Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer’s disease: protocol for a rapid systematic review

Dementia causes deterioration of cognitive function that is expected to affect approximately 65.7 million individuals around the world by 2030 [1]. Although there are several types of dementia, Alzheimer’s disease (AD) is the most common type, which comprises around 60-80% of dementia cases [2]. This disease has not only consequences on a social level in terms of costs but also more importantly on patient’s and caregiver’s quality of life [3]. AD can be categorized into three phases: (1) the preclinical phase, which may begin years before diagnosis as AD [4]; (2) mild cognitive impairment (MCI), the transitional stage between normal cognition and AD, during which individuals experience slight impairments in their cognitive function and memory, while maintaining the ability to perform their daily activities [5]; (3) AD, that is indicated by decline in cognitive function and changes in behavior [2]. Although some individuals with MCI remain stable or return to normal cognition, progression to AD occurs at a rate of about 10% to 15% per year [6, 7]. Those cases where individuals convert from MCI to AD are termed progressive MCI (pMCI) and those cases where cognition remains stable are termed stable MCI (sMCI). Currently, it is not entirely understood why some individuals experience pMCI and convert to AD. Therefore, in a clinical context, it is not clear which patients with MCI would benefit the most from clinical interventions and cognitive rehabilitation programs. The ability to predict who will progress to AD dementia from MCI will allow identifying the individuals who could benefit the most from clinical trials. As a result, early prediction of AD could provide the potential therapeutic windows to slow down the progression process [8].

To predict the conversion to Alzheimer’s, researchers have followed several directions. Some studies investigated genetic assessments or biological markers such as those in cerebrospinal fluid (CSF) and many of them explored neuroimaging modalities like electroencephalography (EEG) and functional imaging [9‐12]. The main limitation of CSF biomarkers is that their collection requires the invasive procedure of lumbar puncture which is not recommended in routine clinical assessments [13, 14].

Many studies show that AD pathology can be predicted with high accuracy using neuroimaging modalities [15, 16]. These modalities mainly include structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), functional single-photon emission computed tomography (SPECT), and electroencephalography (EEG) [16‐18]. In each modality, researchers measure the changes of different biomarkers (i.e., changes in gray matter, white matter, or cortical thickness in MRI modality) during the conversion process to predict the conversion from MCI to AD [19, 20]. Both structural and functional magnetic resonance imaging modalities are noninvasive and non-radiation tools for detecting neuronal degeneration at the early stage of AD and investigating progressive brain changes from MCI to AD [21]. Researchers have used PET modalities to investigate metabolic changes during AD progression. In comparison with MRI, PET studies are more invasive and less available, due to their radiation exposure and higher costs [22]. The other type of modality is EEG, which is a non-invasive, extensively available, and low-cost procedure to record the electrical activity of the brain. The findings of a recent study show that EEG coherence is a great predictor of AD progression [23]. Since each modality has its advantages and limitations, it can be expected that the combination of different modalities could provide complementary information [9, 24, 25].

After the identification of informative AD modalities, appropriate data analysis techniques are required to be applied to data to assess the conversion from MCI to AD [26‐28]. Recent decades have shown significant growth in the emergence of machine-learning (ML) and pattern-recognition techniques as the effective tools for analyzing brain images to develop the prognosis of AD, while the pathological signs are not observable by visual assessments [5].

In this study, we will conduct a rapid systematic review of the most recent studies that have investigated the progression from MCI to AD dementia using neuroimaging modalities along with machine learning techniques. Thus, the state of the art in neuroimaging and neurodegenerative assessment is presented. There will be two main fields of synthesis in this review. Firstly, we will explore the most recent neuroimaging procedures used to predict the conversion from MCI to AD for clinical applications. Secondly, we will systematically investigate the machine learning techniques applied to create predictive models for the classification of individuals with progressive MCI versus stable MCI.

This rapid review will provide a knowledge synthesis of the potential neuroimaging modalities as well as data analysis techniques used in recent years to predict the progression from mild cognitive impairment to Alzheimer’s disease dementia. Additionally, we will emphasize the essential need for early prediction of AD to provide the potential therapeutic windows for this progressive disease. Basically, the two main contributions of our review will be as following: (1) an investigation of the recent neuroimaging modalities used for prediction of AD dementia progression, and (2) a discussion of data analysis techniques employed to measure the brain changes during the conversion process. The review will merge different domains of cognitive science, neuroscience, and computer science to benefit the research focused on the prediction of Alzheimer’s disease in the early stage of MCI. The results of the review will identify possible gaps in the current studies and brings knowledge for further research. Any amendments or modifications made in the protocols will be outlined and reported in the final papers. We anticipate a limitation of this study will be a lack of complete and in-depth reporting of biomarkers, data analysis techniques, and data sources in the prediction of AD dementia studies.

Another limitation of this study is that we restricted our search to the studies published from January 1, 2017, to the date of search commencement, which would be a relatively short timeline. The reason behind this consideration is that the review paper has focused on a cutting-edge research topic that is fast-paced and developing quickly, we aimed to examine only the most recent studies, not the outdated ones.