Biology subjects

Griffiths, K. R.

Publications and source records attributed to Griffiths, K. R..

3 recordsLinked to original sources

bioRxiv2023

Standardisation of cell-free DNA measurements: An International Study on Comparability of Low Concentration DNA Measurements using cancer variants

For the impact of genomic testing from liquid biopsies to be maximized, mechanisms to ensure reproducible and comparable test performance will be required. This can be established and maintained through reference measurement procedures and materials with property values that are internationally comparable through traceability to a common standard. To achieve this objective, an interlaboratory study was organised to explore digital PCR (dPCR) for standardisation of cell-free DNA (cfDNA) quantification. Blinded samples of wild-type/variant mixtures of two DNA sequences (BRAF p.V600E single nucleotide variant or EGFR exon 19 deletion) were provided to 12 laboratories. Laboratories independently designed and applied dPCR assays to determine absolute and relative quantities, with no guidance provided to harmonise the approach. The mean and coefficient of variation (CV) of copy number concentrations for variant sequences were 18 copies/L (CV 7.2%) (BRAF variant sample) and 9 copies/L (CV 25%) (EGFR variant sample) while the mean variant allele frequencies (vAF) were 8.0% (CV 5.3%) and 0.080% (CV 29%) respectively. This study demonstrated that dPCR was capable of exceptional technical accuracy for variant copy number concentration and vAF, even when different assays and platforms were used. This implies that dPCR offers a unique analytical methodology that can be deployed globally in supporting comparability for cfDNA testing based on the existing framework of the International System of units of measurement.

genetics↗

bioRxiv2020

No support for white matter microstructural properties and connectivity differences in the combined and inattentive adhd presentations.

Evidence from functional neuroimaging studies support neural differences between the Attention Deficit Hyperactivity Disorder (ADHD) presentation types. It remains unclear if these neural deficits also manifest at the structural level. We have previously shown that the ADHD combined, and ADHD inattentive types demonstrate differences in graph properties of structural covariance suggesting an underlying difference in neuroanatomical organization. The goal of this study was to examine and validate white matter brain organization between the two subtypes using both scalar and connectivity measures of brain white matter. We used both tract-based spatial statistical (TBSS) and tractography analyses with network-based Statistics (NBS) and graph-theoretical analyses in a cohort of 35 ADHD participants (aged 8-17 years) defined using DSM-IV criteria as combined (ADHD-C) type (n=19) or as predominantly inattentive (ADHD-I) type (n=16), and 28 matched neurotypical controls. We performed TBSS analyses on scalar measures of fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity to assess differences in WM between ADHD types and controls. NBS and graph theoretical analysis of whole brain inter-regional tractography examined connectomic differences and brain network organization, respectively. None of the scalar measures significantly differed between ADHD types or relative to controls. Similarly, there were no tractography connectivity differences between the two subtypes and relative to controls using NBS. Global and regional graph measures were also similar between the groups. A single significant finding was observed for nodal degree between the ADHD-C and controls, in the right insula (corrected p=.029). Our result of no white matter differences between the subtypes is consistent with most previous findings. These findings together might suggest that the white matter structural architecture is largely similar between the DSM-based ADHD presentations.

neuroscience↗

bioRxiv2017

The Algorithmic Neuroanatomy Of Action-Outcome Learning

Although it is well known that animals can encode the consequences of their actions and can use this information to control action selection and evaluation, it is not known what learning rules control action-outcome (AO) learning. Here we trained participants to encode specific AO associations whilst undergoing functional imaging (fMRI) and used computational modelling to evaluate competing models. This analysis revealed that a Kalman filter, which learned the unique causal effect of each action, best characterized AO learning and found the medial prefrontal cortex differentiated the unique effect of actions from background effects. We subsequently extended these findings to show that mPFC participated in a circuit with parietal cortex and caudate nucleus to segregate distinct contributions to AO learning. The results extend our understanding of goal-directed learning and demonstrate that sensitivity to the causal relationship between actions and outcomes guides goal-directed learning rather than contiguous state-action relations.

neuroscience↗