Poster No:
1248
Submission Type:
Abstract Submission
Authors:
Halima Rafi1, Juan Carlos Farah2, Fosco Bernasconi3, Jevita Potheegadoo4, Olaf Blanke4
Institutions:
1École Polytechnique Fédérale de Lausanne, Carouge, Geneva, 2Laboratory of Cognitive Neuroscience, Neuro-X institute & Brain Mind Institute, EPFL, Geneva, Switzerland, 3EPFL, Geneva, GE, 4Ecole Polytechnique Federale de Lausanne, Geneva, Switzerland
First Author:
Co-Author(s):
Juan Carlos Farah
Laboratory of Cognitive Neuroscience, Neuro-X institute & Brain Mind Institute, EPFL
Geneva, Switzerland
Olaf Blanke
Ecole Polytechnique Federale de Lausanne
Geneva, Switzerland
Introduction:
The temporal variability of dorsal and ventral striatal functional connectivity (FC) may provide valuable insights into the pathophysiology of non-motor symptoms in Parkinson's Disease (PD) such as apathy. In addition to the robust associations between the dorsal striatum and cognitive and motor control (Alexander et al., 1986; Middleton et al., 2000), and between the ventral striatum plays and motivation, learning and reward (Kalia et al., 2015; Russo et al., 2010), both striatal subregions play key roles in regulating prodromal and non-motor PD symptoms. Apathy is one such critical transdiagnostic symptom in neurodegenerative disorders known to be impacted by striatal-cortical pathways (Reijnders et al., 2010; Le Heron et al., 2024). Apathy is a complex condition that involves a persistent lack of motivation and reduction in goal-directed behavior, which can lead to considerable and long-lasting functional impairment (Husain & Roiser, 2018). It is a multi-dimensional syndrome that affects several areas of behavior, including reward sensitivity, emotional responsiveness, cognitive and executive function, and self-initiation (Ang et al., 2017). Research suggests that changes in dopaminergic, serotonergic, and cholinergic systems across key brain regions-including the striatum-play a significant role in the development of apathy (Husain & Roiser, 2018; Le Heron et al., 2024). Understanding these mechanisms is essential for developing treatments that specifically target apathy subtypes and help maintain or improve daily functioning in people with PD.
Methods:
In this study, we investigated the relationship between dynamic striatal FC and clinical apathy, as measured by the Dimensional Apathy Scale (Radakovic et al., 2014) in 72 PD patients. Resting-state functional MRI (rs-fMRI) data were analyzed using micro-co-activation patterns (μCAPs; Delavari et al., 2024). μCAPs is a novel, data-driven and iterative method that identifies temporally dynamic, whole-brain patterns of FC correlating with specific and partially overlapping functional subregions within a seed region, which comprised of the dorsal and ventral striatum in the present study.
Results:
Out of the six μCAPs identified in our dataset, we found significant associations between apathy and increased occurrences of a unique dynamic pattern centering around the ventral striatum. More specifically, activity in the left nucleus accumbens was associated with a μCAP showing increased functional connectivity with the right insular cortex and decreased connectivity with the brainstem, cerebellum, thalamus and anterior cingulate gyrus (pFDR = 0.00002). Importantly, no dorsal striatal-centered μCAP showed a significant relationship with apathy in this cohort.
Conclusions:
Our work used an innovative method to link clinical apathy in PD to a dynamic FC pattern centering on the ventral, rather than dorsal striatum, and linking it to major hubs of a cortico-striatal network spanning the insular cortex, thalamus anterior cingulate gyrus, and brainstem. Together these regions have been associated with salience and emotion processing, suggesting a link to the emotional-affective apathy subtype. Modulating this dynamic network may not only advance our understanding of the neural underpinnings of apathy but also provide potential biomarkers for earlier detection and targeted interventions in PD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Basal Ganglia
Brainstem
Data analysis
FUNCTIONAL MRI
Limbic Systems
MRI
Statistical Methods
Other - Micro Coactivation Pattern Analysis
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.
Resting state
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:
Functional MRI
Neuropsychological testing
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
1. Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual review of neuroscience, 9(1), 357-381.
2. Middleton, F. A., & Strick, P. L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain research reviews, 31(2-3), 236-250
3. Grueter, B. A., Rothwell, P. E., & Malenka, R. C. (2012). Integrating synaptic plasticity and striatal circuit function in addiction. Current opinion in neurobiology, 22(3), 545-551.
4. Kalia, L. V., & Lang, A. E. (2015). Parkinson's disease. The Lancet, 386(9996), 896-912.
5. Russo, S. J., Dietz, D. M., Dumitriu, D., Morrison, J. H., Malenka, R. C., & Nestler, E. J. (2010). The addicted synapse: mechanisms of synaptic and structural plasticity in nucleus accumbens. Trends in neurosciences, 33(6), 267-276
6. Reijnders, J. S., Scholtissen, B., Weber, W. E., Aalten, P., Verhey, F. R., & Leentjens, A. F. (2010). Neuroanatomical correlates of apathy in Parkinson's disease: A magnetic resonance imaging study using voxel‐based morphometry. Movement Disorders, 25(14), 2318-2325
7. Kos, C., van Tol, M. J., Marsman, J. B. C., Knegtering, H., & Aleman, A. (2016). Neural correlates of apathy in patients with neurodegenerative disorders, acquired brain injury, and psychiatric disorders. Neuroscience & Biobehavioral Reviews, 69, 381-401
8. Le Heron, C., Horne, K. L., MacAskill, M. R., Livingstone, L., Melzer, T. R., Myall, D., ... & Harrison, S. (2024). Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease. Neurology, 102(12), e209301.
9. Radakovic, R., & Abrahams, S. (2014). Developing a new apathy measurement scale: Dimensional Apathy Scale. Psychiatry research, 219(3), 658-663.
10. Delavari, F., Sandini, C., Kojovic, N., Saccaro, L. F., Eliez, S., Van De Ville, D., & Bolton, T. A. (2024). Thalamic contributions to psychosis susceptibility: Evidence from co‐activation patterns accounting for intra‐seed spatial variability (μCAPs). Human Brain Mapping, 45(5), e26649.
No