Poster No:
277
Submission Type:
Abstract Submission
Authors:
Karl-Heinz Nenning1, Florian Fischmeister2, Astrid Novak2, Rainer Seidl2, Ting Xu3, Gregor Kasprian2, Lisa Bartha-Doering2, Kathrin Kollndorfer2
Institutions:
1Nathan Kline Institute, Orangeburg, NY, 2Medical University of Vienna, Vienna, Austria, 3Child Mind Institute, New York, NY
First Author:
Co-Author(s):
Ting Xu
Child Mind Institute
New York, NY
Introduction:
Childhood arterial ischemic stroke is a rare but severe disorder that can cause lasting cognitive impairments, particularly in executive functions (Mallick et al., 2014). While early research assumed an improved outcome due to better neuronal plasticity in childhood stroke patients compared to adult patients, more recent studies indicate similar rates of disabilities and cognitive impairment. Previous studies using resting-state fMRI to investigate the impact of stroke found an association between large-scale functional network disruptions and neurological impairment (Rehme & Grefkes, 2013; Siegel et al., 2016; Tao & Rapp, 2020), and that behavioral deficits post-stroke were better predicted by functional connectivity than lesion size alone (Salvalaggio et al., 2020). Here, we use resting-state fMRI to study transient brain states in children following childhood arterial ischemic stroke, the relationship between their altered dynamics and cognitive performance, and how these dynamics relate to the extent functional networks are impacted by the location of the lesion.
Methods:
We studied 16 patients (13.06±2.89 years; 7 female) and 17 age- and sex-matched typically developing children (10.75±2.89 years; 7 female). All participants underwent a standardized assessment of cognitive performance (fluid reasoning index) and working memory, and parents completed questionnaires about their child's executive functioning. Structural and functional MRI imaging was acquired on a 3T Siemens MRI scanner, with 5 minutes of resting-state. Standard neuroimaging preprocessing was performed with fMRIprep. We used whole-brain coactivation pattern (CAP) analysis to identify 8 recurrent patterns of fMRI coactivation (Liu et al., 2013). For each individual and CAP, we quantified the occurrence rate, dwell time, and the transition rates between each CAP pair. Additionally, we quantified the overlap between stroke lesions and seven canonical resting-state networks (Yeo et al., 2011).
Results:
We observed that primarily the dynamics of one specific coactivation pattern (CAP4) which characterized the frontoparietal network were highly related to cognitive function and outcome. The occurrence rate of CAP4 showed a significant negative association with fluid reasoning (r=-0.61, pFDR=0.0040) across the entire study cohort but also within the patient group (r=-0.72, pFDR=0.0427). Negative associations, albeit not significant after FDR correction, between dwell times and executive functions and working memory scores were also observed for this CAP. Moreover, patients with below-average cognitive performance showed a significantly higher transition rate into this CAP than patients with average cognitive performance (pFDR=0.0012) and typically developing children (pFDR=0.0148). No difference was found between typically developing children and patients with average cognitive performance (pFDR=0.3604). Correlation analysis between transition rates and cognitive performance in the patient group revealed significant negative and positive associations for all three cognitive tests. Additionally, the overlap between stroke lesion and resting-state networks showed a stronger correlation with the temporal properties of the coactivation patterns than lesion size.
Conclusions:
Collectively, our results suggest that childhood stroke disrupts the typical developmental trajectory of macroscale brain dynamics. In particular, increased occurrence rates and dwell times hint at a diminished ability to utilize a frontoparietal brain state with implications on cognitive performance. Importantly, while lesion size was different across patient subgroups, the overlap between stroke lesion and functional networks was more informative than lesion size alone. Taken together, our findings emphasize the importance of considering brain dynamics when assessing and treating cognitive impairments after childhood stroke.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Keywords:
FUNCTIONAL MRI
Other - resting-state fMRI; co-activation patterns; pediatric stroke
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?
AFNI
FSL
Free Surfer
Provide references using APA citation style.
Mallick, A. A., Ganesan, V., Kirkham, F. J., Fallon, P., Hedderly, T., McShane, T., Parker, A. P., Wassmer, E., Wraige, E., Amin, S., Edwards, H. B., Tilling, K., & O'Callaghan, F. J. (2014). Childhood arterial ischaemic stroke incidence, presenting features, and risk factors: a prospective population-based study. The Lancet. Neurology, 13(1), 35–43.
Rehme, A. K., & Grefkes, C. (2013). Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans. The Journal of physiology, 591(1), 17–31.
Siegel, J. S., Ramsey, L. E., Snyder, A. Z., Metcalf, N. V., Chacko, R. V., Weinberger, K., Baldassarre, A., Hacker, C. D., Shulman, G. L., & Corbetta, M. (2016). Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proceedings of the National Academy of Sciences of the United States of America, 113(30), E4367–E4376.
Tao, Y., & Rapp, B. (2020). How functional network connectivity changes as a result of lesion and recovery: An investigation of the network phenotype of stroke. Cortex; a journal devoted to the study of the nervous system and behavior, 131, 17–41.
Salvalaggio, A., De Filippo De Grazia, M., Zorzi, M., Thiebaut de Schotten, M., & Corbetta, M. (2020). Post-stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain : a journal of neurology, 143(7), 2173–2188.
Liu, X., Chang, C., & Duyn, J. H. (2013). Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Frontiers in systems neuroscience, 7, 101.
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology, 106(3), 1125–1165.
No