Biology subjects

Peters, S.

Publications and source records attributed to Peters, S..

5 recordsLinked to original sources

bioRxiv2018

Developmental maturation of the precuneus as a functional core of the default-mode network

Efforts to map the functional architecture of the developing human brain have shown that connectivity between and within functional neural networks changes from childhood to adulthood. While prior work has established that the adult precuneus distinctively modifies its connectivity during task versus rest states (Utevsky, Smith, & Huettel, 2014), it remains unknown how these connectivity patterns emerge over development. Here, we use functional magnetic resonance imaging (fMRI) data collected at two longitudinal timepoints from over 250 participants between the ages of 8 and 26 engaging in two cognitive tasks and a resting-state scan. By applying independent component analysis (ICA) to both task and rest data, we identified three canonical networks of interest - the rest-based default mode network (DMN) and the task-based left and right frontoparietal networks (LFPN, RFPN) - which we explored for developmental changes using dual-regression analyses. We found systematic state-dependent functional connectivity in the precuneus, such that engaging in a task (compared to rest) resulted in greater precuneus-LFPN and precuneus-RFPN connectivity, whereas being at rest (compared to task) resulted in greater precuneus-DMN connectivity. These cross-sectional results replicated across both tasks and at both developmental timepoints. Finally, we used longitudinal mixed models to show that the degree to which precuneus distinguishes between task and rest states increases with age, due to age-related increasing segregation between precuneus and LFPN at rest. Our results highlight the distinct role of the precuneus in tracking processing state, in a manner that is both present throughout and strengthened across development.

neuroscience↗

bioRxiv2018

Qoala-T: A supervised-learning tool for quality control of automatic segmented MRI data

Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans. Different supervised-learning models were then compared to predict manual quality ratings. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of a novel unseen dataset (N = 112; mean AUC = 0.95). These outcomes indicate that using Qoala-T in other datasets could greatly reduce the time needed for quality control. More importantly, this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies.

neuroscience↗

bioRxiv2017

Longitudinal development of hippocampal subregions from childhood to adulthood

Detailed descriptions of the development of the hippocampus promise to shed light on the neural foundation of development of memory and other cognitive functions, as well as the emergence of major mental disorders. Hippocampus is a heterogeneous structure with a well characterized internal complexity, but development of its distinct subregions in humans has remained poorly described. We analyzed magnetic resonance imaging (MRI) data from a large longitudinal sample (270 participants, 678 scans) using an automated segmentation tool and mixed models to delineate the development of hippocampal subregion volumes from childhood to adulthood. We also examined sex differences in subregion volumes and their development, and associations between hippocampal subregions and general cognitive ability. Nonlinear developmental trajectories with early volume increases were observed for subiculum, cornu ammonis (CA) 1, molecular layer (ML) and fimbria. In contrast, parasubiculum, presubiculum, CA2/3, CA4 and the granule cell layer of the dentate gyrus (GC-DG) showed linear volume decreases. No sex differences were found in hippocampal subregion development. Finally, general cognitive ability was positively associated with CA2/3 and CA4 volumes, as well as with ML development. In conclusion, hippocampal subregions appear to develop in diversified ways across adolescence, and specific subregions may link to general cognitive level.\n\nHighlightsO_LIHippocampal subregions develop in differential ways from childhood to adulthood\nC_LIO_LISubiculum, CA1, ML and fimbria showed nonlinear trajectories with initial increases\nC_LIO_LIParasubiculum, presubiculum, CA2/3, CA4 and GC-DG showed linear volume decreases\nC_LIO_LIThere were no sex differences in hippocampal subregion development\nC_LIO_LIGeneral cognitive ability associated with CA2/3 and CA4 volumes and ML development\nC_LI

neuroscience↗

bioRxiv2017

RANKL Signaling Sustains Primary Tumor Growth In Genetically Engineered Mouse Models Of Lung Adenocarcinoma

HypothesisNon-small cell lung cancer (NSCLC) is the leading cause of cancer mortality. Recent retrospective clinical analyses suggest that blocking the receptor activator of NF-{kappa}B (RANK) signaling pathway inhibits the growth of NSCLC and might represent a new treatment strategy.\n\nMethodsRANK and RANKL expression in human lung adenocarcinoma was interrogated from publicly available gene expression datasets. Several genetically engineered mouse models were used to evaluate treatment efficacy of RANK-Fc to block RANKL, with primary tumor growth measured longitudinally using micro-computed tomography. A combination of RANKL blockade with cisplatin was tested to mirror an ongoing clinical trial.\n\nResultsIn human lung adenocarcinoma datasets, RANKL expression was associated with decreased survival and KRAS mutation, with the highest levels in tumors with co-occurring KRAS and LKB1 mutations. In KrasLSL-G12D/WT, KrasLSL-G12D/WT; Lkb1Flox/Flox and KrasLSL-G12D/WT; p53Flox/Flox mouse models of lung adenocarcinoma, we monitored an impaired progression of tumors upon RANKL blockade. Despite elevated expression of RANKL and RANK in immune cells, treatment response was not associated with major changes in the tumor immune microenvironment. Combined RANK-Fc with cisplatin revealed increased efficacy compared to single agents.\n\nConclusionsRANKL blocking agents impair the growth of primary lung tumors in several mouse models of lung adenocarcinoma, and suggest that patients with KRAS mutant lung tumors will benefit from such treatments.

cancer biology↗

bioRxiv2016

A Canonical Neural Mechanism for Behavioral Variability

The ability to generate variable movements is essential for learning and adjusting complex behaviors. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioral variability is unclear. Here we combine modeling, electrophysiological and behavioral studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviors. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analyzing behavioral data, we find remarkable similarities in the babbling statistics of 5-6 month-old human infants and juveniles from three songbird species, and show that our model naturally accounts for these universal statistics.

neuroscience↗