Poster No:
114
Submission Type:
Abstract Submission
Authors:
Angelo Bumanglag1, Kristina Horne1, Samuel Warren1, Rebekah Ahmed2, Haridra Somasundaram1, Olivier Piguet1, Muireann Irish1
Institutions:
1Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2Memory and Cognition Clinic, Department of Clinical Neurosciences, RPA Hospital, Sydney, NSW
First Author:
Co-Author(s):
Kristina Horne
Brain and Mind Centre, The University of Sydney
Sydney, NSW
Samuel Warren
Brain and Mind Centre, The University of Sydney
Sydney, NSW
Rebekah Ahmed
Memory and Cognition Clinic, Department of Clinical Neurosciences, RPA Hospital
Sydney, NSW
Olivier Piguet
Brain and Mind Centre, The University of Sydney
Sydney, NSW
Muireann Irish
Brain and Mind Centre, The University of Sydney
Sydney, NSW
Introduction:
Anhedonia and apathy are common and debilitating symptoms in younger-onset dementia syndromes (Chow et al., 2009; Shaw et al., 2021; Zhao et al., 2016) significantly affecting patients' quality of life and increasing caregiver burden (Silverman et al., 2022). Despite their high prevalence, the neural mechanisms underlying these motivational disturbances remain poorly understood in these populations. This study employed an unsupervised machine learning approach to identify distinct motivational subtypes in dementia and to examine their associated brain volumetric changes based on structural MRI.
Methods:
We assessed 112 patients diagnosed with Alzheimer's disease (AD), behavioural-variant frontotemporal dementia (bvFTD), or semantic dementia (SD), along with 50 matched healthy controls. Anhedonia was measured using the Snaith-Hamilton Pleasure Scale (SHAPS) (Snaith et al., 1995), and apathy was evaluated with the Dimensional Apathy Scale (DApS) (Radakovic & Abrahams, 2014). Spectral clustering was used to group patients based on apathy and anhedonia severity. Participants also underwent a whole brain T1-weighted structural MRI, following which FSL-FIRST software (Patenaude et al., 2011) was used to obtain subcortical volumetrics. The MNI structural atlas (Mazziotta et al., 2001) was used to extract cortical volumes. Statistical analyses for cognitive and clinical data were performed using IBM SPSS Statistics, version 24.0. Volumetric differences between clusters were analysed using multivariate analysis of covariance, controlling for sex, disease duration, and Addenbrooke's Cognitive Examination III (ACE-III) scores, with Sidak correction for multiple comparisons.
Results:
Spectral clustering revealed three distinct motivational subtypes. Cluster 1, with significantly higher DApS scores indicating greater apathy, consisted of 51 patients (11 AD, 32 bvFTD, 8 SD) and showed lower nucleus accumbens (p=.043) and frontal lobe (p=.006) volumes compared to controls. Cluster 2, with lower SHAPS scores reflecting anhedonia, included 8 patients (5 bvFTD, 3 SD) and showed reduced hippocampal (p=.014), frontal (p=.033), and temporal lobe (p<.001) volumes relative to controls. Cluster 3, with a mixture of anhedonia and apathy symptoms, comprised 53 patients (24 AD, 16 bvFTD, 13 SD) but no significant volumetric differences were observed compared to controls.
Conclusions:
Our data-driven analysis revealed three distinct motivational subtypes in younger-onset dementia; including one characterised by higher apathy and another by anhedonia. Each of these subtypes was associated with specific patterns of brain atrophy. While preliminary, these findings provide insights into the neural substrates of apathy and anhedonia in dementia using a transdiagnostic approach. Our study highlights the potential of combining neuroimaging volumetric analysis and machine learning techniques to inform diagnosis and guide future research into targeted treatments to improve patient outcomes.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
STRUCTURAL MRI
Other - Younger-Onset Dementia; Anhedonia; Apathy; Clustering
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Other, Please specify
-
Unsupervised machine learning technique in clustering
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
FSL
Provide references using APA citation style.
Chow, T. W., Binns, M. A., Cummings, J. L., Lam, I., Black, S. E., Miller, B. L., Freedman, M., Stuss, D. T., & Van Reekum, R. (2009). Apathy Symptom Profile and Behavioral Associations in Frontotemporal Dementia vs Dementia of Alzheimer Type. Archives of Neurology, 66(7). https://doi.org/10.1001/archneurol.2009.92
Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., Woods, R., Paus, T., Simpson, G., Pike, B., Holmes, C., Collins, L., Thompson, P., MacDonald, D., Iacoboni, M., Schormann, T., Amunts, K., Palomero-Gallagher, N., Geyer, S., … Mazoyer, B. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B, 356(1412), 1293–1322. https://doi.org/10.1098/rstb.2001.0915
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56(3), 907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046
Radakovic, R., & Abrahams, S. (2014). Developing a new apathy measurement scale: Dimensional Apathy Scale. Psychiatry Research, 219(3), 658–663. https://doi.org/10.1016/j.psychres.2014.06.010
Shaw, S., El-Omar, H., Roquet, D., Hodges, J., Piguet, O., Ahmed, R., Whitton, A., & Irish, M. (2021). Uncovering the prevalence and neural substrates of anhedonia in frontotemporal dementia. Brain, 144. https://doi.org/10.1093/brain/awab032
Silverman, H. E., Ake, J. M., Manoochehri, M., Appleby, B. S., Brushaber, D., Devick, K. L., Dickerson, B. C., Fields, J. A., Forsberg, L. K., Ghoshal, N., Graff‐Radford, N. R., Grossman, M., Heuer, H. W., Kornak, J., Lapid, M. I., Litvan, I., Mackenzie, I. R., Mendez, M. F., Onyike, C. U., … the ALLFTD consortium. (2022). The contribution of behavioral features to caregiver burden in FTLD spectrum disorders. Alzheimer’s & Dementia, 18(9), 1635–1649. https://doi.org/10.1002/alz.12494
Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., & Trigwell, P. (1995). A Scale for the Assessment of Hedonic Tone The Snaith—Hamilton Pleasure Scale.
Zhao, Q.-F., Tan, L., Wang, H.-F., Jiang, T., Tan, M.-S., Tan, L., Xu, W., Li, J.-Q., Wang, J., Lai, T.-J., & Yu, J.-T. (2016). The prevalence of
No