Exploring longitudinal relationships among Alzheimer's disease biomarkers.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches

[1] § METHODS › Plasma biomarker assays ↔ Movie_Data_code/app.r, lines 1–66 · score 0.76 · PrecivityAD2, LucentAD, NeuroToolKit, ALZpath, Plex, kits
[2] § METHODS › Plasma biomarker assays ↔ Raindrop_Plots_Code/Needed_Functions.R, lines 1–70 · score 0.65 · LucentAD, NeuroToolKit, ALZpath, Plex, kits, tau181

Paper

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The authors' code

R · 568 lines · 23 KB · MIT · 1 match

#raindrops actual
# tidyverse in particular is incredibly important for graphing and filtering.
list.of.packages <- c("tidyverse", "data.table","shiny","RColorBrewer","ggforce","ggbreak","av","polished","shinyjs")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(colourpicker)
library(shiny)
library(ggplot2)
library(data.table)
library(RColorBrewer)
library(ggforce)
library(av)
library(shinyjs)
library(polished)
source("Needed_Functions.R")
##Replace APS with Tipping Point Probability ## Not
##X default - Centiloid
##Y default - % pt217
##Color default - CDR_SB
#(optional legend)
scramble_key <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,21,16,17,18,19,20)
step_size_list <- c(0.1, #C2N Plasma Ptau217 ratio
0.2, #C2N Plasma Ptau217 conc
0.005, #C2N Plasma Ab4240
0.001, #Fujirebio Ptau217
0.001, #Fujirebio Ab4240
0.005, #Alzpath Ptau217
0.005, #Janssen Ptau217
0.01, #Roche Ptau181
0.005, #Roche Ab4240
0.001, #Roche GFAP
0.05, #Roche NfL
0.01, #Quanterix Ptau 181
1, #Quanterix GFAP
0.005, #Quanterix Ab4240
0.01, #Quanterix NfL
1,#Centiloid
0.01,#Tau PET Early
0.01,#Tau PET Late
0.001,#Cortical Thickness Meta ROI
0.5, #CDR Sum of Boxes
1) #CSF ab42/ptau181
graph_choices_ref <- c(paste0("C2N PrecivityAD2 ","plasma %p-tau217 (%)"),
paste0("C2N PrecivityAD2 ","plasma p-tau217 (pg/mL)"),
paste0("C2N PrecivityAD2 ","plasma A","\U03B2","42/A","\U03B2","40"),
paste0("Fujirebio Lumipulse ","plasma p-tau217 (pg/mL)"),
paste0("Fujirebio Lumipulse ","plasma A","\U03B2","42/A","\U03B2","40"),
paste0("ALZpath Quanterix ","plasma p-tau217 (pg/mL)"),
paste0("Janssen LucentAD Quanterix ","plasma p-tau217 (pg/mL)"),
paste0("Roche NeuroToolKit ","plasma p-tau181 (pg/mL)"),
paste0("Roche NeuroToolKit ","plasma A","\U03B2","42/A","\U03B2","40"),
paste0("Roche NeuroToolKit ","plasma GFAP (ng/mL)"),
paste0("Roche NeuroToolKit ","plasma NfL (pg/mL)"),
paste0("Quanterix Neurology 4-Plex ","plasma p-tau181 (pg/ml)"),
paste0("Quanterix Neurology 4-Plex ","plasma GFAP (pg/mL)"),
paste0("Quanterix Neurology 4-Plex ","plasma A","\U03B2","42/A","\U03B2","40"),
paste0("Quanterix Neurology 4-Plex ","plasma NfL (pg/mL)"),
"Amyloid PET Centiloid",
"Tau PET Mesial-Temporal (Early)",
"Tau PET Temporo-Parietal (Late)",
"Cortical Thickness (Meta ROI)",
"Clinical Dementia Rating Sum of Boxes",
paste0("Roche Elecsys ","CSF A","\U03B2","42/p-tau181"))
graph_limits_list <- list(c(0,25), #C2N Plasma Ptau217 ratio
c(0,20), #C2N Plasma Ptau217 conc
c(0,0.15), #C2N Plasma Ab4240
c(0,1.8), #Fujirebio Ptau217
c(0,0.3), #Fujirebio Ab4240
c(0,3), #Alzpath Ptau217
c(0,0.35), #Janssen Ptau217
c(0,4.7), #Roche Ptau181
c(0,0.23), #Roche Ab4240
c(0,0.46), #Roche GFAP
c(0,16), #Roche NfL
c(0,80), #Quanterix Ptau 181
c(0,565), #Quanterix GFAP
c(0,0.125), #Quanterix Ab4240
c(0,80), #Quanterix NfL
c(-40,190),#Centiloid
c(0.85,2),#Tau PET Early
c(0.9,2.5),#Tau PET Late
c(1.8,3.25),#Cortical Thickness Meta ROI
c(-0.5,10),#CDR Sum of Boxes
c(5,185) #CSF ab42/ptau181
)
group_threshold_defaults <- c("1.5, 2.5, 4.1, 8",#C2N Plasma Ptau217 ratio
"3, 7, 13, 16", #C2N Plasma Ptau217 conc
"0.04, 0.06, 0.0924, 0.1", #C2N Plasma Ab4240
"0.06, 0.1, 0.158, 0.5", #Fujirebio Ptau217
"0.04, 0.06, 0.0869, 1.5", #Fujirebio Ab4240
"0.2,0.3,0.444,0.5", #Alzpath Ptau217
"0.04,0.05,0.0615,0.085", #Janssen Ptau217
"0.65,1.0,1.14,1.3", #Roche Ptau181
"0.11,0.12,0.126,0.14", #Roche Ab4240
"0.0661, 0.0916, 0.113, 0.1230", #Roche GFAP
"2.650, 3.270, 4.29, 7", #Roche NfL
"12.1, 17.8, 20.5, 30", #Quanterix Ptau 181
"89, 128, 205, 250", #Quanterix GFAP
"0.045, 0.053, 0.0582, 0.070", #Quanterix Ab4240
"13.27, 17.57, 21.7, 28", #Quanterix NfL
"-5.00, 7.00, 20, 39",#Centiloid
"1.09, 1.15, 1.328, 1.4",#Tau PET Early
"1.05, 1.12, 1.269, 1.31",#Tau PET Late
"2.5, 2.572, 2.932, 3",#Cortical Thickness Meta ROI
"0.5, 1, 2, 4",#CDR Sum of Boxes
"10,25,60,91") #CSF ab42/ptau181
rank_thresholds <- c(
236, #C2N Plasma Ptau217 ratio
254, #C2N Plasma Ptau217 conc
186, #C2N Plasma Ab4240
241, #Fujirebio Ptau217
160, #Fujirebio Ab4240
250, #Alzpath Ptau217
252, #Janssen Ptau217
237, #Roche Ptau181
209, #Roche Ab4240
250, #Roche GFAP
269, #Roche NfL
203, #Quanterix Ptau 181
260, #Quanterix GFAP
130, #Quanterix Ab4240
217, #Quanterix NfL
233, #Centiloid
103, #Tau PET Early
114, #Tau PET Late
26, #Cortical Thickness Meta ROI
266, #CDR Sum of Boxes
53 #CSF ab42/ptau181
)
# Default threshold lines for each variable
value_thresholds <- c(
4.06, #C2N Plasma Ptau217 ratio
2.34, #C2N Plasma Ptau217 conc
0.0924, #C2N Plasma Ab4240
0.158, #Fujirebio Ptau217
0.0869, #Fujirebio Ab4240
0.444, #Alzpath Ptau217
0.0615, #Janssen Ptau217
1.14, #Roche Ptau181
0.126, #Roche Ab4240
0.113, #Roche GFAP
4.29, #Roche NfL
20.5, #Quanterix Ptau 181
205, #Quanterix GFAP
0.0582, #Quanterix Ab4240
21.7, #Quanterix NfL
20, #Centiloid
1.328, #Tau PET Early
1.269, #Tau PET Late
2.572, #Cortical Thickness Meta ROI
1, #CDR Sum of Boxes
25 #CSF ab42/ptau181
)
graph_choices_suffixes <- c("C2N_plasma_ptau217_ratio","C2N_plasma_ptau217", "C2N_plasma_Abeta42_Abeta40",
"Fuji_plasma_ptau217","Fuji_plasma_Ab42_Ab40",
"AlzPath_plasma_ptau217","Janssen_plasma_ptau217","Roche_plasma_ptau181","Roche_plasma_Ab42_Ab40",
"Roche_plasma_GFAP", "Roche_plasma_NfL",
"QX_plasma_ptau181","QX_plasma_GFAP","QX_plasma_Ab42_Ab40","QX_plasma_NfL",
"CENTILOIDS","MesialTemporal","TemporoParietal","metaROI","CDSOB","AB42_PTAU181")
high_risk_high <- graph_choices_ref[c(1,2,4,6,7,8,10,11,12,13,15,16,17,18,20)]
high_risk_low <- graph_choices_ref[c(3,5,9,14,19,21)]
ui <- shinyUI(fluidPage(
useShinyjs(),
tags$head(tags$style(".shiny-notification {
position: fixed;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
width: 600px;
height: 200px;
font-size: 24px;
text-align: center;
opacity: 0.95;
z-index: 1000;
}")),
# Hidden inputs - these exist for the code but aren't displayed
shinyjs::hidden(
selectInput("graphtype_anim", "Type:", choices=c("Raindrop","Timetrails"), selected="Timetrails"),
selectInput("y_var", "Y:", choices=graph_choices_ref[scramble_key], selected=graph_choices_ref[1]),
selectInput("x_var", "X:", choices=graph_choices_ref[scramble_key], selected=graph_choices_ref[16]),
selectInput("group_xy_check", "Group:", choices=c("Longitudinal","Baseline"), selected="Baseline"),
selectInput("other_type", "Other:", choices=graph_choices_ref[scramble_key], selected=graph_choices_ref[1]),
selectInput("otherb_type", "OtherB:", choices=graph_choices_ref[scramble_key], selected=graph_choices_ref[1]),
checkboxInput("origin_plot", "Origin", value=FALSE),
sliderInput("cutoff1", "C1", min=0, max=20, step=0.2, value=0),
sliderInput("cutoff2", "C2", min=0, max=20, step=0.2, value=0),
sliderInput("cutoff3", "C3", min=0, max=20, step=0.2, value=0),
sliderInput("cutoff4", "C4", min=0, max=20, step=0.2, value=0),
numericInput("th1", "Th1", value=0.0673, step=0.0005),
numericInput("th2_rank", "Th2 Rank", value=90),
numericInput("th2_trail", "Th2 Trail", value=0.158, step=0.0005),
actionButton("reset_thresholds", "Reset"),
downloadButton("downloadData", "Download")
),
# Empty white screen
tags$div(style = "background-color: white; width: 100%; height: 100vh;")
))
server <- function(input, output, session) {
load("ADNI_Raindrop_Data_Test_v3.rdata")
# Auto-trigger download when app loads with URL parameters
observe({
query <- parseQueryString(session$clientData$url_search)
# Only auto-trigger if coming from another app with URL parameters
if(length(query) > 0){
delay(1000, click("downloadData")) # Wait 1 second then trigger download
}
})
# Parse URL parameters and update hidden inputs
observe({
query <- parseQueryString(session$clientData$url_search)
if(!is.null(query[['graphtype']])){
graph_variable_id <- query[['graphtype']]
updateSelectInput(session, "graphtype_anim", selected = graph_variable_id)
}
if(!is.null(query[['xvar']])){
x_variable_id <- graph_choices_ref[which(graph_choices_suffixes==query[['xvar']])]
updateSelectInput(session, "x_var", selected = x_variable_id)
}
if(!is.null(query[['yvar']])){
y_variable_id <- graph_choices_ref[which(graph_choices_suffixes==query[['yvar']])]
updateSelectInput(session, "y_var", selected = y_variable_id)
}
if(!is.null(query[['gxyc']])){
gxyc <- query[['gxyc']]
updateSelectInput(session, "group_xy_check", selected = gxyc)
}
if(!is.null(query[['oty']])){
oty_variable_id <- graph_choices_ref[which(graph_choices_suffixes==query[['oty']])]
updateSelectInput(session, "other_type", selected = oty_variable_id)
}
if(!is.null(query[['otyb']])){
otyb_variable_id <- graph_choices_ref[which(graph_choices_suffixes==query[['otyb']])]
updateSelectInput(session, "otherb_type", selected = otyb_variable_id)
}
if(!is.null(query[['co1']])){
co1 <- as.numeric(query[['co1']])
updateSliderInput(session, "cutoff1", value = co1)
}
if(!is.null(query[['co2']])){
co2 <- as.numeric(query[['co2']])
updateSliderInput(session, "cutoff2", value = co2)
}
if(!is.null(query[['co3']])){
co3 <- as.numeric(query[['co3']])
updateSliderInput(session, "cutoff3", value = co3)
}
if(!is.null(query[['co4']])){
co4 <- as.numeric(query[['co4']])
updateSliderInput(session, "cutoff4", value = co4)
}
if(!is.null(query[['th1']])){
th1 <- as.numeric(query[['th1']])
updateNumericInput(session,'th1', value=th1)
}
if(!is.null(query[['th2']])){
th2 <- as.numeric(query[['th2']])
updateNumericInput(session,'th2_rank', value=th2)
updateNumericInput(session,'th2_trail', value=th2)
}
if(!is.null(query[['ogn']])){
ogn <- query[['ogn']]
ogn <- ogn =="TRUE"
updateCheckboxInput(session,'origin_plot',value = ogn)
}
})
output$secondSelection <- renderUI({
selectInput("other", "Grouping Variable:", choices = graph_choices_ref[scramble_key])
})
# Reactive expressions
variableids_for_plot <- reactive({
if(input$graphtype_anim=="Raindrop"){
graph_choice_y <- graph_choices_suffixes[which(graph_choices_ref==input$y_var)]
graph_choice_BL <- graph_choices_suffixes[which(graph_choices_ref==input$x_var)]
graph_vars_list <- c(paste0("N_",graph_choice_BL),
paste0("BL_EST_",graph_choice_BL),
paste0("EST_",graph_choice_y))
}else{
graph_choice_y <- graph_choices_suffixes[which(graph_choices_ref==input$y_var)]
graph_choice_x <- graph_choices_suffixes[which(graph_choices_ref==input$x_var)]
graph_vars_list <- c(paste0("EST_",graph_choice_x),
paste0("EST_",graph_choice_y))
}
graph_vars_list
})
param_change <- reactive({
list(input$graphtype_anim,input$cutoff1,input$cutoff2,input$cutoff3,input$cutoff4,
input$y_var, input$x_var, input$group_xy_check,
input$other_type, input$otherb_type)
})
update_origin_point <- reactive({input$origin_plot})
cutoff_change <- reactive({
list(input$group_xy_check, input$other_type, input$otherb_type, input$reset_thresholds)
})
c1_change <- reactive({list(input$cutoff1)})
c2_change <- reactive({list(input$cutoff2)})
c3_change <- reactive({list(input$cutoff3)})
observeEvent(c1_change(),{
min2 <- input$cutoff1
updateSliderInput(session, "cutoff2", min = min2)
})
observeEvent(c2_change(),{
min3 <- input$cutoff2
updateSliderInput(session, "cutoff3", min = min3)
})
observeEvent(c3_change(),{
min4 <- input$cutoff3
updateSliderInput(session, "cutoff4", min = min4)
})
observeEvent(cutoff_change(),{
req(input$group_xy_check)
if(input$group_xy_check=="Longitudinal"){
req(input$other_type)
var <- input$other_type
} else {
req(input$otherb_type)
var <- input$otherb_type
}
group_thresholds <- extract_comma_sep(group_threshold_defaults[which(graph_choices_ref==var)])
group_choice <- graph_choices_suffixes[which(graph_choices_ref==var)]
group_var <- paste0("EST_",group_choice)
var_step <- step_size_list[which(graph_choices_ref==var)]
dec_count <- decimalplaces(var_step)
max_value_slider <- paste(signif(max(data_10th_demo[,group_var],na.rm = T),dec_count))
updateSliderInput(session, "cutoff1",value=group_thresholds[1],
min = floor(min(data_10th_demo[,group_var],na.rm = T)),
max = max_value_slider,
step = var_step)
updateSliderInput(session, "cutoff2",value=group_thresholds[2],
min = floor(min(data_10th_demo[,group_var],na.rm = T)),
max = max_value_slider,
step = var_step)
updateSliderInput(session, "cutoff3",value=group_thresholds[3],
min = floor(min(data_10th_demo[,group_var],na.rm = T)),
max = max_value_slider,
step = var_step)
updateSliderInput(session, "cutoff4",value=group_thresholds[4],
min = floor(min(data_10th_demo[,group_var],na.rm = T)),
max = max_value_slider,
step = var_step)
query <- parseQueryString(session$clientData$url_search)
if(!is.null(query[['co1']])){
co1 <- as.numeric(query[['co1']])
updateSliderInput(session, "cutoff1", value = co1)
}
if(!is.null(query[['co2']])){
co2 <- as.numeric(query[['co2']])
updateSliderInput(session, "cutoff2", value = co2)
}
if(!is.null(query[['co3']])){
co3 <- as.numeric(query[['co3']])
updateSliderInput(session, "cutoff3", value = co3)
}
if(!is.null(query[['co4']])){
co4 <- as.numeric(query[['co4']])
updateSliderInput(session, "cutoff4", value = co4)
}
})
group_cutoffs <- reactive({
paste(input$cutoff1,input$cutoff2,input$cutoff3,input$cutoff4,sep=",")
})
data_cat <- eventReactive(param_change(),{
variables_for_plot <- variableids_for_plot()
group_breaks <- group_cutoffs()
group_thresholds <- extract_comma_sep(group_breaks)
if(input$group_xy_check=="Longitudinal"){
graph_choice <- graph_choices_suffixes[which(graph_choices_ref==input$other_type)]
splitting_variable <- paste0("EST_",graph_choice)
if(input$other_type %in% high_risk_high){
create_groups_EST(data_10th_demo,splitting_variable,group_thresholds)
} else if (input$other_type %in% high_risk_low) {
create_groups_ratio_EST(data_10th_demo,splitting_variable,group_thresholds)
}
} else if(input$group_xy_check=="Baseline"){
graph_choice <- graph_choices_suffixes[which(graph_choices_ref==input$otherb_type)]
splitting_variable <- paste0("BL_EST_",graph_choice)
if(input$otherb_type %in% high_risk_high){
create_groups_EST(data_10th_demo,splitting_variable,group_thresholds)
} else {
create_groups_ratio_EST(data_10th_demo,splitting_variable,group_thresholds)
}
}
})
# Download Handler - at top level, not inside observe()
output$downloadData <- downloadHandler(
filename = function() {
xvar_filename <- graph_choices_suffixes[which(graph_choices_ref==input$x_var)]
yvar_filename <- graph_choices_suffixes[which(graph_choices_ref==input$y_var)]
if(input$graphtype_anim=="Raindrop"){
paste0("out_raindrop_",yvar_filename,"_vs_",xvar_filename,".mp4")
} else {
paste0("out_timetrails_",yvar_filename,"_vs_",xvar_filename,".mp4")
}
},
content = function(file) {
source("Needed_Functions.R")
in.csv.graphdata <- data_cat()
variables_for_plot <- variableids_for_plot()
in.csv.graphdata <- in.csv.graphdata[which(complete.cases(in.csv.graphdata[,variables_for_plot])),]
if(input$graphtype_anim=="Raindrop"){
in.csv.graphdata[,variables_for_plot[1]] <- as.numeric(factor(in.csv.graphdata[,variables_for_plot[1]]))
limits_for_plot <- graph_limits_list[[which(graph_choices_ref==input$y_var)]]
withProgress(message = 'Video being created',
detail = 'Please wait approximately 1 minute. Video will download automatically when complete.',
value = 0, {
av_capture_graphics(create_raindrop_plot_movie(data = in.csv.graphdata,
variablelist = variables_for_plot,
ylimits=limits_for_plot,
graph.xlab = input$x_var,
graph.ylab = input$y_var,
th1=input$th1,
th2=input$th2_rank,
originpoints=FALSE),
output = file,
res=100,
width=1250,
height=750,
framerate = 5)
}
)
showNotification(
"Video completed and downloading!",
duration = 3,
type = "message"
)
} else if(input$graphtype_anim=="Timetrails"){
xlimits_for_plot <- graph_limits_list[[which(graph_choices_ref==input$x_var)]]
limits_for_plot <- graph_limits_list[[which(graph_choices_ref==input$y_var)]]
withProgress(message = 'Video being created',
detail = 'Please wait approximately 1 minute. Video will download automatically when complete.',
value = 0, {
av_capture_graphics(create_timepath_plot_video(data = in.csv.graphdata,
variablelist = variables_for_plot,
xlimits=xlimits_for_plot,
ylimits=limits_for_plot,
graph.xlab = input$x_var,
graph.ylab = input$y_var,
th1=input$th1,
th2=input$th2_trail),
output = file,
res=100,
width=1250,
height=750,
framerate = 5)
}
)
showNotification(
"Video completed and downloading!",
duration = 3,
type = "message"
)
}
},
contentType = "video/mp4"
)
outputOptions(output, "downloadData", suspendWhenHidden = FALSE)
update_group_long <- reactive({
list(input$group_xy_check, input$other_type, input$otherb_type)
})
# Update threshold values when x_var changes
observeEvent(input$x_var,{
x = input$x_var
query <- parseQueryString(session$clientData$url_search)
updated_threshold_th2 <- rank_thresholds[which(graph_choices_ref==x)]
updateNumericInput(session,'th2_rank', value=updated_threshold_th2)
if(!is.null(query[['th2']])){
th2 <- as.numeric(query[['th2']])
updateNumericInput(session,'th2_rank', value=th2)
}
updated_threshold_th2 <- value_thresholds[which(graph_choices_ref==x)]
updateNumericInput(session,'th2_trail', value=updated_threshold_th2)
if(!is.null(query[['th2']])){
th2 <- as.numeric(query[['th2']])
updateNumericInput(session,'th2_trail', value=th2)
}
})
# Update threshold values when y_var changes
observeEvent(input$y_var,{
y = input$y_var
query <- parseQueryString(session$clientData$url_search)
updated_threshold_th1 <- value_thresholds[which(graph_choices_ref==y)]
updateNumericInput(session,'th1', value=updated_threshold_th1)
if(!is.null(query[['th1']])){
th1 <- as.numeric(query[['th1']])
updateNumericInput(session,'th1', value=th1)
}
})
}
shinyApp(ui, server)

app.r at commit eea99a6, under MIT · at the source

Overview

Authors: Benjamin Saef¹, Kellen K. Petersen¹, Katherine Volluz¹, Yan Li¹, Duygu Tosun^2,3, Marta Mila‐Aloma^2,3, Leslie M. Shaw⁴, Henrik Zetterberg^5,6,7,8,9,10, Jeffrey L. Dage^11,12, Carrie E. Rubel¹³, Kyle Ferber¹³, Lei Du‐Cuny¹⁴, Janaky Coomaraswamy¹⁵, Michael Baratta¹⁵, Yulia Mordashova¹⁴, Ziad S. Saad¹⁶, Gallen Triana‐Baltzer¹⁶, Nicholas J. Ashton^5,17,18, Emily A. Meyers¹⁹, Erin G. Rosenbaugh²⁰

and 6 other authors

J. Martin Sabandal²⁰, Anthony W. Bannon²¹, William Z. Potter²², Alzheimer's Disease Neuroimaging Initiative (ADNI), Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium Plasma Aβ and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer's Disease Project Team, Suzanne E. Schindler¹

22 affiliations

Department of Neurology Washington University in St. Louis St. Louis Missouri USA
Northern California Institute for Research and Education San Francisco California USA
Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco California USA
Department of Pathology and Laboratory Medicine University of Pennsylvania Philadelphia Pennsylvania USA
Institute of Neuroscience and Physiology Department of Psychiatry and Neurochemistry The Sahlgrenska Academy at University of Gothenburg Mölndal Sweden
Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden
UK Dementia Research Institute Fluid Biomarkers Laboratory UK DRI at UCL London UK
Department of Neurodegenerative Disease UCL Queen Square Institute of Neurology London UK
Hong Kong Center for Neurodegenerative Diseases Clear Water Bay Hong Kong China
Wisconsin Alzheimer's Disease Research Center University of Wisconsin School of Medicine and Public Health University of Wisconsin–Madison Madison Wisconsin USA
Department of Neurology Indiana University School of Medicine Indianapolis Indiana USA
Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis Indiana USA
Biogen Cambridge Massachusetts USA
AbbVie Deutschland GmbH & Co. KG Ludwigshafen am Rhein Rheinland‐Pfalz Germany
Takeda Pharmaceutical Company Ltd. Cambridge Massachusetts USA
Precision Measures Johnson & Johnson San Diego California USA
Banner Alzheimer's Institute Phoenix Arizona USA
Banner Sun Health Research Institute Sun City Arizona USA
Alzheimer's Association Chicago Illinois USA
The Foundation for the National Institutes of Health North Bethesda Maryland USA
AbbVie North Chicago Illinois USA
Highly qualified expert Philadelphia Pennsylvania USA

Institutions: Washington University in St. Louis (United States); University of California, San Francisco (United States); Northern California Institute for Research and Education (United States); University of Pennsylvania (United States); Sahlgrenska University Hospital (Sweden); UK Dementia Research Institute (United Kingdom); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); University of Gothenburg (Sweden); Indiana University Indianapolis (United States); Indiana University School of Medicine; Biogen (United States) (United States); AbbVie (Germany) (Germany); Johnson & Johnson (United States) (United States); Banner Sun Health Research Institute (United States); Banner Alzheimer’s Institute; Alzheimer's Association (United States); Foundation for the National Institutes of Health (United States); AbbVie (United States) (United States)

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 8, article e71711

Dates: received 29 January 2026; accepted 9 June 2026; published online 31 July 2026; in print August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/alz.71711 · PMID 42535261 · PMCID PMC13425622 · OpenAlex W7172033668

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: other (modality), PET / SPECT (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)

Methods: Statistics, Connectivity, fMRI & imaging, Preprocessing

Keywords: Alzheimer's disease, blood biomarkers, data visualization, neuroimaging, plasma biomarkers, R shiny app

MeSH: Alzheimer Disease*, Amyloid beta-Peptides*, Biomarkers*, tau Proteins*, Aged, Brain, Female, Humans, Longitudinal Studies, Male, Peptide Fragments, Positron-Emission Tomography (* major topic)

Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex

Funding: Foundation for the National Institutes of Health; U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01AG070941 (S.E.S.)); AbbVie Inc., Alzheimer's Association, Diagnostics Accelerator at the Alzheimer's Drug Discovery Foundation, Biogen, Janssen Research & Development, LLC, and Takeda Pharmaceutical Company Limited

Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

INTRODUCTION: While most studies of Alzheimer's disease (AD) examine cross‐sectional relationships among biomarkers, longitudinal relationships are also highly relevant.

METHODS: This study in the Alzheimer's Disease Neuroimaging Initiative cohort (n = 373) used non‐parametric Spearman correlations to explore the relationships of baseline values and rates of change in plasma biomarkers and rates of change in key AD outcomes.

RESULTS: Compared to rates of change of plasma biomarkers, baseline values of plasma biomarkers were more strongly associated with rates of change in key AD outcomes. Change in amyloid positron emission tomography (PET) was most strongly associated with baseline values of amyloid beta (Aβ)42/Aβ40 and phosphorylated tau (p‐tau)217, especially in amyloid PET–‐negative individuals. Changes in cortical thickness and measures of cognition were most strongly associated with baseline p‐tau217, especially in amyloid PET–positive individuals.

DISCUSSION: Baseline p‐tau217 is associated with rates of change of amyloid pathology and cognition. Visualization tools were developed to enable researchers to explore AD biomarker relationships.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

WashUFluidBiomarkers/Dynamic-Visualization

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: eea99a6334b8ff76a8dfc5a0670a7c979fece5be, 6 August 2026

Languages: R (7)

Size: 28 files, 7 scripts

Software Heritage: not archived

Found in: the text, “Generation of the R Shiny apps and related datas”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: data.table (6 files), ggplot2 (6 files), tidyverse (4 files), Plotly (3 files)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

9 files

Movie_Data_code/Needed_Functions.R, R, 591 lines
Movie_Data_code/app.r, R, 568 lines, 1 match
Preprocessing_Code/prep_code.R, R, 430 lines
Raindrop_Plots_Code/Needed_Functions.R, R, 600 lines, 1 match
Raindrop_Plots_Code/app.r, R, 736 lines
Time_Trails_Code/Needed_Functions.R, R, 575 lines
Time_Trails_Code/app.r, R, 745 lines
LICENSE, License, 21 lines
README.md, Text, 193 lines

At the source: github.com/WashUFluidBiomarkers/Dynamic-Visualization

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
7 scripts, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 26 authors, 6 keywords, 12 MeSH terms, 3 funders, 51 references.

Cite

This paper

Saef, B., Petersen, K. K., Volluz, K., Li, Y., Tosun, D., Mila‐Aloma, M., Shaw, L. M., Zetterberg, H., Dage, J. L., Rubel, C. E., Ferber, K., Du‐Cuny, L., Coomaraswamy, J., Baratta, M., Mordashova, Y., Saad, Z. S., Triana‐Baltzer, G., Ashton, N. J., Meyers, E. A., . . . Schindler, S. E. (2026). Exploring longitudinal relationships among Alzheimer's disease biomarkers. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(8), e71711. https://doi.org/10.1002/alz.71711

BibTeX

@article{saef2026exploring, author = {Saef, Benjamin and Petersen, Kellen K. and Volluz, Katherine and Li, Yan and Tosun, Duygu and Mila‐Aloma, Marta and Shaw, Leslie M. and Zetterberg, Henrik and Dage, Jeffrey L. and Rubel, Carrie E. and Ferber, Kyle and Du‐Cuny, Lei and Coomaraswamy, Janaky and Baratta, Michael and Mordashova, Yulia and Saad, Ziad S. and Triana‐Baltzer, Gallen and Ashton, Nicholas J. and Meyers, Emily A. and Rosenbaugh, Erin G. and Sabandal, J. Martin and Bannon, Anthony W. and Potter, William Z. and {Alzheimer's Disease Neuroimaging Initiative (ADNI)} and {Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium Plasma Aβ and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer's Disease Project Team} and Schindler, Suzanne E.}, title = {{Exploring longitudinal relationships among Alzheimer's disease biomarkers}}, journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association}, year = {2026}, month = aug, volume = {22}, number = {8}, pages = {e71711}, publisher = {Wiley}, issn = {1552-5260}, doi = {10.1002/alz.71711}, url = {https://doi.org/10.1002/alz.71711}, pmid = {42535261}, pmcid = {PMC13425622} }

RIS

TY - JOUR AU - Saef, Benjamin AU - Petersen, Kellen K. AU - Volluz, Katherine AU - Li, Yan AU - Tosun, Duygu AU - Mila‐Aloma, Marta AU - Shaw, Leslie M. AU - Zetterberg, Henrik AU - Dage, Jeffrey L. AU - Rubel, Carrie E. AU - Ferber, Kyle AU - Du‐Cuny, Lei AU - Coomaraswamy, Janaky AU - Baratta, Michael AU - Mordashova, Yulia AU - Saad, Ziad S. AU - Triana‐Baltzer, Gallen AU - Ashton, Nicholas J. AU - Meyers, Emily A. AU - Rosenbaugh, Erin G. AU - Sabandal, J. Martin AU - Bannon, Anthony W. AU - Potter, William Z. AU - Alzheimer's Disease Neuroimaging Initiative (ADNI) AU - Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium Plasma Aβ and Phosphorylated Tau as Predictors of Amyloid and Tau Positivity in Alzheimer's Disease Project Team AU - Schindler, Suzanne E. TI - Exploring longitudinal relationships among Alzheimer's disease biomarkers T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association J2 - Alzheimers Dement PY - 2026 DA - 2026/08/01 VL - 22 IS - 8 SP - e71711 SN - 1552-5260 PB - Wiley DO - 10.1002/alz.71711 UR - https://doi.org/10.1002/alz.71711 LA - en ER -

CSL-JSON

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