Dr Marnie Shaw

Research Fellow

Marnie Shaw is a Research Leader in the Battery Storage and Grid Integration Program at the Australian National University. She is also the convenor of the Energy Efficiency research cluster at the ANU’s Energy Change Institute. 

Dr Shaw's current research interests lie in applying data analytics and machine learning to a range of data-rich problems, including the integration of renewable energy into the electricity grid. At the ANU, she has been looking at how community energy models (e.g. shared battery systems) can support increasing amounts of renewable energy in the grid, reduce energy costs for consumers, and address important issues around energy equity.

Research interests

energy change, energy storage

brain imaging (fMRI, MEG, Resting State Networks)

data analytics, multivariate statistics, machine learning

Groups

  • Owens-Walton, C, Jakabek, D, Power, B et al. 2021, 'Structural and functional neuroimaging changes associated with cognitive impairment and dementia in Parkinson's disease', Psychiatry Research: Neuroimaging, vol. 312, pp. 1-12.
  • Fraser, M, Walsh, E, Shaw, M et al. 2020, 'Longitudinal trajectories of hippocampal volume in middle to older age community dwelling individuals', Neurobiology of Aging, vol. 97, pp. 97-105.
  • Espinoza Oyarce, D, Shaw, M, Alateeq, K ..., Cherbuin, N. 2020, 'Volumetric brain differences in clinical depression in association with anxiety: a systematic review with meta-analysis', Journal of Psychiatry and Neuroscience, vol. 45, no. 6, pp. 406-429.
  • Northey, J, Rattray, B, Pumpa , K ..., Cherbuin, N. 2020, 'Objectively measured physical activity is associated with dorsolateral prefrontal cortex volume in older adults', Neuroimage, vol. 221, no. November 2020, pp. 1-8.
  • Tabatabaei Jafari, H, Shaw, M, Walsh, E et al. 2019, 'Regional brain atrophy predicts time to conversion to Alzheimer's disease, dependent on baseline volume', Neurobiology of Aging, vol. 83, pp. 86-94.
  • Owens-Walton, C, Jakabek, D, Power, B et al. 2019, 'Increased functional connectivity of thalamic subdivisions in patients with Parkinson's disease', PLOS ONE (Public Library of Science), vol. 14, no. 9.
  • Walsh, E, Shaw, M, Sachdev, P et al 2019, 'The impact of type 2 diabetes and body mass index on cerebral structure is modulated by brain reserve', European Journal of Neurology, vol. 26, no. 1, pp. 121-127.
  • Tabatabaei Jafari, H, Shaw, M, Walsh, E et al. 2019, 'Cognitive/Functional Measures Predict Alzheimer's Disease, Dependent on Hippocampal Volume', Journals of Gerontology Series B: Psychological Sciences and Social Sciences, vol. Online, pp. 1-10pp.
  • Walsh, E, Shaw, M, Espinoza Oyarce, D et al. 2019, 'Assumption-Free Assessment of Corpus Callosum Shape: Benchmarking and Application', Concepts in Magnetic Resonance Part A, pp. 1-10.
  • Shaw, M, Sachdev, P, Abhayaratna, W et al 2018, 'Body mass index is associated with cortical thinning with different patterns in mid- and late-life', International Journal of Obesity, vol. 42, pp. 455-461.
  • Chopra, S, Shaw, M, Shaw, T et al 2018, 'More highly myelinated white matter tracts are associated with faster processing speed in healthy adults', Neuroimage, vol. 171, pp. 332-340pp.
  • Zhang, T, Shaw, M, Walsh, E et al 2018, 'Higher fasting plasma glucose is associated with smaller striatal volume and poorer fine motor skills in a longitudinal cohort', Psychiatry Research: Neuroimaging, vol. 278, pp. 1-6pp.
  • Tabatabaei Jafari, H, Walsh, E, Shaw, M et al. 2018, 'A simple and clinically relevant combination of neuroimaging and functional indexes for the identification of those at highest risk of Alzheimer's disease', Neurobiology of Aging, vol. 69, pp. 102-110.
  • Fraser, M, Shaw, M, Anstey, K et al 2018, 'Longitudinal Assessment of Hippocampal Atrophy in Midlife and Early Old Age: Contrasting Manual Tracing and Semi-automated Segmentation (FreeSurfer)', Brain Topography: Journal of Functional Neurophysiology, vol. 31, pp. 949-962pp.
  • Jin, K, Zhang, T, Shaw, M et al 2018, 'Relationship Between Sulcal Characteristics and Brain Aging', Frontiers in Aging Neuroscience, vol. 10, no. 339, pp. 1-9.
  • Menikdiwela, M, Nguyen, C, Li, H & Shaw, M 2018, 'CNN-based small object detection and visualization with feature activation mapping', 2017 International Conference on Image and Vision Computing New Zealand, IVCNZ 2017, IEEE, United States, pp. 1-5.
  • Cherbuin, N, Shaw, M, Walsh, E et al 2017, 'Validated Alzheimer's Disease Risk Index (ANU-ADRI) is associated with smaller volumes in the default mode network in the early 60s', Brain Imaging and Behavior, vol. 13, no. 1, pp. 65-74.
  • Tabatabaei Jafari, H, Walsh, E, Shaw, M et al. 2017, 'The cerebellum shrinks faster than normal ageing in Alzheimer's disease but not in mild cognitive impairment', Human Brain Mapping, vol. 38, no. 6, pp. 3141-3150.
  • Shaw, M, Abhayaratna, W, Anstey, K et al 2017, 'Increasing Body Mass Index at Midlife is Associated with Increased Cortical Thinning in Alzheimer's Disease-Vulnerable Regions', Journal of Alzheimer's Disease, vol. 59, no. 1, pp. 113-120.
  • Walsh, E, Shaw, M, Sachdev, P et al 2017, 'Brain atrophy in ageing: Estimating effects of blood glucose levels vs. other type 2 diabetes effects', Diabetes and Metabolism, vol. -, no. -, pp. 1-4pp.
  • Lueders, E, Kurth, F, Das, D et al. 2016, 'Associations between corpus callosum size and ADHD symptoms in older adults: The PATH through life study', Psychiatry Research: Neuroimaging, vol. 256, pp. 8-14pp.
  • Zhang, T, Shaw, M, Humphries, J et al 2016, 'Higher fasting plasma glucose is associated with striatal and hippocampal shape differences: The 2sweet project', BMJ Open Diabetes Research & Care, vol. 4, no. 1, pp. 1-8pp.
  • Shaw, M, Abhayaratna, W, Sachdev, P et al 2016, 'Cortical Thinning at Midlife: The PATH Through Life Study', Brain Topography: Journal of Functional Neurophysiology, vol. 29, no. 6, pp. 875-884pp.
  • Shaw, M, Sachdev, P, Anstey, K et al 2016, 'Age-related cortical thinning in cognitively healthy individuals in their 60s: The PATH Through Life study', Neurobiology of Aging, vol. 39, pp. 202-209.
  • Fraser, M, Shaw, M & Cherbuin, N 2015, 'A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing', Neuroimage, vol. 112, pp. 364-374.
  • Tabatabaei Jafari, H, Cherbuin, N & Shaw, M 2015, 'CEREBRAL ATROPHY IN MILD COGNITIVE IMPAIRMENT A SYSTEMATIC REVIEW WITH META ANALYSIS', Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 1, no. 4, pp. 487-504.
  • Shaw, M, Hämäläinen, M & Gutschalk, A 2013, 'How anatomical asymmetry of human auditory cortex can lead to a rightward bias in auditory evoked fields', Neuroimage, vol. 74, pp. 22-29.
  • Bluhm, R, Clark, C, McFarlane, A et al 2011, 'Default Network Connectivity During a Working Memory Task', Human Brain Mapping, vol. 32, no. 7, pp. 1029-1035.
  • Daniels, J, McFarlane, A, Bluhm, R et al. 2010, 'Switching between executive and default mode networks in posttraumatic stress disorder: Alterations in functional connectivity', Journal of Psychiatry and Neuroscience, vol. 35, no. 4, pp. 258-266.