Biology subjects

Moran, C.

Publications and source records attributed to Moran, C..

3 recordsLinked to original sources

bioRxiv2021

Participant followup rate can bias structural imaging measures in longitudinal studies

Longitudinal MRI analysis is essential to accurately describe neuroanatomical changes over time. Loss of participants to followup (dropout) in longitudinal studies is inevitable and can lead to great difficulty in interpretation of statistical results if dropout is correlated with a study outcome or exposure. Beyond this, technical aspects of longitudinal MRI analysis require specialised processing pipelines to improve reliability while avoiding bias towards individual timepoints. In this article we test whether there is an additional problem that must be considered in longitudinal imaging studies, namely whether dropout has an impact on the function of FreeSurfer, a popular software pipeline used to estimate important structural brain metrics. We find that the number of acquisitions available per individual can impact the estimation of cortical thickness and brain volume using the FreeSurfer longitudinal pipeline, and can induce group differences in brain metrics. The effect on trajectories of brain metrics is smaller than the effect on brain metrics. HighlightsO_LILongitudinal MRI analysis is essential to accurately track neuroanatomical changes over time C_LIO_LILongitudinal MRI analysis requires specialised processing pipelines to reduce bias towards single timepoints C_LIO_LIParticipant drop out or loss can bias neuroanatomical measures derived from longitudinal pipelines C_LIO_LIWe find that group differences in the number of acquisitions available to analyse can cause group differences in estimated cortical thickness and brain volume C_LIO_LIThis bias appears to be due to the number of scans used to create individualised templates in the Freesurfer longitudinal pipeline C_LIO_LIThe effect on estimates of brain metric trajectories appears smaller than the effect on the estimates of brain metrics C_LI

neuroscience↗

bioRxiv2021

The histologic phenotype of lung cancers may be driven by transcriptomic features rather than genomic characteristics

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing(WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data suggest that histology of lung cancers may be determined at the transcriptomic level rather than the genomic level.

genomics↗

bioRxiv2020

Cortical dynamics underlying social behavior in dominance hierarchy and spatial navigation

Rodents establish dominance hierarchy as a social ranking system in which one subject acts as dominant over all the other subordinate individuals. Dominance hierarchy regulates food access and mating opportunities, but little is known of its significance in collective behavior, for instance during navigation for foraging or migration. Here, we implemented a simplified goal-directed spatial navigation task in mice and found that the social context exerts significant influence on individual decision-making, even when efficient navigation rules leading to reward had been previously learned. Thus, decision-making and consequent task performance were strongly dependent on contingent social interactions arising during collective navigation, yet their influence on individual behavior was outlined by dominance hierarchy. Dominant animals did not behave as leaders during navigation; conversely, they were most sensitive to social context. Social ranking in turn was reflected in the neural activity and connectivity patterns of the prefrontal cortex and hippocampus, both in anesthetized and behaving mice. These results suggest that the interplay between contingent social interactions and dominance hierarchy can regulate behavioral performance, supported by the intrinsic matrix of coordinated activity in the hippocampal-prefrontal circuit. Significance StatementDecision-making is shaped by intrinsic features, such as memory-stored information, and external influences, such as social interactions, yet their interplay is not well understood. We studied decision-making during collective behavior and found that instead of prioritizing memory-based pertinent information, mice shifted their individual decisions according to contingent social interactions arising in the social context. Conversely, constitutive social interactions, such as dominance hierarchy, were relevant to outline the effect of the social environment on individual behavior. Our results suggest that intrinsic hippocampal-cortical activity and connectivity patterns define social interactions. Hence, intrinsic cortical dynamics underlie behavioral performance during social decision-making.

neuroscience↗