Hippocampus activation in cognitively normal older individuals during object pattern separation
Poster No:
839
Submission Type:
Abstract Submission
Authors:
Kevin Solar1, Sriranga Kashyap1, Nicolas Deom2, Ljubica Zotovic3, Kâmil Uludağ1, Krista Lanctôt4, Sandra Black3, Mary Pat McAndrews1
Institutions:
1Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Canada, 2Department of Psychology, University of Toronto, Toronto, Canada, 3Division of Neurology, Department of Medicine, Sunnybrook Research Institute, University of Toronto, Toronto, Canada, 4Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Canada
First Author:
Kevin Solar, BSc, MSc, PhD
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Co-Author(s):
Sriranga Kashyap
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Ljubica Zotovic
Division of Neurology, Department of Medicine, Sunnybrook Research Institute, University of Toronto
Toronto, Canada
Division of Neurology, Department of Medicine, Sunnybrook Research Institute, University of Toronto
Toronto, Canada
Kâmil Uludağ
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krista Lanctôt
Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute
Toronto, Canada
Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute
Toronto, Canada
Sandra Black
Division of Neurology, Department of Medicine, Sunnybrook Research Institute, University of Toronto
Toronto, Canada
Division of Neurology, Department of Medicine, Sunnybrook Research Institute, University of Toronto
Toronto, Canada
Mary Pat McAndrews
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Krembil Brain Institute, Toronto Western Hospital, University Health Network
Toronto, Canada
Introduction:
The hippocampus is critical for memory and is among the first brain areas affected by Alzheimer's disease (AD) (Rao et al., 2022; Scheltens et al., 2016). AD pathologies, including beta-amyloid and tau, accumulate in the brain years before AD symptoms develop, and they are associated with hyperactivation which accelerates synaptic dysfunction and propagates beta-amyloid and tau in a vicious cycle (e.g., Marks et al., 2017). Hippocampus hyperactivation has been shown in cognitively normal (CN) and mild cognitive impairment (MCI) stages of AD, followed by hypoactivation in dementia (e.g., Reagh et al., 2019). Levetiracetam reduces hyperactivity and improves episodic memory in amnestic MCI patients (Bakker et al., 2012, 2015), but there have been no trials in CN individuals with hippocampal hyperactivation. By treating pre-symptomatic, at-risk individuals, the onset of cognitive deficits may be delayed or even averted.
To identify CN individuals with hyperactivation, we need to define the normal range of task-based activation; the current literature is based on small-sample group effects, without providing a 'threshold' for excess activation in single subjects. Additionally, multi-echo fMRI, which offers better signal-to-noise ratio and BOLD signal sensitivity than prior work, could further reduce the current long (~45 min) scan times. Thus, the purpose of this study was 1) to establish a normal reference range with a 95% confidence interval for hippocampal activation in object pattern separation, and 2) to determine if we can decrease the scan time necessary to characterize this potential biomarker of risk for AD.
To identify CN individuals with hyperactivation, we need to define the normal range of task-based activation; the current literature is based on small-sample group effects, without providing a 'threshold' for excess activation in single subjects. Additionally, multi-echo fMRI, which offers better signal-to-noise ratio and BOLD signal sensitivity than prior work, could further reduce the current long (~45 min) scan times. Thus, the purpose of this study was 1) to establish a normal reference range with a 95% confidence interval for hippocampal activation in object pattern separation, and 2) to determine if we can decrease the scan time necessary to characterize this potential biomarker of risk for AD.
Methods:
28 participants (19 females; x̄ age = 71±7 [range: 58–85] years) were recruited at Sunnybrook Hospital in Toronto, Canada. MRI data were collected on a 3T Siemens Prisma scanner at the Toronto Western Hospital Slaight imaging centre, using a 32-ch head coil, including a T1-w image (1×1×1 mm3; 4:20 min), T2-w image (0.4×0.4×2 mm3; 6 min), and multi-echo BOLD fMRI (1.5×1.5×1.5 mm3; TEs 13/36/59 ms; TR = 1.8 s; 5:31 min/run) acquired in two batches of four runs each.
We utilized the Automated Segmentation of Hippocampal Subfields protocol (UPenn PMC atlas) to segment CA3 and dentate gyrus (DG) subfields (Yushkevich et al., 2015) and AFNI (Cox & Hyde, 1997) to process the fMRI data, including despiking, slice time correction, anatomical coregistration, distortion-correction, spatial smoothing, multi-echo combination (tedana), and intensity normalization. Then, we extracted the average beta coefficient from left/right CA3/DG subfields during object pattern separation (Fig 1) and employed ANOVAs to test the effect of runs in hippocampal activation and memory performance.
We utilized the Automated Segmentation of Hippocampal Subfields protocol (UPenn PMC atlas) to segment CA3 and dentate gyrus (DG) subfields (Yushkevich et al., 2015) and AFNI (Cox & Hyde, 1997) to process the fMRI data, including despiking, slice time correction, anatomical coregistration, distortion-correction, spatial smoothing, multi-echo combination (tedana), and intensity normalization. Then, we extracted the average beta coefficient from left/right CA3/DG subfields during object pattern separation (Fig 1) and employed ANOVAs to test the effect of runs in hippocampal activation and memory performance.
Results:
There was no effect of run-selection on activation in the CA3/DG hippocampal subfields (x̄1-8 = 0.39 ± 0.91; x̄1-4 = 0.19 ± 1.02; x̄5-8 = 0.60 ± 1.71; F(2,81) = 0.74, p = 0.48) nor in memory performance (x̄1-8 = 0.30 ± 0.17; x̄1-4 = 0.28 ± 0.18; x̄5-8 = 0.30 ± 0.20; F(2,81) = 0.13, p = 0.88) (Fig 2). These results suggest that with multi-echo BOLD fMRI, 22 mins (four runs) of the memory task was sufficient to capture hippocampal subfield activation.
Conclusions:
The importance of these results is two-fold: 1) we defined the distribution of normal hippocampal CA3/DG activation during an object pattern separation task, and 2) we demonstrated that four task runs at ~22 min scan time is sufficient to achieve a robust signal. By using this distribution to define a 'threshold' for normality, we can identify pre-symptomatic individuals and begin to test effectiveness of levetiracetam in reducing hyperactivation in this cohort, to propel further trials on preventing neurodegeneration. Furthermore, key benefits from reducing scan time by ~22 min include decreasing study costs and attrition, and increasing participant engagement.
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
FUNCTIONAL MRI
Limbic Systems
Memory
MRI
Sub-Cortical
Other - Hippocampus; pattern separation
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
2. Bakker, A., Krauss, G. L., Albert, M. S., Speck, C. L., Jones, L. R., Stark, C. E., Yassa, M. A., Bassett, S. S., Shelton, A. L., & Gallagher, M. (2012). Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron, 74(3), 467–474. https://doi.org/10.1016/j.neuron.2012.03.023
3. Cox, R. W., & Hyde, J. S. (1997). Software tools for analysis and visualization of fMRI data. NMR in Biomedicine, 10(4–5), 171–178. https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L
4. Marks, S. M., Lockhart, S. N., Baker, S. L., & Jagust, W. J. (2017). Tau and β-Amyloid are associated with medial temporal lobe structure, function, and memory encoding in normal aging. The Journal of Neuroscience, 37(12), 3192–3201. https://doi.org/10.1523/JNEUROSCI.3769-16.2017
5. Rao, Y. L., Ganaraja, B., Murlimanju, B. V., Joy, T., Krishnamurthy, A., & Agrawal, A. (2022). Hippocampus and its involvement in Alzheimer’s disease: a review. 3 Biotech, 12(2), 1–10. https://doi.org/10.1007/s13205-022-03123-4
6. Reagh, Z. M., Noche, J. A., Tustison, N. J., Delisle, D., Murray, A., & Yassa, M. A. (2019). Functional imbalance of anterolateral entorhinal cortex and hippocampal dentate/CA3 underlies age-related object pattern separation deficits. 97(5), 1187–1198. https://doi.org/10.1016/j.neuron.2018.01.039
7. Scheltens, P., Blennow, K., Breteler, M. M. B., de Strooper, B., Frisoni, G. B., Salloway, S., & Van der Flier, W. M. (2016). Alzheimer’s disease. The Lancet, 388(10043), 505–517. https://doi.org/10.1016/S0140-6736(15)01124-1
8. Yushkevich, P. A., Pluta, J. B., Wang, H., Xie, L., Ding, S. L., Gertje, E. C., Mancuso, L., Kliot, D., Das, S. R., & Wolk, D. A. (2015). Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human Brain Mapping, 36(1), 258–287. https://doi.org/10.1002/hbm.22627