There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Michael McKenna |
| Institution: | Ohio State University |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | Previous research has indicated that attention deficits are a common symptom in both Alzheimer’s Disease and preclinical stages of AD. In fact, there is some evidence that attention deficits are one of the earlier symptoms that can be detected in preclinical stages of Alzheimer’s Disease (mild cognitive impairment), often appearing even before verbal memory deficits (Belleville, Cherrkow & Gauthiaer, 2007). However, despite these findings, much of the previous neuroimaging literature has focused on episodic memory (for example, Sheline & Raichle, 2013, Lau, Leung, Lee, & Law, 2016), rather than looking at a wide-range of impairments. This is understandable as episodic memory dysfunction is a common symptom in a heterogeneous disease, but fails to address individual progressions on the path to dementia symptoms. Additionally, previous neuroimaging analyses often focus on specific a priori regions, especially the default mode network, possibly excluding areas that could be of interest. Due to the heterogeneous nature of mild cognitive impairment, individual-level data-driven techniques could prove useful in categorizing the uniqueness that is MCI. A data driven technique in young adults has identified a set of large-scale functional networks which, while accounting for individual differences in functional connectivity patterns, can predict over 70 percent of the variance in individual-level performance on a gradual-onset continuous performance task (Rosenberg et al., 2016a). These two networks, collectively called the sustained attention Connectome Predictive Modeling networks (saCPM), are divided into a high-attention network and a low-attention network, have been generalized to predict a number of clinical and nonclinical attributes and symptoms (Rosenberg et al., 2016b, Rosenberg et al., 2016a). Recent work by our lab generalized these saCPM networks to a sample of cognitively normal, older adults, predicting attentional control capabilities in the form of reaction time cost in a modified Stroop task (Fountain-Zaragoza, Samimy, Rosenberg, & Prakash, 2019). Within the ADNI dataset, Lin et al (2018) have used a CPM approach to predict ADAS-Cog scores at baseline. The positive network that they derived (not the positive saCPM, but a novel network) was able to explain 49 percent of the variance in ADAS-Cog scores, although their results were non-significant for their negative network. Our analyses hope to bridge these two studies. Utilizing a CPM approach, we will apply the saCPM networks to the ADNI-Go and ADNI-2 resting state data with the attempt to predict symptom severity on baseline based on network strength. As attention deficits have been shown as a major symptom in MCI, we hypothesize that stronger connections in the high-attention network will be associated with lower symptoms severity on the ADAS-Cog scale; inversely, stronger connections in the low-attention network will be associated with higher symptom severity scores. References Belleville, S., Chertkow, H., & Gauthier, S. (2007). Working memory and control of attention in persons with Alzheimer’s disease and mild cognitive impairment. Neuropsychology, 21(4), 458–469. Fountain-Zaragoza, S., Samimy, S., Rosenberg, M. D., & Prakash, R. S. (2019). Connectome-based models predict attentional control in aging adults. NeuroImage, 186, 1–13. https://doi.org/10.1016/j.neuroimage.2018.10.074 Lau, W. K. W., Leung, M.-K., Lee, T. M. C., & Law, A. C. K. (2016). Resting-state abnormalities in amnestic mild cognitive impairment: a meta-analysis. Translational Psychiatry, 6(4), e790. https://doi.org/10.1038/tp.2016.55 Lin, Q., Rosenberg, M. D., Yoo, K., Hsu, T. W., O’Connell, T. P., & Chun, M. M. (2018). Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer’s Disease. Frontiers in Aging Neuroscience, 10. https://doi.org/10.3389/fnagi.2018.00094 Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016a). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171. Rosenberg, M. D., Zhang, S., Hsu, W.-T., Scheinost, D., Finn, E. S., Shen, X., … Chun, M. M. (2016b). Methylphenidate modulates functional network connectivity to enhance attention. Journal of Neuroscience, 36(37), 9547–9557. Sheline, Y. I., & Raichle, M. E. (2013). Resting State Functional Connectivity in Preclinical Alzheimer’s Disease. Biological Psychiatry, 74(5), 340–347. https://doi.org/10.1016/j.biopsych.2012.11.028 |
| Additional Investigators | |
| Investigator's Name: | Oye Gbadeyan |
| Proposed Analysis: | Postdoc developing fMRI analysis methods for project |
| Investigator's Name: | James Teng |
| Proposed Analysis: | Graduate student who will be helping with fMRI analyses |
| Investigator's Name: | Madhura Phansikar |
| Proposed Analysis: | I am a postdoctoral researcher at Ohio State University and my background is in mindfulness, cognition, and psychosocial well-being. I will be working on a paper examining the role of min-wandering in psychosocial well-being among older adults and would like to use the extensive ADNI dataset for these analyses. |
