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Paper   IPM / Cognitive Sciences / 16484
School of Cognitive Sciences
  Title:   Electroencephalography (EEG) reveals a decrease in speed of animacy processing in mild cognitive impairment and an alteration in neural response patterns
  Author(s): 
1.  H. Karimi
2.  H. Marefat
3.  M. Khanbagi
4.  C. Kalafatis
5.  Z. Vahabi
6.  S. Khaligh Razavi
  Status:   In Proceedings
  Proceeding: Alzheimer�??s & Dementia, Alzheimer�??s Association International Conference (AAIC), 2020 July 26-30.
  Year:  2020
  Supported by:  IPM
  Abstract:
Background: Electroencephalography (EEG) has been commonly used to measure the brain alterations in the early stages of Alzheimer’s Disease. However, the reported measures are limited to the univariate changes, including activation level and frequency bands. To look beyond the activation level, we used a task-based EEG and applied multivariate pattern analysis (MVPA) to study the changes in the pattern of information processing. Method: We recruited 18 participants with mild cognitive impairment (MCI), and 22 age-matched healthy controls (HC). We acquired EEG data from all participants, while they were doing the integrative cognitive assessment (ICA) test (Khaligh-Razavi et al., 2019). ICA is a rapid visual categorization task, in which participants are presented with a sequence of natural images of animals and non-animals, and are asked to respond as quickly and accurately, whether images contained an animal. To analyze the EEG data, we took advantage of MVPA techniques to measure how accurately EEG signals can discriminate between brain representations of animals and non-animals. Result: Univariate analysis showed a significant decline in the activation level of the right temporal lobe in MCI compared to HC. In the left parietal lobe, we found that the pattern of EEG responses to the presented visual stimuli was significantly different between HC and MCI, in the absence of any difference in their level of activation. Furthermore, we found that animacy information (animal vs. non-animal categorization) emerges later in the brain of patients with MCI (t = 67 ms ± 33 SE of mean; p-value ≤ 0.03) compared to HC. Conclusion: We demonstrated that in addition to the level of activation (i.e., mean ERP response), the pattern of EEG responses to visual stimuli also carries information about the status of the disease. In particular, we see that in some of the brain areas where the mean activation shows no difference between HC and MCI, patterns of EEG responses are significantly different and can be used to discriminate MCI from HC. Furthermore, the results also suggest a delay or impairment in the speed of processing animacy information in MCI patients.

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