discuss this finding in the context of tracing studies from the macaque area 44, which leads to the need to reconsider current models of speech motor control. Unlike the left, the right IFCop revealed increased connectivity of the inferior posterior ventral parcel and decreased connectivity of the posterior dorsal parcel with the anterior insula, particularly in stuttering boys. This divergent connectivity pattern in young children adds to the debate on potential core deficits in stuttering and challenges the theory that right hemisphere differences might exclusively indicate compensatory changes that evolve from lifelong exposure. Instead, early right prefrontal connectivity differences may reflect additional brain signatures of aberrant cognition-emotion-action influencing speech motor control