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**Our Science**The discrimination between

subjects with mild cognitive

impairment and normal controls

subjects with mild cognitive

impairment and normal controls

Miro Versions

v3.1.2.6 MiroMindv2.1.3.0 On-device speech recognition algorithms

v2.1.2.0 Natural language processing algorithms

v2.0 Machine learning algorithms

v3.0 Reference data set

One hundred forty four participants, comprised of 100 cognitively normal volunteers, and 44 participants with mild cognitive impairment (MCI), were included in an analysis of the ability of the Miro platform to distinguish participants with MCI from normal participants.

The participants with MCI were a heterogeneous group with varying levels of performance. Some were referred by community neuropsychologists to one of three study sites, and others were identified by researchers at Johns Hopkins University. “MCI” includes participants whose performance on the Telephone Interview of Cognitive Status (TICS) fall within the range of Mild Cognitive Impairment. Automated MCI Risk Score classification was developed with data from 100 Normal Control participants and the 44 participants with MCI. Both MCI and High Functioning MCI groups were analyzed according to the MCI Risk Score.

The participants with MCI were a heterogeneous group with varying levels of performance. Some were referred by community neuropsychologists to one of three study sites, and others were identified by researchers at Johns Hopkins University. “MCI” includes participants whose performance on the Telephone Interview of Cognitive Status (TICS) fall within the range of Mild Cognitive Impairment. Automated MCI Risk Score classification was developed with data from 100 Normal Control participants and the 44 participants with MCI. Both MCI and High Functioning MCI groups were analyzed according to the MCI Risk Score.

Table 1. Demographics

GROUP | F/M | MEAN AGE (RANGE) |
---|---|---|

Normal controls | 55/45 | 69.9 (36-82) |

MCI | 20/24 | 66.1 (35-92) |

Age and sex distributions of the normal and MCI participants used in the analysis of the ability

to distinguish these groups by their performances on assessments using the Miro platform.

to distinguish these groups by their performances on assessments using the Miro platform.

Methods

Standardized versions of basic variable scores were combined to form an MCI Risk Score. This score is designed to specifically distinguish performance of normal participants from the performance of participants with Mild Cognitive Impairment. Prior to combining variables into aggregate scores, each raw variable score was quantile-normalized to mitigate undue influence of outliers or peculiar distributions of any individual scores. Normalized variable scores were then standardized based on Miro assessment results collected from cognitively normal controls have standardized scores that are normally distributed with means set to zero and standard deviations set to one for among normal participants. For a minimal subset of Miro modules with non-equivalent versions, basic scores were normalized and standardized independently, per version. These included: 1. Picture Description, wherein each picture to be described produces a unique lexicon; Category Fluency, wherein each category to be explored produces an independent word list; and Letter Fluency, wherein each letter used to prompt word-production varies in difficulty and produces a distinct word-list length.

Missing values were imputed using low rank matrix completion [1]. If a participant participated in multiple assessments (as for test-retest reliability), only initial (T1) assessment results were included in the discrimination analysis. The process for combining normalized variables to form an MCI risk score is described below:

The risk score was developed using L1-, L2- regularized “elastic net” logistic regression [2] which is a modified version of logistic regression. In this application of L1-, L2- elastic net regression, the combination of normalized input variables were optimized based on the log-odds estimates of each individual’s performance pattern correlating with a predefined MCI performance pattern. The MCI performance pattern was defined by the MCI participant group mean and standard deviation per variable. The effect of the L1- penalty was to exclude input variables from the risk score if they were not particularly useful for inferring the odds of individuals being categorized as MCI. The effect of the L2- penalty was seen in situations where there are several highly correlated variables that each predict the odds of being categorized as MCI. With an L2- penalty, rather than picking a single input from the set of correlated input scores, a weighted combination was used — possibly smoothing out noise or measurement error. The L1- and L2- penalties were chosen by 5-fold cross-validation to optimize average out-of-sample prediction AUROC over 10 random partitions of the data. The selected penalties were an L1- penalty of (0.1)*(alpha) and an L2- penalty of (0.9)*(alpha) for alpha=0.047. With those penalties fixed, the penalized logistic regression model regression coefficients – or variable weights – were re-estimated 144 times, once with each participant left out of the data set. Then for each analysis on a reduced data set, the logistic regression model prediction on the log-odds scale was calculated for the left-out participant.

This prediction is a weighted combination of the participant’s scores for each of the variables used in the model, plus a constant “intercept term”. This prediction or weighted combination of scaled Miro variables is a new variable optimized for separation of the Normal and MCI groups. We call these new variables “risk-scores” because they indicate the risk of a participant having an MCI diagnosis. MCI Risk Scores were calculated for all 144 participants.

For calculation of MCI-vs-Normal risk scores for additional Miro users who are not members of the set of 144 participants used here in the development and evaluation of the risk scores, the elastic-net logistic regression model was trained using all 144 participants and the penalties selected earlier. The resulting recipe -- or set of set of Miro variables and their weights to use in calculating an MCI-vs-Normal risk score – is saved and used to calculate risk scores for all participants who complete a Miro assessment.

Missing values were imputed using low rank matrix completion [1]. If a participant participated in multiple assessments (as for test-retest reliability), only initial (T1) assessment results were included in the discrimination analysis. The process for combining normalized variables to form an MCI risk score is described below:

The risk score was developed using L1-, L2- regularized “elastic net” logistic regression [2] which is a modified version of logistic regression. In this application of L1-, L2- elastic net regression, the combination of normalized input variables were optimized based on the log-odds estimates of each individual’s performance pattern correlating with a predefined MCI performance pattern. The MCI performance pattern was defined by the MCI participant group mean and standard deviation per variable. The effect of the L1- penalty was to exclude input variables from the risk score if they were not particularly useful for inferring the odds of individuals being categorized as MCI. The effect of the L2- penalty was seen in situations where there are several highly correlated variables that each predict the odds of being categorized as MCI. With an L2- penalty, rather than picking a single input from the set of correlated input scores, a weighted combination was used — possibly smoothing out noise or measurement error. The L1- and L2- penalties were chosen by 5-fold cross-validation to optimize average out-of-sample prediction AUROC over 10 random partitions of the data. The selected penalties were an L1- penalty of (0.1)*(alpha) and an L2- penalty of (0.9)*(alpha) for alpha=0.047. With those penalties fixed, the penalized logistic regression model regression coefficients – or variable weights – were re-estimated 144 times, once with each participant left out of the data set. Then for each analysis on a reduced data set, the logistic regression model prediction on the log-odds scale was calculated for the left-out participant.

This prediction is a weighted combination of the participant’s scores for each of the variables used in the model, plus a constant “intercept term”. This prediction or weighted combination of scaled Miro variables is a new variable optimized for separation of the Normal and MCI groups. We call these new variables “risk-scores” because they indicate the risk of a participant having an MCI diagnosis. MCI Risk Scores were calculated for all 144 participants.

For calculation of MCI-vs-Normal risk scores for additional Miro users who are not members of the set of 144 participants used here in the development and evaluation of the risk scores, the elastic-net logistic regression model was trained using all 144 participants and the penalties selected earlier. The resulting recipe -- or set of set of Miro variables and their weights to use in calculating an MCI-vs-Normal risk score – is saved and used to calculate risk scores for all participants who complete a Miro assessment.

Results

The AUROC of the MCI Risk Score to separate normal participants from clinically impaired MCI participants is (0.94).

To evaluate the performance of the MCI-vs-Normal risk score the receiver operating characteristic curve was generated and the area under this curve was calculated. Each of the 144 participants used in this analysis has an MCI risk score and a verified MCI diagnostic status (MCI, or Normal (not-MCI) ). Participants may be classified as either MCI or Normal based on their risk scores, by choosing a threshold (say 0) and then labelling participants with risk scores greater than 0 as MCI and those with risk scores below 0 as Normal. Using this threshold, there are likely to be some mis-diagnoses: either false positives when Normal participants have scores greater than 0, or false negatives when MCI participants have scores below 0. From the counts of mis-diagnoses, we can calculate rates: a True Positive Rate (TPR or Sensitivity) as (difference of the counts of true MCI participants and false negatives, divided by the count of true MCI participants) and a False Positive Rate (FRP or 1-Specificity) as (count of false positives divided by count of actual Normal participants). These rates are then plotted for a range of values so that the Sensitivity ranges from 0 to 1 and plotted as TPR vs FPR. This curve illustrates the trade-off between Sensitivity and Specificity that is made for any particular selection of a threshold on the risk score that could be used to assign diagnoses.

The total area under this curve, the Area Under the Receiver Operating Characteristic curve or AUROC, is a general measure of how good a particular score is for separating groups without forcing the choice of a particular threshold and trade-off between sensitivity and specificity.

To evaluate the performance of the MCI-vs-Normal risk score the receiver operating characteristic curve was generated and the area under this curve was calculated. Each of the 144 participants used in this analysis has an MCI risk score and a verified MCI diagnostic status (MCI, or Normal (not-MCI) ). Participants may be classified as either MCI or Normal based on their risk scores, by choosing a threshold (say 0) and then labelling participants with risk scores greater than 0 as MCI and those with risk scores below 0 as Normal. Using this threshold, there are likely to be some mis-diagnoses: either false positives when Normal participants have scores greater than 0, or false negatives when MCI participants have scores below 0. From the counts of mis-diagnoses, we can calculate rates: a True Positive Rate (TPR or Sensitivity) as (difference of the counts of true MCI participants and false negatives, divided by the count of true MCI participants) and a False Positive Rate (FRP or 1-Specificity) as (count of false positives divided by count of actual Normal participants). These rates are then plotted for a range of values so that the Sensitivity ranges from 0 to 1 and plotted as TPR vs FPR. This curve illustrates the trade-off between Sensitivity and Specificity that is made for any particular selection of a threshold on the risk score that could be used to assign diagnoses.

The total area under this curve, the Area Under the Receiver Operating Characteristic curve or AUROC, is a general measure of how good a particular score is for separating groups without forcing the choice of a particular threshold and trade-off between sensitivity and specificity.

Figure 1. Receiver operating characteristic curve for Miro MCI-vs-Normal risk scores.

Discussion

This study shows notable improvement in the sensitivity and specificity of discriminating between healthy participants and those with mild cognitive impairment as compared to comparator clinical and research practices. Larger data sets will allow development of more sophisticated risk scores.

^{1 }Jian-Feng Cai, Emmanuel J. Candès, and Zuowei

Shen A Singular Value Thresholding Algorithm for

Matrix Completion. SIAM J. Optim., 20(4), 1956–1982.

(27 pages)

^{2}Jerome Friedman, Trevor Hastie and Rob

Tibshirani. (2008). Regularization Paths for

Generalized Linear Models via Coordinate Descent.

Journal of Statistical Software, Vol. 33(1), 1-22 Feb

2010.

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