Grape split imaging to examine physical changes before and after splitting using diffusion magnetic resonance

Berry split is a condition in which the grape epidermis splits which often occurs during periods of high rainfall and is a significant cause of grape crop loss. In damp conditions there is increased uptake of water via osmosis and decreased water loss from transpiration. The pre-dawn turgor pressure of table grape cultivars lies in the vicinity of 5-38 kPa but prior to berry split can be as high as 1.5-3.7 MPa.
In order to examine and characterise the immediate effect of fruit split on grape, National Wine & Grape Industry and Western Sydney University node of National Imaging Facility have been collaborating on an ongoing project, which investigates the physical changes within the grape berry both before and after splitting using diffusion magnetic resonance imaging. Thirty-six table grapes of the Thompson Seedless variety were involved in the study and assigned to three groups: a control group (12 berries), a group in which they were wrapped in damp tissue (12 berries), and a total immersion group (12 berries). Five axial images (including diffusion tensor images) spaced evenly apart along the length of each berry were acquired simultaneously every hour to create a time-course study of each grape.
For each grape that split within the MRI during the study it was observed that there was an immediate change in the diffusion coefficient in the region of the wound. This region increased in volume over the course of the subsequent scans and correlated with regions of non-vital cells (as determined by fluorescence microscopy). It was determined from the study that grape berries left exposed to standing water after splitting exhibit greater cell death within the vicinity of the split. Therefore, the surface of split berries should be kept dry if possible to reduce further damage.



Nanoscale Organisation and Dynamics Group, University of Western Sydney
School of Medicine, University of Western Sydney
National Wine & Grape Industry Centre
NSW Department of Primary Industries


Dean, R.J., Bobek, G., Stait-Gardner, T., Clarke, S.J., Rogiers, S.Y. and Price, W.S. (2015), Time-course study of grape berry split using diffusion magnetic resonance imaging. Aust. J. Grape Wine R. doi: 10.1111/ajgw.12184
Dean R.J., Stait-Gardner, T., Clarke, S.J., Rogiers, S.Y., Bobek, G. and Price, W.S. (2014) Use of diffusion magnetic resonance imaging to correlate the developmental changes in grape berry tissue structure with water diffusion patterns. Plant methods 10(1):35


Abnormal brain areas common to the focal epilepsies

A group of scientists at The Florey Institute may soon be able to diagnose a common form of epilepsy after a simple 10-­minute brain scan. The result? Patients will commence immediate treatment and minimize the risk of further damage caused by seizures.

New research published in Brain Connectivity shows that people with focal epilepsy seemingly share characteristic brain network connectivity in three precise regions of the brain, even though the seizure site is in heterogeneous brain regions.

People with focal epilepsy, including all the patients in this new study, have slower psychomotor reflexes, and neuropsychological symptoms such as depression and working memory deficiencies. About one per cent of the population will develop epilepsy at some point in their lifetimes, with childhood and old age being more vulnerable periods. Over half of all diagnosed epilepsies are focal in nature, arising in specific brain regions.

Mangor Pedersen, together with a team led by Professor Graeme Jackson at the Florey node of National Imaging Facility (NIF), used functional Magnetic Resonance Imaging (fMRI) scans obtained on a 3T Siemens Trio located at the Melbourne Brain Centre, Austin Hospital campus. The analysis techniques used in this study may be used to target MR biomarker data allowing patients to be classified as having focal epilepsy, versus other types of epilepsy. The team scanned brains of 14 people with focal epilepsy, and compared them to 14 age-­and sex-matched people without the disease. The group then used two connectivity measures -­ a local network between one voxel and the 27 surrounding voxels (about one cubic centimeter of brain), and a more distributed network from each single voxel connected to all the other voxels in the brain – to show abnormal connectivity in three brain regions of people with focal epilepsy.

The three common brain areas in people with focal epilepsy were both shallow & deep brain regions in the temporal lobe (just in front of and above the ear) and the prefrontal cortex (at the front of the head in between the eyes). What amazed the researchers was that passing the connectivity results through a multivariate pattern analysis (a “machine learning algorithm”) differentiated healthy people from those with focal epilepsy with almost 90 per cent accuracy.

Mangor said of the work, “Focal epilepsy is a disease where seizures originate from different areas of the brain. In this study we tested whether patients had any brain markers in common. We used network connectivity and pattern analysis to classify brain patterns at a single subject level. We hope that this work is a preliminary step towards using network analysis from functional imaging and pattern analysis to detect focal epilepsy biomarkers.”

The work is the culmination of three years’ hard work as part of Mangor’s Ph.D research. However, he is not content to leave it there, saying “we now need to shore up these findings firstly by scanning more patients.” Other future experiments are to scan people with co-morbities, like depression, but not focal epilepsy. This will help assess the reliability of the clinical fMRI classification.



The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia.
The University of Melbourne, Florey Department of Neuroscience and Mental Health, Melbourne, VIC, Australia.
Department of Neurology, Austin Health, Melbourne, VIC, Australia


Pedersen, Mangor, et al. “Abnormal brain areas common to the focal epilepsies: Multivariate pattern analysis of fMRI.” Brain connectivity (2015).

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