Abstract

High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer’s disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain contains neural circuitry involved in olfactory perception. Several studies have suggested that olfactory deficit can be used as a marker for early diagnosis of AD. A quantitative assessment of the performance of the olfactory system can hence serve as a potential biomarker for Alzheimer’s disease, offering a relatively convenient and inexpensive diagnosis method. This study examines the decline in the perception of olfactory stimuli and the deficit in the performance of high-frequency frontal oscillations in response to olfactory stimulation as markers for AD. Two measurement modalities are employed for assessing the olfactory performance: 1) An interactive smell identification test is used to sample the response to a sizable variety of odorants, and 2) Electroencephalography data are collected in an olfactory perception task with a pair of selected odorants in order to assess the connectivity of frontal cortex regions. Statistical analysis methods are used to assess the significance of selected features extracted from the recorded modalities as Alzheimer’s biomarkers. Olfactory decline regressed to age in both healthy and mild AD groups are evaluated, and single- and multi-modal classifiers are also developed. The novel aspects of this study include: 1) Combining EEG response to olfactory stimulation with behavioral assessment of olfactory perception as a marker of AD, 2) Identification of odorants most significantly affected in mild AD patients, 3) Identification of odorants which are still adequately perceived by mild AD patients, 4) Analysis of the decline in the spatial coherence of different oscillatory bands in response to olfactory stimulation, and 5) Being the first study to quantitatively assess the performance of olfactory decline due to aging and AD in the Iranian population.

Highlights

  • Alzheimer’s disease (AD) is the most prevalent type of dementia affecting approximately one individual in 10 in the population older than 65 [1]

  • Perception of smells is affected more severely in mild AD patients compared to its decline caused by normal aging, and several studies have suggested that olfactory deficit can be used as a biomarker for early diagnosis of AD [20, 21]

  • An support vector machine (SVM) classifier with a linear kernel was used to separate mild AD patients from healthy participants based on the significant components of the modified University of Pennsylvania smell identification test (UPSIT) test and the significant imaginary part of coherence (ImCoh) values between the EEG electrodes, which were calculated for the beta and gamma frequency bands in the Fz-Cz connection

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Summary

Introduction

Alzheimer’s disease (AD) is the most prevalent type of dementia affecting approximately one individual in 10 in the population older than 65 [1]. Further studies have demonstrated that standard methods of assessing the olfactory system such as sniffing kits can be helpful in distinguishing mild AD patients from healthy individuals [14,15,16]. Perception of smells is affected more severely in mild AD patients compared to its decline caused by normal aging, and several studies have suggested that olfactory deficit can be used as a biomarker for early diagnosis of AD [20, 21]. In a more recent study [28], OERP test was employed to distinguish between AD and mild cognitive impairment (MCI) patients Another method for the differential analysis of EEG data of mild AD patients and healthy participants is coherence analysis. By employing the statistically significant components from both modalities, we propose a multi-modal classifier of mild AD patients versus healthy participants, which regresses the olfactory decline due to aging

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