A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

 With your likely chance of getting dementia you'll want this test to baseline you and if needed use those dementia prevention protocols your doctor has so assiduously put together.

Your chances of getting dementia.

1. A documented 33% dementia chance post-stroke from an Australian study?   May 2012.

2. Then this study came out and seems to have a range from 17-66%. December 2013.`    

3. A 20% chance in this research.   July 2013.

4. Dementia Risk Doubled in Patients Following Stroke September 2018 

The latest here:

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Ibrahim Almubark^1*, Lin-Ching Chang¹, Kyle F. Shattuck², Thanh Nguyen¹, Raymond Scott Turner³ and Xiong Jiang^2*

• ¹Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
• ²Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
• ³Department of Neurology, Georgetown University Medical Center, Washington, DC, United States

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.

Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.

Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.

Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in older adults. Due to significant progress in basic and clinical research, putative disease-modifying treatments for AD may be on the horizon - which may be most effective in early disease stages. As a result, there is increasing impetus to develop techniques that have high sensitivity and specificity to assist in the diagnosis of early AD (Fiandaca et al., 2014).

Machine learning—with the ability to extract features from high dimensional spaces—holds strong promise in assisting disease diagnosis in both translational research and clinical practice (Weng et al., 2017; Dwyer et al., 2018), especially with recent advances in deep learning techniques (Esteva et al., 2017). Over the past decade, there has been increasing interest in developing machine learning techniques to assist in the diagnosis of AD and mild cognitive impairment (MCI) and to predict disease progression. Most of these studies focus on brain imaging data from magnetic resonance imaging (MRI) or positron emission tomography (PET) scans (Pellegrini et al., 2018), or cerebrospinal fluid (CSF) proteomics to assess CNS amyloid deposition (A), pathologic tau accumulation (T), and neurodegeneration (N) – the A/T/N criteria under the current NIA-AA research framework (Jack et al., 2018). Compared to brain imaging data, behavioral data are feasible and relatively inexpensive to collect. Behavioral data from speech (Fraser et al., 2016; Nagumo et al., 2020), body movement (Khan and Jacobs, 2020), and neuropsychologic test scores (Lemos et al., 2012; Williams et al., 2013; Kang et al., 2019; Lee et al., 2019a) may provide useful features to machine learning classifiers for the diagnosis of MCI and AD.

In addition to standard neuropsychological tests that are widely used in both research and clinical environments, cognitive tasks are usually highly specific and customized and are often only used in research studies. Compared to standard neuropsychological tests, cognitive tasks have certain advantages and disadvantages: on the one hand, cognitive tasks are usually limited by a lack of standardized data and/or validation with a large population of participants; on the other hand, cognitive tasks are often based on cutting-edge research hypothesis and may be more sensitive in detecting very specific changes in brain function due to brain disease such as AD (Perry and Hodges, 1999) – which might eventually lead to the development of improved and/or novel neuropsychological tests (or being integrated with existing neuropsychological test battery) that can be used in clinical practice after validation. Machine learning studies have shown that data from certain cognitive tasks may contain useful information to differential AD/MCI patients from healthy controls (Wallert et al., 2018; Valladares-Rodriguez et al., 2019; Hong et al., 2020). Therefore, it is of a high interest to investigate whether a combination of neuropsychological tests and cognitive task(s) may improve machine learning-based classification accuracy in AD (Wallert et al., 2018; He et al., 2019). In a previous study with traditional machine learning models and multivariate feature selection techniques, we investigated the classification performance with data from a standard neuropsychological test battery, a 5-min cognitive task, or both, to distinguish CN from MCI/AD patients (Almubark et al., 2019). The cognitive task was designed to assess the effects of spatial inhibition of return (IOR). Spatial IOR refers to the phenomenon by which individuals are slower to respond to stimuli appearing at a previously cued location compared to un-cued locations when the stimuli onset asynchrony (SOA) between the target and cue is long (~300–500 ms or more) (Klein, 2000). First reported by Posner and Cohen (1984), spatial IOR has been extensively studied, including in healthy older adults (Hartley and Kieley, 1995), patients with various neurogenerative disorders (Possin et al., 2009; Bayer et al., 2014), and non-human subjects (Shariat Torbaghan et al., 2012). In addition to the superior colliculus (Posner et al., 1985), cortical areas such as the temporoparietal junction (TPJ) and the inferior parietal cortex are important to maintain normal spatial IOR function (Seidel Malkinson and Bartolomeo, 2018; Satel et al., 2019). Both regions have been are implicated in AD progression (Besson et al., 2015), suggesting that spatial IOR may be useful to assist MCI and AD diagnosis. While early studies suggest that spatial IOR is relatively preserved in AD (Amieva et al., 2004), recently we (Jiang et al., 2020) and others (Tales et al., 2005, 2011; Bayer et al., 2014) have provided evidence that spatial IOR impairment in MCI/AD, and spatial IOR impairment in MCI patients may be predictive of conversion to dementia (Bayer et al., 2014). Therefore, machine learning with spatial IOR data may be useful in assisting diagnosis of MCI and AD. In addition, spatial IOR have two appealing features: first, the task is simple to understand and easy to implement, thus making it a feasible tool with AD/MCI patients in a typical clinical setting; second, spatial IOR is robust and resistant to practice effect (Pratt and McAuliffe, 1999; Bao et al., 2011), thus making it an ideal tool in longitudinal studies or clinical trials. However, in the previous study, we found that the classification performance with IOR data as well as the NP data had a low sensitivity and combining IOR and neuropsychological data did not significantly improve classification accuracy (Almubark et al., 2019), suggesting a need for further research.

Deep learning has advantages over machine learning due to its capacity of extracting useful features from highly complex and non-linear datasets (Pedregosa et al., 2011; LeCun et al., 2015), and is gaining popularity in AD research. For example, a PubMed search revealed 8 relevant publications before 2017, 8 in 2017, 26 in 2018, and 65 in 2019. Convolutional-Neural Network (CNN) is the most commonly used deep learning techniques (Gautam and Sharma, 2020). The overwhelming majority of these studies have been focusing on complex and high dimension brain imaging data, especially PET and structural MRI (Jo et al., 2019; Ebrahimighahnavieh et al., 2020; Gautam and Sharma, 2020; Haq et al., 2020). Several recent studies have aimed to integrate multimodal imaging to improve classification performance (Suk et al., 2014; Lu et al., 2018; Huang et al., 2019; Punjabi et al., 2019; Zhou et al., 2019). Deep learning can also help to identify features that are important for disease progression or serve as markers for clinical trials (Ithapu et al., 2015). In addition to harvesting brain imaging data [especially the multimodality imaging data from the public ADNI database (http://adni.loni.usc.edu/)], deep learning has been applied to biospecimens (Lee et al., 2019b; Lin et al., 2020), electronic health records (Landi et al., 2020; Nori et al., 2020), speech (Lopez-de-Ipina et al., 2018), neuropsychological data (Choi et al., 2018; Kang et al., 2019), and a combination of MRI and neuropsychological data (Qiu et al., 2018; Duc et al., 2020). By contrast, few studies have applied deep learning to cognitive task data, which – by design – is supposed to be more sensitive to detect early and mild neurocognitive impairment (Locascio et al., 1995; Perry and Hodges, 1999). Highly relevant to the present study, Rutkowski et al. applied various traditional and deep learning models to behavioral data collected from a facial emotion implicit short term memory task (Rutkowski et al., 2020). In their study, Rutkowski et al. obtained an accuracy close to 90% in distinguishing MCI from normal older adults with either deep learning or logistic regression, supporting a potential of deep learning with cognitive task to aid MCI/AD diagnosis. However, one limitation of their study was that the MCI status was solely defined by the Montreal Cognitive Assessment (MoCA) score rather than a formal clinical evaluation, which is necessary to diagnose MCI (Albert et al., 2011).

In the present study, we further investigated the classification performance of MCI/AD vs. CN using behavioral data from standard neuropsychological tests, a cognitive task (spatial IOR), or both. Both MCI and AD patients were formally diagnosed by clinicians with the consensus guidelines (Albert et al., 2011; McKhann et al., 2011). A variety of machine learning algorithms were tested: four traditional machine learning models and a feed-forward artificial neural network (ANN) model, which has been widely used in AD research (Jo et al., 2019).