Profiling dementias using complex sound: from symptoms to brain networks.

Award Number
091673/Z/10/Z
Status / Stage
Completed
Dates
1 August 2010 -
31 December 2016
Duration (calculated)
06 years 04 months
Funder(s)
Wellcome Trust
Funding Amount
£1,207,695.00
Contracted Centre
University College London
Contracted Centre Webpage
Principal Investigator
Prof Jason Warren
PI Contact
jason.warren@ucl.ac.uk
PI ORCID
0000-0002-5405-0826
WHO Catergories
Development of clinical assessment of cognition and function
Methodologies and approaches for risk reduction research
Understanding Underlying Disease
Disease Type
Alzheimer's Disease (AD)
Frontotemporal Dementia (FTD)

CPEC Review Info
Reference ID319
ResearcherReside Team
Published12/06/2023

Data

Award Number091673/Z/10/Z
Status / StageCompleted
Start Date20100801
End Date20161231
Duration (calculated) 06 years 04 months
Contracted CentreUniversity College London
Contracted Centre Webpage
Funding Amount£1,207,695.00

Abstract

Detailed pathophysiological understanding of dementias is essential for development and evaluation of therapies. Here I will adopt a multi-disciplinary behavioural and neuroimaging approach that uses an important and general class of symptoms impaired complex sound processing to probe brain structure and function in two common dementias, Alzheimer s and frontotemporal lobar degeneration. Key goals are to characterise deficits of complex sound (environmental sounds, music, voices, speech); an d to relate behavioural signatures to structural and functional MRI changes, cross-sectionally and longitudinally, in patients compared with healthy older controls and individuals at-risk of familial dementias. The emerging paradigm of neurodegenerative network dysfunction will be explored. Subjects will have annual clinical and neuropsychological assessments, structural-volumetric and functional MRI paradigms designed to test specific hypotheses about brain bases for disordered complex sound pr ocessing in neurodegenerative dementias, motivated by previous normal and clinical work. MRI data will be analysed with unbiased techniques including morphometric and registration algorithms, statistical parametric mapping, cortical thickness and connectivity measures, and findings integrated with behavioural, genetic and pathological data using parametric and non-parametric statistics. Translational opportunities include improved clinical understanding of key dementia diseases, new diagnostic a nd progression biomarkers, and identification of pathophysiological mechanisms that could become therapeutic targets.