Provide three acute behavioral measures.

README.md

@@ -1,32 +1,20 @@
 ### NiiVue brainchop
 
-Provided a lesion map and an acute [Center of Cancellation (CoC)](https://github.com/neurolabusc/Cancel) score, this web page will predict the chronic neglect severity. The provided lesion map should be normalized to standard space. This is an edge-based solution: the values are computed directly on the web browser, and are not shared with a cloud service. [Try the live demo](https://niivue.github.io/niivue-neglect/)
+Provided a lesion map the [live demo](https://niivue.github.io/niivue-neglect/) web page will predict the chronic neglect severity. The provided lesion map should be normalized to standard space. For optimal performance, acute behavioral data should include three behavioral measures: the [Center of Cancellation (CoC) letter score](https://github.com/neurolabusc/Cancel), the [Center of Cancellation (CoC) bells score](https://github.com/neurolabusc/Cancel) and a simple [Copy Task](https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft28076-000). However, the software will provide an estimate with degraded accuracy without these behavioral measures. This is an edge-based solution: the values are computed directly on the web browser, and are not shared with a cloud service. 
 
-### Optimal Usage
 
-One can create an accurate prediction of long term spatial neglect symptom severity by providing a lesion map showing the location and extent of the injury as well as the acute behavior (the CoC score).
 
- 1. Open the [live demo](https://niivue.github.io/niivue-neglect/).
- 2. Press the `Open Lesion Map` button and select the normalized lesion. You can normalize lesions using [the clinical toolbox for SPM](https://github.com/neurolabusc/Clinical).
- 3. You can visually inspect the lesion (shown as red) with respect to the grayscale atlas and the core prediction voxels (shown in blue).
- 4. Make sure the `Acute CoC Score` option is `checked`. 
- 5. Set the [Center of Cancellation (CoC)](https://github.com/neurolabusc/Cancel) score that was measured acutely.
- 6. Press the `Prediction` button to see the expected outcome score.
-
-![niivue-neglect user interface](acuteCoC.png)
-
-### Basic Usage
-
-One can create an rough prediction of long term spatial neglect symptom severity by providing a lesion map showing the location and extent of the injury. Note this method is less accurate than methods where acute behavior is known.
+### Usage
 
  1. Open the [live demo](https://niivue.github.io/niivue-neglect/).
  2. Press the `Open Lesion Map` button and select the normalized lesion. You can normalize lesions using [the clinical toolbox for SPM](https://github.com/neurolabusc/Clinical).
- 3. You can visually inspect the lesion (shown as red) with respect to the grayscale atlas and the extended prediction voxels (shown in translucent green).
- 4. Make sure the `Acute CoC Score` option is `unchecked`. Notice that the ability to set the symptom severity will be disabled.
- 6. Press the `Prediction` button to see the expected outcome score.
-
-![niivue-neglect user interface](NoCoC.png)
+ 3. You can visually inspect the lesion (shown as red) with respect to the grayscale atlas and the core prediction voxels (shown in blue).
+ 4. If known, enter the `letters CoC`: this [Center of Cancellation](https://github.com/neurolabusc/Cancel) value should be a number in the range of 0..1. If this value is unknown, make sure this field is empty.
+ 5. If known, enter the `bells CoC`: this [Center of Cancellation](https://github.com/neurolabusc/Cancel) value should be a number in the range of 0..1. If this value is unknown, make sure this field is empty.
+ 6. If known, enter the `copying score`: this [value](https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft28076-000) value should be a number in the range of 0..8. If this value is unknown, make sure this field is empty. 
+ 7. Press the `Prediction` button to see the expected outcome score.
 
+![niivue-neglect user interface](niivue-neglect.png)
 
 ### For Developers
 

index.html

@@ -11,9 +11,12 @@
 <body>
   <header>
     <button id="openBtn">Open Lesion Map</button>
-    <input type="checkbox" id="cocCheck" checked />
-    <label for="cocNumber">Acute CoC Score</label>
-    <input style="width: 7em" type="number" id="cocNumber" min="-1" value ="0.65" max="1"/>
+    <label for="cocLetters">letters CoC (-1..1)</label>
+    <input style="width: 7em" type="number" id="cocLetters" min="-1" value ="0.3" max="1"/>
+    <label for="cocBells">bells CoC (-1..1)</label>
+    <input style="width: 7em" type="number" id="cocBells" min="-1" value ="0.2" max="1"/>
+    <label for="copyScore">copying score (0..8)</label>
+    <input style="width: 7em" type="number" id="copyScore" min="0" value ="2" max="8"/>
     <button id="predictBtn">Prediction</button>
     <label for="maskSlider">Mask Opacity</label>
     <input

main.js

@@ -1,5 +1,16 @@
 import { Niivue } from '@niivue/niivue'
 async function main() {
+  function meanOmitNaN(values) {
+    // e.g. let mn = meanOmitNaN([NaN,2, 3])
+    let sum = 0
+    let n = 0
+    for (let i = 0; i < values.length; i++) {
+    if (isNaN(values[i])) continue
+      n ++
+      sum += values[i]
+    }
+    return sum/n
+  } // meanOmitNaN()
   async function addVolumeFromFiles(f) {
       await nv1.loadFromFile(f[0])
       nv1.setColormap(nv1.volumes[3].id, 'red')
@@ -103,11 +114,27 @@ async function main() {
     const chronCoC = chronZ * 0.0216 + 0.00803
     return [lesionVolTotalML, ROI_volML, chronCoC, chronZ]
   } // neglect_predict_noCoC()
-  function neglect_predict() {
+  function meanOmitNaN(values) {
+    // e.g. let mn = meanOmitNaN([NaN,2, 3])
+    let sum = 0
+    let n = 0
+    for (let i = 0; i < values.length; i++) {
+    if (isNaN(values[i])) continue
+      n ++
+      sum += values[i]
+    }
+    return sum/n
+  } // meanOmitNaN()
+  function neglect_predict(acuteLetters, acuteBells, acuteCopy) {
     if (nv1.volumes.length !== 4) {
       window.alert('Please reload the page and open lesions with the "Open Lesion Map" button')
       return
     }
+    let acuteCoC = meanOmitNaN([acuteLetters, acuteBells])
+    let acuteLettersZ = (acuteLetters - 0.00686)/0.0179 // mean/SD of controls
+    let acuteBellsZ = (acuteBells - 0.0092)/0.0253 // mean/SD of controls
+    let acuteCopyZ = (acuteCopy - 0.23333)/0.43018 // mean/SD of controls
+    let acuteZ = meanOmitNaN([acuteLettersZ, acuteBellsZ, acuteCopyZ])
     //volumes: 0=T1, 1=MaskNoCoC, 2=Mask, 3=lesion
     let mask = nv1.volumes[2].img
     let lesion = nv1.volumes[3].img
@@ -153,7 +180,6 @@ async function main() {
     for (let p = 0; p < nVoxPCA; p++) {
         PC[p] = norm0to1(PC[p], -51.9073, 110.0535)
     }
-    let acuteCoC = parseFloat(cocNumber.value)
     let acuteCoC0_1 = norm0to1(acuteCoC, -0.024243014, 0.951938077)
     let lesionVolTotalML = lesionVolTotal / 1000
     let ROI_volML = lesionVol / 1000
@@ -194,8 +220,6 @@ async function main() {
     }
     const prediction_mean = prediction_sum / models.length
     const diffZ = prediction_mean * (38.72560594 + 1.211389735) - 1.211389735
-    // calculate acute z-score based on user input CoC
-    const acuteZ = (acuteCoC - 0.00803)/0.0216 // mean/SD of controls
     const chronZ = acuteZ-diffZ
     const chronCoC = chronZ * 0.0216 + 0.00803
     return [acuteCoC, acuteZ, lesionVolTotalML, ROI_volML, chronCoC, chronZ]
@@ -213,18 +237,19 @@ async function main() {
     input.click()
   }
   predictBtn.onclick = function () {
-    if (cocCheck.checked) {
-      const [acuteCoC, acuteZ, lesionVolTotalML, ROI_volML, chronCoC, chronZ] = neglect_predict()
-      const str = (`Given ${lesionVolTotalML.toFixed(4)}ml lesion (with ${ROI_volML.toFixed(4)} in core neglect voxels), and acute CoC ${acuteCoC.toFixed(2)}  (z= ${acuteZ.toFixed(4)}), predicted chronic CoC is ${chronCoC.toFixed(2)} (z= ${chronZ.toFixed(4)})`)
-      window.alert(str)
-    } else {
+    let acuteLetters = parseFloat(cocLetters.value)
+    let acuteBells = parseFloat(cocBells.value)
+    let acuteCopy = parseFloat(copyScore.value)
+    let acuteCoCNaN = isNaN(acuteLetters) && isNaN(acuteBells)
+    if (acuteCoCNaN) {
       const [lesionVolTotalML, ROI_volML, chronCoC, chronZ] = neglect_predict_noCoC()
       const str = (`Given ${lesionVolTotalML.toFixed(4)}ml lesion (with ${ROI_volML.toFixed(4)} in core neglect voxels), predicted chronic CoC is ${chronCoC.toFixed(4)} (z= ${chronZ.toFixed(4)})`)
       window.alert(str)
-    }
-  }
-  cocCheck.onchange = function () {
-    cocNumber.disabled = !this.checked
+    } else {
+      const [acuteCoC, acuteZ, lesionVolTotalML, ROI_volML, chronCoC, chronZ] = neglect_predict(acuteLetters, acuteBells, acuteCopy)
+      const str = (`Given ${lesionVolTotalML.toFixed(4)}ml lesion (with ${ROI_volML.toFixed(4)} in core neglect voxels), and acute CoC ${acuteCoC.toFixed(2)}  (z= ${acuteZ.toFixed(4)}), predicted chronic CoC is ${chronCoC.toFixed(4)} (z= ${chronZ.toFixed(4)})`)
+      window.alert(str)
+    } 
   }
   function handleLocationChange(data) {
     document.getElementById("intensity").innerHTML = data.string

public/neglect_predict.m

@@ -3,7 +3,7 @@ function neglect_predict(fnm, acuteCoCl, acuteCoCb, acuteCop)
 % Assumes lesion map is normalized to 181x217x181 using SPM
 %Examples
 % neglect_predict; %use GUI;
-% neglect_predict('example_lesion.nii.gz', 0.87, 0.81, 2)
+% neglect_predict('M2095_lesion.nii.gz', 0.87, 0.81, 2)
     if nargin < 1
         [p_file, p_path] = uigetfile('*.nii.gz;*.nii', 'Select lesion map');
         if p_file==0