Garrett, Liam Rodney;
Niccoli, Teresa;
(2022)
Frontotemporal Dementia and Glucose Metabolism.
Frontiers in Neuroscience
, 04
, Article 837269. 10.3389/fnins.2022.812222.
Preview |
PDF
Pellicano_Evaluating the Impact of Voice Activity Detection on Speech Emotion Recognition for Autistic Children_VoR.pdf - Published Version Download (897kB) | Preview |
Abstract
Frontotemporal dementia (FTD), hallmarked by antero-temporal degeneration in the human brain, is the second most common early onset dementia. FTD is a diverse disease with three main clinical presentations, four different identified proteinopathies and many disease-associated genes. The exact pathophysiology of FTD remains to be elucidated. One common characteristic all forms of FTD share is the dysregulation of glucose metabolism in patients’ brains. The brain consumes around 20% of the body’s energy supply and predominantly utilizes glucose as a fuel. Glucose metabolism dysregulation could therefore be extremely detrimental for neuronal health. Research into the association between glucose metabolism and dementias has recently gained interest in Alzheimer’s disease. FTD also presents with glucose metabolism dysregulation, however, this remains largely an unexplored area. A better understanding of the link between FTD and glucose metabolism may yield further insight into FTD pathophysiology and aid the development of novel therapeutics. Here we review our current understanding of FTD and glucose metabolism in the brain and discuss the evidence of impaired glucose metabolism in FTD. Lastly, we review research potentially suggesting a causal relationship between FTD proteinopathies and impaired glucose metabolism in FTD.
| Type: | Article |
|---|---|
| Title: | Frontotemporal Dementia and Glucose Metabolism |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fnins.2022.812222 |
| Publisher version: | https://doi.org/10.3389/fnins.2022.812222 |
| Language: | English |
| Additional information: | © 2022 Milling, Baird, Bartl-Pokorny, Liu, Alcorn, Shen, Tavassoli, Ainger, Pellicano, Pantic, Cummins and Schuller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | affective computing, voice activity detection, deep learning, speech emotion recognition, children with autism, robot human interaction |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146486 |
Archive Staff Only
 |
View Item |
