Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI.

Code ↔ Paper

10 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 10 matches

[1] § Methods › Quantifying eccentricity- and location-dependent variations in sensitivity › Using subject-specific pRF estimates ↔ BackProjection_Slopes_Patient.m, lines 99 to 138 · score 0.93 · near periphery, 0.5–2.5 deg, 15–20 deg, 2.5–4.5 deg, 4.5–9.5 deg, 9.5–15 deg · Report a mismatch
[2] § Methods › Quantifying eccentricity- and location-dependent variations in sensitivity › Using subject-specific pRF estimates ↔ BackProjection_Slopes_Controls.m, lines 160 to 200 · score 0.88 · 0.5–2.5 deg, 15–20 deg, 2.5–4.5 deg, 4.5–9.5 deg, 9.5–15 deg, fovea · Report a mismatch
[3] § Methods › Quantifying eccentricity- and location-dependent variations in sensitivity › Using calibrated Benson retinotopic template atlas ↔ src/eccen_adjust/__init__.py, lines 61 to 151 · score 0.82 · foveal confluence, integrating Horton, surface area, numerical, Hoyt, match · Report a mismatch
[4] § Results › Effect of eye movements on V1 cortical sensitivity ↔ BackProjection_Slopes_Controls.m, lines 160 to 200 · score 0.80 · 0.5–2.5 deg, 15–20 deg, 2.5–4.5 deg, 4.5–9.5 deg, 9.5–15 deg, eye motion · Report a mismatch
[5] § Results › Spatial frequency preference changes in V1 neuronal populations across eccentricities ↔ BackProjection_Slopes_Patient.m, lines 99 to 138 · score 0.80 · 0.5–2.5 deg, 15–20 deg, 2.5–4.5 deg, 4.5–9.5 deg, 9.5–15 deg, peripheral · Report a mismatch
[6] § Results › Contrast sensitivity is not equal across the visual field quadrants in V1 neurons ↔ BackProjection_Slopes_Controls.m, lines 987 to 1094 · score 0.64 · Vertical Meridian Anisotropy, Left Right Horizontal, cortical contrast sensitivity, HVA, VMA, positions · Report a mismatch
[7] § Methods › Quantifying eccentricity- and location-dependent variations in sensitivity › Using calibrated Benson retinotopic template atlas ↔ src/eccen_adjust/__init__.py, lines 39 to 59 · score 0.64 · surface area, maximum eccentricities, cortical magnification, Horton, Hoyt, model · Report a mismatch
[8] § Methods › Processing of the contrast sensitivity task ↔ BackProjection_Slopes_Controls.m, lines 24 to 43 · score 0.53 · FreeSurfer, SPM12, compatible, MATLAB · Report a mismatch
[9] § Methods › Processing of the contrast sensitivity task ↔ BackProjection_Slopes_Patient.m, lines 23 to 42 · score 0.53 · FreeSurfer, SPM12, compatible, MATLAB · Report a mismatch
[10] § Methods › Quantifying eccentricity- and location-dependent variations in sensitivity › Using calibrated Benson retinotopic template atlas ↔ src/eccen_adjust/__init__.py, lines 39 to 59 · score 0.52 · magnification model, surface area, Horton, Hoyt, 90 deg, cortical · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 1,606 lines · 82 KB · MIT · 4 matches

%% =========================================================================
% SECTION 1 — INITIALISE ENVIRONMENT AND SET PATHS
% -------------------------------------------------------------------------
% This section:
% • Resets the MATLAB workspace
% • Detects whether MATLAB is running on Intel vs Apple Silicon macOS
% • Loads the appropriate versions of SPM and FreeSurfer MATLAB toolboxes
% • Prompts the user to select the project folder
% =========================================================================
clear all; close all; clc; commandwindow;
set(0,'units','pixels');
Rect = get(0,'ScreenSize'); % Monitor resolution in pixels
%% Detect CPU architecture (Intel vs Apple Silicon)
% Required because SPM12 and FreeSurfer ship different binaries for each.
if ismac()
[~,result] = system('uname -m');
is_silicon_mac = strcmp(strtrim(result),'arm64'); % Apple Silicon machines report "arm64"
else
is_silicon_mac = false;
end
%% Add required toolboxes based on detected architecture
% These paths MUST be customised by the user before running.
% -------------------------------------------------------------------------
% For Apple Silicon:
% - Uses a M1/M2/M3‑compatible SPM build
% - Uses FreeSurfer 7.4.1 MATLAB tools
%
% For Intel macOS or other systems:
% - Uses standard SPM12 build
% - Uses FreeSurfer 7.1.1 MATLAB tools
% -------------------------------------------------------------------------
if is_silicon_mac
% Apple Silicon versions
addpath(genpath('~/Dropbox (Personal)/PhD_UCL/Hugo_PhD/Toolboxes/spm12-M3chip'));
addpath(genpath(['/Applications/freesurfer/7.4.1/matlab']))
else
% Intel macOS / other systems
addpath(genpath('~/Dropbox (Personal)/PhD_UCL/Hugo_PhD/Toolboxes/spm12'));
addpath(genpath(['/Applications/freesurfer/7.1.1/matlab']))
end
%% Ask user to choose the main project directory (interactive GUI)
pinfo = struct();
disp('SELECT MAIN PROJECT FOLDER')
pinfo.Home = uigetdir(); % User selects folder containing beta-values (BetaValues)
%% =========================================================================
% SECTION 2 — USER INPUT & CONDITION SELECTION
% -------------------------------------------------------------------------
% This section:
% • Lets the user choose retinotopic map source (Benson vs pRF)
% • Asks whether to generate per‑subject figures
% • Scans the BetaValues directory and categorises files by condition
% • Provides GUI dialogs for selecting:
% - Experimental condition (Low/High SF, Eye Motion, Visual Loss)
% - Visit/session (if applicable)
% - One or multiple subjects
% • Produces a list of filenames (selectfn{indx}) for later analysis
% =========================================================================
Eccentricity = 20; % Maximum eccentricity to display/use (deg)
ContrastToPlot = [7.5, 42.2, 60, 100]; % Tested contrast levels
%% Ask which retinotopy dataset to use
% Choice determines which files are matched later:
% 'benson_Neuropy' → Benson retinotopic atlas outputs
% 'pRF_NonEq' → Subject-specific pRF mapping outputs
pRFFolderName = input('BENSON (0) OR pRF MAP (1)? ','s');
switch pRFFolderName
case '0'
pRFFolderName = 'benson_Neuropy';
case '1'
pRFFolderName = 'pRF_NonEq';
end
%% Ask whether to generate subject-level diagnostic figures
% 0 = skip (faster)
% 1 = create all figures (slower but visually informative)
PlotInd = str2num(input('Plot figures for each individual (0/1)? ','s'));
%% =========================================================================
% Collect list of available BetaValues files & classify them
% =========================================================================
directory_allfiles = dir(fullfile(pinfo.Home,'BetaValues'));
% Each index corresponds to a specific experimental label embedded in filenames.
% Using logical masks to filter subjects based on naming conventions.
idx_sem = contains({directory_allfiles.name},'SEM') & contains({directory_allfiles.name},pRFFolderName); % Simulated Eye Motion
idx_RingLoss = contains({directory_allfiles.name},'Ring') & contains({directory_allfiles.name},pRFFolderName); % Ring-shaped loss
idx_URLoss = contains({directory_allfiles.name},'UpRight') & contains({directory_allfiles.name},pRFFolderName); % Upper-right VF loss
idx_visit2 = contains({directory_allfiles.name},'RepMeas') & contains({directory_allfiles.name},pRFFolderName); % Repeat-measures session (Visit 2)
idx_controls = ~contains({directory_allfiles.name},'P1') & contains({directory_allfiles.name},pRFFolderName); % Controls only
% Visit 1 = everything not labelled RingLoss, URLoss, SEM, or visit2
idx_visit1 = idx_controls - idx_RingLoss - idx_URLoss - idx_sem - idx_visit2;
%% =========================================================================
% GUI dialog: Select which experimental condition to analyse
% =========================================================================
answer = questdlg('Choose one condition','Condition Label','Low & High SF','SimEyeMotion','SimVisualLoss','Low & High SF');
switch answer
case 'Low & High SF' % CASE 1: Low vs High Spatial Frequency experiment
% Ask whether this is Visit 1 or Visit 2
answer2 = questdlg('1st or 2nd visit?','Session Label','Visit 1','Visit 2','Visit 1');
switch answer2
case 'Visit 1'
selectfn = {directory_allfiles(find(idx_visit1)).name};
[indx,~] = listdlg('PromptString',{'Select a file.','Multiple file can be selected at a time.',''}, ...
'SelectionMode','multiple','ListString',selectfn);
case 'Visit 2'
selectfn = {directory_allfiles(find(idx_visit2)).name};
[indx,~] = listdlg('PromptString',{'Select a file.','Multiple file can be selected at a time.',''}, ...
'SelectionMode','multiple','ListString',selectfn);
end
case 'SimEyeMotion'
% Eye‑movement simulation condition
selectfn = {directory_allfiles(find(idx_sem)).name};
[indx,~] = listdlg('PromptString',{'Select a file.','Multiple file can be selected at a time.',''}, ...
'SelectionMode','multiple','ListString',selectfn);
case 'SimVisualLoss'
% Choose simulated loss type
answer3 = questdlg('3-8deg Ring or Upper-Right VF loss?','Loss Type','Ring','Quadrant','Ring');
switch answer3
case 'Ring'
selectfn = {directory_allfiles(find(idx_RingLoss)).name};
[indx,~] = listdlg('PromptString',{'Select a file.','Multiple file can be selected at a time.',''}, ...
'SelectionMode','multiple','ListString',selectfn);
case 'Quadrant'
selectfn = {directory_allfiles(find(idx_URLoss)).name};
[indx,~] = listdlg('PromptString',{'Select a file.','Multiple file can be selected at a time.',''}, ...
'SelectionMode','multiple','ListString',selectfn);
end
end
% Stop if user cancels
if isempty(indx)
return
end
%% =========================================================================
% SECTION 3 — DEFINE ECCENTRICITY BINS & POLAR ANGLE WEDGE REGIONS
% -------------------------------------------------------------------------
% This section sets up all visual-field sampling coordinates used for
% extracting vertex subsets from the cortical surface.
%
% Steps:
% • Define eccentricity bins depending on experimental condition
% • Compute bin midpoints, search radii, and tick labels
% • Define polar-angle ROIs (meridians or obliques)
% • Build a full set of (θ, r) seed coordinates
% • Convert seed coordinates to Cartesian (x,y)
% • Define search radius for each seed for vertex selection
% =========================================================================
%% -------------------------------------------------------------------------
% ECCENTRICITY BINNING
% Different experimental conditions use different eccentricity bin sizes.
% -------------------------------------------------------------------------
if strcmp(answer,'SimVisualLoss')
% ----------------------------- RING LOSS ------------------------------
if strcmp(answer3,'Ring')
answer4 = questdlg('Ring Loss: 1deg binning or less detailed','Loss Binning','1deg','Less detailed','1deg');
switch answer4
case '1deg'
% High-resolution eccentricity bins (1° width)1
ECCBoundaries = round([0.5:1:20],1); % 0.5º to 20º
BinModeLabel = 'PerECC';
case 'Less detailed'
% Low-resolution bins: {0–3°, 3–8°, 8–15°, 15–20°}
BinModeLabel = '4Bins';
ECCBoundaries = [0 3 8 15 20];
end
else
% -------------------------- QUADRANT LOSS ------------------------------
% Quadrant VF loss always uses 1° resolution
ECCBoundaries = round([0.5:1:20],1);
BinModeLabel = 'PerECC';
end
else
% -------------------------------------------------------------------------
% DEFAULT CASE — Low/High SF or SimEyeMotion
% Use clinically meaningful eccentricity bands
% -------------------------------------------------------------------------
% These boundary definitions correspond to known anatomical regions:
Fovea = [0.5,2.5];
ParaFovea = [2.5,4.5];
PeriFovea = [4.5,9.5];
NearPeriphery1 = [9.5,15];
NearPeriphery2 = [15,20];
BinModeLabel = 'ClinicallyDefined';
ECCBoundaries = unique([Fovea,ParaFovea,PeriFovea,NearPeriphery1,NearPeriphery2]);
end
%% -------------------------------------------------------------------------
% Compute eccentricity bin helper variables
% -------------------------------------------------------------------------
BinECC = ECCBoundaries; % Bin edges
StepSizeECC = diff(ECCBoundaries)/2; % Half-bin width
RadiusForSearch = diff(ECCBoundaries)/2; % Search radius = half bin
Radius_seedroi = ECCBoundaries(1:end-1)+StepSizeECC; % Bin midpoints
BinTick = Radius_seedroi; % Used for axis ticks later
%% =========================================================================
% POLAR ANGLE DEFINITIONS
% These define which meridians (or oblique axes) to sample from.
% -------------------------------------------------------------------------
% MATLAB polar-angle convention for pRF outputs:
% 0 rad = Right horizontal meridian (RHM)
% π/2 rad = Upper vertical meridian (UVM)
% -π/2 rad = Lower vertical meridian (LVM)
% π rad = Left meridian (LHM)
% =========================================================================
% Cardinal meridians
UVM = [pi/2]; % Upper Vertical Meridian (Ventral V1 = Positive Polar Values)
LVM = [-pi/2]; % Lower Vertical Meridian (Dorsal V1 = Negative Polar Values)
LHM = [-pi]; % Left Horizontal Meridian (Right V1 = Values between -90:-180/180:90)
RHM = [0]; % Right Horizontal Meridian (Left V1= Values between -90:0:90)
% Oblique diagonals (used for quadrant loss)
Diagonals = [-pi/4; pi/4; 3/4*pi; -3/4*pi]; % (Lower‑Right, Upper‑Right, Upper‑Left, Lower‑Left)
%% Determine which polar-angle set to use
if strcmp(answer,'SimVisualLoss')
switch answer3
case 'Ring'
% -------------------------------------------------- RING LOSS -----
PolarCoords = [LHM;UVM;RHM;LVM]; % Only cardinal meridians
% Compute wedge half-width (in degrees)
AngleForSearch = unique(rad2deg(diff(sort(PolarCoords(1:4)))))/2;
% Replicate polar angles for each eccentricity bin
th = repmat(PolarCoords(1:4),length(Radius_seedroi),1);
% Colour coding for meridian patches
colour = repmat([0 0.7 0.3; 0.75 0.75 0; 0 0 1; 1 0 0],length(Radius_seedroi),1);
case 'Quadrant'
% ---------------------------------------------- QUADRANT LOSS -----
PolarCoords = [Diagonals]; % Use oblique diagonals
AngleForSearch = unique(rad2deg(diff(sort(PolarCoords(1:4)))))/2;
th = repmat(PolarCoords(1:4),length(Radius_seedroi),1); % -pi/4; pi/4; 3/4*pi; -3/4*pi];
colour = repmat([0.75 0.75 1; 1 0.75 0; 0.75 0.75 0; 0 1 1],length(Radius_seedroi),1);
end
else
% -------------------------------------------------------------------------
% DEFAULT: Use cardinal meridians for SF or eye-movement conditions
% -------------------------------------------------------------------------
PolarCoords = [LHM;UVM;RHM;LVM];
AngleForSearch = unique(rad2deg(diff(sort(PolarCoords(1:4)))))/2;
th = repmat(PolarCoords(1:4),length(Radius_seedroi),1);
colour = repmat([0 0.7 0.3; 0.75 0.75 0; 0 0 1; 1 0 0],length(Radius_seedroi),1);
end
% Should eccentricity search be constrained by wedge angle (i.e., take into account cortical magnification)?
% If MScaling == 1 -> search region is a wedge (θ ± Δθ AND r ± Δr)
% If MScaling == 0 -> search region is a circle in (x,y) / (eccentricity) space only.
MScaling = 1;
%% =========================================================================
% Build seed coordinates in polar space (θ, r) and convert to (x,y)
% =========================================================================
% Create radial coordinate repeated for each polar angle
r = [];
for i = 1:length(Radius_seedroi)
r = [r; repmat(Radius_seedroi(i),length(th)/length(Radius_seedroi),1)];
end
% Convert polar -> Cartesian coordinates (for visual-space mapping)
[x,y] = pol2cart(th,r);
x_ycoords0 = []; x_ycoords0 = [x,y];
% Clean tiny negative zeros produced by floating point precision
x_ycoords0(x_ycoords0 > -1e-6 & x_ycoords0 < 1e-6) = 0; % some values are -0, correct them
%% =========================================================================
% Single-ROI (V1‑wide) coordinate definition
% Instead of multiple eccentricity bins, use 4 fixed quadrants.
% =========================================================================
x_ycoords0_1ROI = [-max(BinECC)/2 0; 0 max(BinECC)/2; max(BinECC)/2 0; 0 -max(BinECC)/2];
r_search_1ROI = repmat(max(BinECC)/2-min(BinECC),length(x_ycoords0_1ROI),1);
% Convert to polar again for convenience
[th_1ROI,r_1ROI] = cart2pol(x_ycoords0_1ROI(:,1),x_ycoords0_1ROI(:,2));
%% =========================================================================
% Define search radius for each seed point
% =========================================================================
r_search = [];
for i = 1:length(RadiusForSearch)
r_search = [r_search; repmat(RadiusForSearch(i),size(x_ycoords0,1)/length(RadiusForSearch),1)];
end
%% -------------------------------------------------------------------------
% Angular wedge width for M-scaling (converted to radians)
% -------------------------------------------------------------------------
if MScaling
a_search = AngleForSearch;
a_search_rad = deg2rad(a_search);
end
%% =========================================================================
% SECTION 4 — PLOTTING OPTIONS, COLOUR MAPS, MODEL PARAMETERS, PREALLOCATION
% -------------------------------------------------------------------------
% This section:
% • Sets default plotting styles (line types, marker types, labels)
% • Lets the user choose a heatmap colour scale (incl. colour‑blind safe)
% • Selects the exponent strategy for the cortical contrast sensitivity model R = a + b*C^p
% • Preallocates (initialises) containers used later for:
% - Eccentricity‑binned data & CI
% - Polar‑angle ROI data & CI
% - Fitted parameters and counts
% - pRF size summaries
% =========================================================================
%% -------------------------------------------------------------------------
% General plotting preferences
% -------------------------------------------------------------------------
idxFigs = 0; % Counter for figure handles
YAxisLim = [-5 20]; % Default plotting range
lineStyle = {'-','-','--','--'}; % One per SF condition (fallback)
MarkerType = {'o','o','s','s'}; % Markers per SF condition
VariableLabel = {'Beta Values'}; % Y-axis label for β estimates
%% -------------------------------------------------------------------------
% Heatmap colour scale selection (with an accessible palette option)
% Choices:
% 0: MATLAB 'hot' colormap
% 1: Custom black → yellow linear gradient
% 2: Colour-blind friendly palette (Paul Tol-like sequence, interpolated)
% -------------------------------------------------------------------------
colorchoice = str2num(input('Heatmap color scale: hot scale (0) or black-to-yellow (1) or color-blindness (2)? ','s'));
if colorchoice == 1
% ----- Black -> Yellow continuous gradient (256 steps)
numColors = 256;
colorMap = zeros(numColors, 3);
blackRGB = [0, 0, 0];
yellowRGB = [1, 1, 0];
% Interpolate between black and yellow
for i = 1:numColors
colorMap(i, :) = blackRGB + (i - 1) / (numColors - 1) * (yellowRGB - blackRGB);
end
% NOTE: Several plotting sections set the axes background using 'colorBck'.
% Define a neutral light gray here to keep behaviour consistent.
colorBck = [0.75 0.75 0.75];
elseif colorchoice == 2
% ----- Colour‑blind safe palette (interpolated to 256 colours)
% RGB anchors (approx. from a colour‑blind friendly set):
colors = [
100, 143, 255; % Light blue
120, 94, 240; % Purple
220, 38, 127; % Magenta
254, 97, 0; % Orange
255, 176, 0 % Yellow
];
colorBck = [0.75 0.75 0.75];
% Interpolate to produce a smooth 256‑colour map
positions = linspace(0, 1, size(colors, 1));
N = 256;
colorMap = interp1(positions, colors/255, linspace(0, 1, N));
elseif colorchoice == 0
% ----- MATLAB 'hot' built‑in colormap
colorMap = 'hot';
colorBck = [0.75 0.75 0.75];
end
%% -------------------------------------------------------------------------
% Model fitting option for cortical contrast sensitivity exponent p in: R = a + b * C^p
% Choices:
% 1: Fit exponent p from the data (nonlinear fit)
% 2: Fix p = 0.5 (i.e., R = a + b * sqrt(C))
% -------------------------------------------------------------------------
FitExponent = str2num(input('Fit exponent (1) or sqrt of C = 0.5 (2): ','s'));
%% -------------------------------------------------------------------------
% Preallocate / initialise containers for analysis results
% (They will be populated incrementally in the subject loop)
% -------------------------------------------------------------------------
% -------------------- ECCENTRICITY‑BINNED DATA ----------------------------
% Data_ECC(sub, bin, SF, contrast) : Mean β per bin/condition
% CIBetaVals_ECC(sub, SF, bin, contrast, 2) : 95% CI on β (lower/upper)
% FitParams_ECC(sub, SF, bin, [a b p]) : Mean fitted cortical contrast sensitivity params
% FitParams_ECC_nvals(sub, SF, bin) : Count of contributing vertices
% ECC_pRFSize(sub, SF, bin) : Mean pRF size in bin
Data_ECC = [];
CIBetaVals_ECC = [];
FitParams_ECC = [];
FitParams_ECC_nvals = [];
ECC_pRFSize = [];
% -------------------- POLAR‑ANGLE ROI (BIN × POLAR) -----------------------
% Data_VFrois(sub, SF, contrast, bin, polar) : Mean β
% FitParams_VFrois(sub, SF, bin, polar, [a b p]) : Fitted cortical contrast sensitivity params
% FitParams_VFrois_nvals(sub, SF, bin, polar) : Count of vertices
% VFrois_pRFSize(sub, SF, bin, polar) : Mean pRF size
Data_VFrois = [];
FitParams_VFrois = [];
FitParams_VFrois_nvals = [];
VFrois_pRFSize = [];
% -------------------- SINGLE‑ROI (V1‑WIDE) POLAR INDICES ------------------
% Data_VFrois_1ROI(sub, SF, contrast, polar) : Mean β, pooled V1
% FitParams_VFrois_1ROI(sub, SF, polar, [a b p]) : Fitted cortical contrast sensitivity params
% FitParams_VFrois_1ROI_nvals(sub, SF, polar) : Count of vertices
% VFrois_1ROI_pRFSize(sub, SF, polar) : Mean pRF size
Data_VFrois_1ROI = [];
FitParams_VFrois_1ROI = [];
FitParams_VFrois_1ROI_nvals = [];
VFrois_1ROI_pRFSize = [];
%% =========================================================================
% SECTION 5 — SUBJECT LOOP: LOAD CSV, ORGANISE BETA DATA, FIT PER‑VERTEX
% CORTICAL CONTRAST SENSITIVTY
% -------------------------------------------------------------------------
% For each selected subject file the script:
% 1. Loads the CSV (β-values + pRF parameters)
% 2. Extracts subject ID, spatial frequencies, contrasts
% 3. Reshapes β-values into [SF × Contrast × Vertex]
% 4. Extracts pRF eccentricity, polar angle, sigma, and R²
% 5. For every vertex:
% • Fit cortical contrast sensitivity model R = a + b*C^p
% • Store intercept, slope, exponent, residuals
% =========================================================================
close all; % Reset plots for each subject
for subs = 1:length(selectfn(indx))
%% ---------------------------------------------------------------------
% Display which subject is being processed
% ----------------------------------------------------------------------
tmp = []; tmp = split(selectfn{indx(subs)},'_');
disp(['Subject: ' tmp{1}])
%% ---------------------------------------------------------------------
% Load subject CSV table
% Table structure includes columns such as:
% SubID, SF, ContrastLevel, Beta,
% pRFecc, pRFpolar, pRFsize, pRFrsquare
% ----------------------------------------------------------------------
T = [];
T = readtable(char(fullfile(pinfo.Home,'BetaValues',selectfn{indx(subs)})));
% Basic identifiers extracted from table
SubID = char(unique(T.SubID));
SFLabels = unique(T.SF); % Spatial frequencies tested
ContrastLabels = unique(T.ContrastLevel); % Contrast levels tested
Data = T.Beta; % β-values across all conditions
%% ---------------------------------------------------------------------
% Determine number of vertices for a single condition
% The table repeats pRF values across contrasts/SFs; we extract one block.
% ----------------------------------------------------------------------
nVertsPerCond = length(T.SubID([T.ContrastLevel == ContrastLabels(1) & T.SF == SFLabels(1)]));
%% ---------------------------------------------------------------------
% Extract pRF parameters for the first condition (unique across table)
% ----------------------------------------------------------------------
pRFx_y_ECC = T.pRFecc(1:nVertsPerCond); % Eccentricity (º)
pRFx_y_Polar = T.pRFpolar(1:nVertsPerCond); % Polar angle (rad)
pRFSigma_ECC = T.pRFsize(1:nVertsPerCond); % pRF sigma (º)
pRFR2_ECC = T.pRFrsquare(1:nVertsPerCond); % Model R²
%% ---------------------------------------------------------------------
% Reshape β-values into [SF × Contrast × Vertex]
% Then permute to keep dimensions consistent throughout analysis.
% ----------------------------------------------------------------------
BetaVals_ECC = reshape(Data,nVertsPerCond,length(ContrastLabels),length(SFLabels));
BetaVals_ECC = permute(BetaVals_ECC,[3 2 1]); % β-values: BetaVals_ECC(SF, Contrast, Vertex)
%% =========================================================================
% CLASSIFY VERTICES INTO VISUAL-FIELD SEED REGIONS (ECC × POLAR)
% -------------------------------------------------------------------------
% For each seed coordinate (θ, r) defined in Section 3:
% • Identify vertices within eccentricity radius
% • Optional: restrict by polar-angle wedge (M-scaling)
% • Store included vertices in vtxidx(coord).inclvtx
% =========================================================================
vtxidx = struct(); % Container for all seed-based vertex lists
for coord = 1:size(x_ycoords0,1)
% Convert seed’s Cartesian location to polar (th_seed, rseed)
[a,b] = cart2pol(x_ycoords0(coord,1),x_ycoords0(coord,2));
vtxidx(coord).seed = x_ycoords0(coord,:);
vtxidx(coord).seed_PolarRad = a;
vtxidx(coord).seed_EccDeg = b;
%% -------------------------------------------------------------
% Select vertices based on eccentricity + polar constraints
% - If MScaling=1: apply angular wedge limits
% - Else: use simple Euclidean distance in (ECC, polar)
% -------------------------------------------------------------
if MScaling
% ---- LEFT/RIGHT HORIZONTAL MERIDIANS ----
if x_ycoords0(coord,1) < 0 && x_ycoords0(coord,2) == 0 || x_ycoords0(coord,1) > 0 && x_ycoords0(coord,2) == 0
k = {}; k = {find(pRFx_y_ECC >= b-r_search(coord) & pRFx_y_ECC <= b+r_search(coord) & ...
abs(pRFx_y_Polar) >= abs(a)-a_search_rad & abs(pRFx_y_Polar) <= abs(a) + a_search_rad)};
% ---- UPPER VERTICAL MERIDIAN ----
elseif x_ycoords0(coord,1) == 0 && x_ycoords0(coord,2) > 0
k = {}; k = {find(pRFx_y_ECC >= b-r_search(coord) & pRFx_y_ECC <= b+r_search(coord) & ...
pRFx_y_Polar >= a-a_search_rad & pRFx_y_Polar <= a + a_search_rad)};
% ---- LOWER VERTICAL MERIDIAN ----
elseif x_ycoords0(coord,1) == 0 && x_ycoords0(coord,2) < 0
k = {}; k = {find(pRFx_y_ECC >= b-r_search(coord) & pRFx_y_ECC <= b+r_search(coord) & ...
pRFx_y_Polar <= a+a_search_rad & pRFx_y_Polar >= a - a_search_rad)};
% ---- LOWER OBLIQUES (LR quadrant) ----
elseif x_ycoords0(coord,1) > 0 && x_ycoords0(coord,2) < 0 || x_ycoords0(coord,1) < 0 && x_ycoords0(coord,2) < 0
k = {}; k = {find(pRFx_y_ECC >= b-r_search(coord) & pRFx_y_ECC <= b+r_search(coord) & ...
pRFx_y_Polar <= a + a_search_rad & pRFx_y_Polar >= a - a_search_rad)};
% ---- UPPER OBLIQUES (UR quadrant) ----
elseif x_ycoords0(coord,1) > 0 && x_ycoords0(coord,2) > 0 ||x_ycoords0(coord,1) < 0 && x_ycoords0(coord,2) > 0
k = {}; k = {find(pRFx_y_ECC >= b-r_search(coord) & pRFx_y_ECC <= b+r_search(coord) & ...
pRFx_y_Polar >= a - a_search_rad & pRFx_y_Polar <= a + a_search_rad)};
end
% Distance from seed in eccentricity space
D = {abs(pRFx_y_ECC(cell2mat(k))-b)};
else
% No angular wedge — circular window in ECC space
[k,D] = rangesearch(pRFx_y,x_ycoords0(coord,:),r_search(coord),'SortIndices',false);
end
% Final vertex indices for this ROI
D = cell2mat(D);
tmp = []; tmp = cell2mat(k);
vtxidx(coord).inclvtx = tmp;
% --- Diagnostic printout ---
fprintf('Coordinates %s [%s-%sº: %s vertices]: Minimum and maximum Euclidian distance from seed points = [%s - %s]º (search radius set to %sº) \n',...
num2str(coord),num2str(min(pRFx_y_ECC(vtxidx(coord).inclvtx))),num2str(max(pRFx_y_ECC(vtxidx(coord).inclvtx))),num2str(length(vtxidx(coord).inclvtx)),...
num2str(min(D)),num2str(max(D)),num2str(r_search(coord)))
% figure;
% ax = polarscatter(pRFx_y_Polar,pRFx_y_ECC,SizePoint,[0.9 0.9 0.9],'filled');
% hold on; polarscatter(pRFx_y_Polar(vtxidx(coord).inclvtx),pRFx_y_ECC(vtxidx(coord).inclvtx),50,'filled')
end
%% =========================================================================
% PER‑VERTEX CORTICAL CONTRAST SENSITIVITY FITTING: R = a + b * C^p
% -------------------------------------------------------------------------
% Fit the contrast-response function for each vertex and each SF.
% =========================================================================
% Select contrast indices corresponding to ContrastToPlot
idxC_ToPlot = find(ismember(ContrastLabels,ContrastToPlot));
C = ContrastLabels(idxC_ToPlot)/100; % normalised contrast (0–1)
fprintf('\n');
func = @(x, C) x(1) + x(2)*C.^x(3); % Vary exponent (if FitExponent = 2; set to 0.5)
% func = @(x, C) x(1) + x(2)*C.^(x(3)+x(4))./(C.^x(4)+x(5)^x(4)); % Full Naka-Rushton equation
% Initialise variables for each subject (Reason: differences in vertex count from pRF structure)
x_slope_intercept = [];
x_resnorm = [];
x_residuals = [];
x_exponent = [];
% Compute slope values for each CSF condition
for CSFCondition = 1:size(BetaVals_ECC,1)
disp(['... ' num2str(SFLabels(CSFCondition)) 'cpd: Fitting Linear Equation (R = a+b*C) – Vertex level'])
% Preallocate SF-specific outputs
a = nan(1,size(BetaVals_ECC,3)); % intercept
b = nan(1,size(BetaVals_ECC,3)); % slope
c = nan(1,size(BetaVals_ECC,3)); % residual norm
p = nan(1,size(BetaVals_ECC,3)); % exponent
count = 0;
% Loop over vertices
for ii = 1:size(BetaVals_ECC,3)
if ii == 1 || mod(ii,500) == 0
disp(['Vertex #' num2str(ii)])
end
% Extract β-values across contrast levels for this vertex & SF
R = [];
R = squeeze(BetaVals_ECC(CSFCondition,:,ii))';
if sum(isnan(R)) < 4 % requires minimal valid data
% Call custom fitting function for fitting the square-root
% function to the data
[x,resnorm] = dolinearfit_SqCdata(R,C,func,FitExponent);
a(ii) = x(1); % intercept set to 0
b(ii) = x(2); % Slope
p(ii) = x(3); % Exponent (if FitExponent = 1, it is estimated, else, it is set to 0.5)
c(ii) = resnorm;
% Save per-vertex fits
x_slope_intercept(CSFCondition,ii,:) = [a(ii) b(ii)];
x_resnorm(CSFCondition,ii,:) = c(ii);
x_exponent(CSFCondition,ii,:) = p(ii);
else
% Save per-vertex fits
x_slope_intercept(CSFCondition,ii,:) = [a(ii) b(ii)];
x_resnorm(CSFCondition,ii,:) = c(ii);
x_exponent(CSFCondition,ii,:) = p(ii);
end
end
end
%% =========================================================================
% SECTION 6 — APPLY CORTICAL CONTRAST SENSITIVITY FITS TO ECCENTRICITY BINS (+ OPTIONAL PLOTTING)
% -------------------------------------------------------------------------
% This section aggregates vertex-level data within eccentricity bins and:
% • Computes mean β-values per contrast and bin
% • Bootstraps 95% CIs for β-values (per bin & contrast)
% • Averages fitted cortical contrast sensitivity parameters (a, b, p) across vertices per bin
% • Optionally plots per-subject cortical contrast sensitivity per bin
% • Optionally back-projects bin-level slope values onto visual field
%
% Note:
% - The x-axis for plots uses sqrt(C) for readability (percent ticks),
% while the model itself can use either p=0.5 (fixed) or p fitted.
% =========================================================================
%% Eccentricity: Apply Linear Equation (R = a + b*C^p) within bins
if strcmp(answer,'Low & High SF') || strcmp(answer,'SimEyeMotion') || strcmp(answer3,'Ring')
% Colour per SF condition (2 colours repeated if >2 SFs)
colour_ecc = repmat([120 94 240; 254 97 0]/255,round(size(BetaVals_ECC,1)/2),1);
% Line handle container for legends (reused across subplots)
if exist('s','var'); clear s; end
% Loop over spatial frequency conditions
for CSFCondition = 1:size(BetaVals_ECC,1)
disp(['... ' num2str(SFLabels(CSFCondition)) 'cpd: Linear Equation Fit (R = a+b*C.^p) – Eccentricity bin level'])
%% Prepare figure canvas if requested
if PlotInd
ncols = 5; % number of subplots per row
[q,r] = quorem(sym(length(BinTick)),sym(ncols));
nlines = double(q);
if r > 0
nlines = nlines+1;
end
if CSFCondition == 1
idxFigs = idxFigs+1;
if nlines <= 4
figure(idxFigs); set(gcf,'Color','w','Position',[0 0 Rect(3) Rect(4)*nlines/4]);
else
figure(idxFigs); set(gcf,'Color','w','Position',Rect);
end
end
end
%% Iterate through eccentricity bins
for band = 1:length(BinECC)-1
% --------------- Mean β per contrast (within bin) ---------------
R = [];
R = squeeze(nanmean(BetaVals_ECC(CSFCondition,:,pRFx_y_ECC > BinECC(band) & pRFx_y_ECC <= BinECC(band+1)),3))'; % [1 × nContrast]
Data_ECC(subs,band,CSFCondition,:) = R;
% --------------- 95% CI for β via bootstrap ---------------------
for c = 1:size(BetaVals_ECC,2)
y = []; y = squeeze(BetaVals_ECC(CSFCondition,c,pRFx_y_ECC > BinECC(band) & pRFx_y_ECC <= BinECC(band+1))); % β over vertices
if sum(isnan(y)) < length(y)-4
CIBetaVals_ECC(subs,CSFCondition,band,c,:) = bootci(1000,@nanmean,y); % [2×1]
else
CIBetaVals_ECC(subs,CSFCondition,band,c,:) = [NaN;NaN];
end
end
% --------------- Mean fitted params (a, b, p) per bin -----------
% x_slope_intercept = [a b] per vertex; x_exponent = p
FitParams_ECC(subs,CSFCondition,band,:) = [squeeze(nanmean(x_slope_intercept(CSFCondition,pRFx_y_ECC > BinECC(band) & pRFx_y_ECC <= BinECC(band+1),:),2));squeeze(nanmean(x_exponent(CSFCondition,pRFx_y_ECC > BinECC(band) & pRFx_y_ECC <= BinECC(band+1))))];
% Number of contributing vertices (for diagnostics/reporting)
FitParams_ECC_nvals(subs,CSFCondition,band,:) = numel(x_slope_intercept(CSFCondition,pRFx_y_ECC > BinECC(band) & pRFx_y_ECC <= BinECC(band+1),2));
%% -------------------------- Plot per bin -----------------------
if PlotInd
subplot(nlines,ncols,band); hold on
% Error bars from bootstrapped CI
upper = abs(R-squeeze(CIBetaVals_ECC(subs,CSFCondition,band,:,2)));
lower = abs(R-squeeze(CIBetaVals_ECC(subs,CSFCondition,band,:,1)));
errorbar(sqrt(C),R,lower,upper,'Marker',MarkerType{CSFCondition},'LineStyle','none','LineWidth',1,'Color',colour_ecc(CSFCondition,:));
% Overlay function fit using the mean parameters for the bin
C_fit = linspace(min(C), max(C), 100);
R_fit = func(FitParams_ECC(subs,CSFCondition,band,:), C_fit);
s(CSFCondition) = plot(sqrt(C_fit), R_fit,lineStyle{CSFCondition},'Color',colour_ecc(CSFCondition,:),'LineWidth',2);
% Axis formatting
if CSFCondition == size(BetaVals_ECC,1)
legend([s],[num2str(SFLabels),repmat('cpd',length(SFLabels),1)],'Location','NorthWest','box','off')
end
ylim([-5 15])
xlabel('Contrast (%)');% xlabel('Contrast^(^0^.^5^)');
xlim([0 max(C)]); % From 0-1 (normalised values)
xticks(sqrt(C)); % display sqrt(C) ticks for readability
xticklabels(num2str(C*100)); % label in %
ylabel('Response (\beta-value)');
title(['Ecc:' num2str(BinECC(band)) '–' num2str(BinECC(band+1)) 'º'])
set(gca,'LineWidth',2,'FontSize',14)
end
end
end
%% =========================================================================
% Eccentricity — Back-project mean slope (b) to visual space (optional)
% -------------------------------------------------------------------------
% For each SF condition:
% • Take bin-level slope estimates (b)
% • Paint them onto wedge-shaped patches in (θ, r) space
% • Produce a heatmap (per subject) showing spatial distribution of b
% =========================================================================
if PlotInd
idxFigs = idxFigs+1;
figure(idxFigs);
if size(BetaVals_ECC,1) > 2
set(gcf,'Color','w','Position', [0 0 Rect(3) Rect(4)/2])
FontSize = 22;
else
set(gcf,'Color','w','Position', [0 0 Rect(3)/2 Rect(4)/2])
FontSize = 20;
end
% Convert original seeds back to polar (used to draw wedge patches)
[th,r] = cart2pol(x_ycoords0(:,1),x_ycoords0(:,2));
CLim = [0 12]; % colour scale limits for slope
for CSFCondition = 1:size(BetaVals_ECC,1)
disp(['... ' num2str(SFLabels(CSFCondition)) 'cpd: Linear Equation Fit (R = a+b*C.^p) – Back Projection Eccentricity bin level'])
subplot(1,size(BetaVals_ECC,1),CSFCondition)
% Vector of slope values (b) per bin for this subject & SF
Data = []; Data = squeeze(FitParams_ECC(subs,CSFCondition,:,2)); % (a=1, b=2, p=3)
% Draw a patch per seed wedge; reuse the same Data value for
% each set of 4 polar angles (LHM, UVM, RHM, LVM) across bins.
idx = 0;
for coord = 1:length(x_ycoords0) % LHM, RHM, UVM, LVM
% Define wedge bounds around seed (th ± a_search_rad, r ± r_search)
th0 = th(coord)-a_search_rad;
rh0 = r(coord)-r_search(coord);
th1 = th(coord)+a_search_rad;
rh1 = r(coord)+r_search(coord);
a1 = linspace(th0,th0);
r1 = linspace(rh0,rh1);
a2 = linspace(th0,th1);
r2 = linspace(rh1,rh1);
a3 = linspace(th1,th1);
r3 = linspace(rh1,rh0);
a4 = linspace(th1,th0);
r4 = linspace(rh0,rh0);
[X,Y]=pol2cart([a1,a2,a3,a4],[r1,r2,r3,r4]);
% move to next eccentricity bin every 4 patches
if mod(coord,length(PolarCoords)) == 1 % Change Data value after each 4 coordinates (Left, Right, Up, Down)
idx = idx+1;
end
s_ecc_avg(CSFCondition,coord) = patch(X,Y,Data(idx)); % Note: patch function takes text or matrix color def
s_ecc_avg(CSFCondition,coord).LineStyle = '-';
s_ecc_avg(CSFCondition,coord).EdgeColor = 'k';
end
% Axis & colour formatting
axis([-Eccentricity Eccentricity -Eccentricity Eccentricity])
axis square;
yticks([-Eccentricity:Eccentricity/2:Eccentricity])
xticks([-Eccentricity:Eccentricity/2:Eccentricity])
ax = gca;
ax.Color = colorBck;
title([num2str(SFLabels(CSFCondition)) 'cpd'])
colormap(colorMap);
cb = colorbar; % show color bar
cb.Ticks = round(linspace(0,max(CLim),4),2);
ylabel(cb,['Slope'],'Rotation',270,'Position',[4 max(CLim)/2 0]);
set(gca,'FontSize',FontSize,'LineWidth',2,'CLim',CLim);
end
end
end
%% =========================================================================
% SECTION 7 — POLAR-ANGLE ROIs: MERIDIAN AGGREGATION, ANISOTROPY, BACK-PROJ
% -------------------------------------------------------------------------
% This section aggregates β-values and cortical contrast sensitivity fits along polar-angle ROIs
% (meridians or quadrants), computes bootstrapped CIs, and produces:
% • Per-ROI contrast-response curves (with fit overlays)
% • HVA (Horizontal vs Vertical), VMA (UVM vs LVM), LRHM (Left vs Right)
% • V1-wide (single-ROI) pooled measures across eccentricity
% • Back-projected maps of slope (b) onto visual field (polar bins)
%
% Notes on gating:
% - We run this section for:
% 'Low & High SF' OR any SimVisualLoss with answer3 defined.
% - For plotting the quadrant case, the code explicitly checks
% 'Low & High SF' OR 'Quadrant' (no meridian anisotropy for ring).
% =========================================================================
if strcmp(answer,'Low & High SF') || exist('answer3')
if strcmp(answer,'Low & High SF') || strcmp(answer3,'Quadrant')
% Only compute meridian ROIs for:
% • Low & High SF
% • SimVisualLoss with Quadrant loss
CIData_VFrois = [];
CIData_VFrois_1ROI = [];
for CSFCondition = 1:size(BetaVals_ECC,1)
%% -------------------------------------------------------------
% 7.1 Aggregate across polar-angle ROIs (per eccentricity bin)
% -------------------------------------------------------------
% Build Data_VFrois and FitParams_VFrois by iterating through
% (bin, polar) seed coordinates in the same order as vtxidx.
% The loop over idxvtx walks through each seed exactly once.
% -------------------------------------------------------------
idxvtx = 0;
for bin = 1:length(Radius_seedroi) % eccentricity bins
for coord = 1:length(x_ycoords0)/length(Radius_seedroi) % polar coords
idxvtx = idxvtx + 1;
% -------- Mean β for each contrast within this ROI ----
for c = 1:size(BetaVals_ECC,2)
% disp(['Polar = ' num2str(vtxidx(idxvtx).seed_PolarRad) ', ECC = ' num2str(vtxidx(idxvtx).seed_EccDeg) ', Contrast = ' num2str(c)])
% Vertex list for this seed (bin, polar)
incl = []; incl = vtxidx(idxvtx).inclvtx;
Data_VFrois(subs,CSFCondition,c,bin,coord) = nanmean(BetaVals_ECC(CSFCondition,c,incl),3);
% 95% bootstrap CI for β (if enough valid vertices)
if numel(incl) > 2
validCount = []; validCount = numel(incl) - sum(isnan(squeeze(BetaVals_ECC(CSFCondition,c,incl))));
if validCount>2
CIData_VFrois(subs,CSFCondition,c,bin,coord,:) = bootci(1000,@nanmean,BetaVals_ECC(CSFCondition,c,incl));
else
CIData_VFrois(subs,CSFCondition,c,bin,coord,:) = [NaN;NaN];
end
else
CIData_VFrois(subs,CSFCondition,c,bin,coord,:) = [NaN;NaN];
end
end
% -------- Mean fitted cortical contrast sensitivity params within this ROI -------
incl = []; incl = vtxidx(idxvtx).inclvtx;
if isempty(x_slope_intercept(CSFCondition,incl,:))
FitParams_VFrois(subs,CSFCondition,bin,coord,:) = [NaN; NaN; NaN];
else
FitParams_VFrois_nvals(subs,CSFCondition,bin,coord,:) = numel(x_slope_intercept(CSFCondition,incl,2));
FitParams_VFrois(subs,CSFCondition,bin,coord,:) = [squeeze(nanmean(x_slope_intercept(CSFCondition,incl,:),2));squeeze(nanmean(x_exponent(CSFCondition,incl)))];
FitParams_VFrois_nvals(subs,CSFCondition,bin,coord,:) = numel(x_slope_intercept(CSFConditionincl,2));
end
% -------- Mean pRF size within this ROI ----------------
VFrois_pRFSize(subs,CSFCondition,bin,coord) = nanmean(pRFSigma_ECC(vtxidx(idxvtx).inclvtx));
end
end
% -------------------------- Shapes reminder --------------------
% Data_VFrois : [sub, SF, contrast, ECC bin, Polar]
% CIData_VFrois : [sub, SF, contrast, ECC bin, Polar, CI(2)]
% FitParams_VFrois : [sub, SF, ECC bin, Polar, [a b p]]
% FitParams_VFrois_nvals : [sub, SF, ECC bin, Polar]
% VFrois_pRFSize : [sub, SF, ECC bin, Polar]
%% -------------------------------------------------------------
% 7.2 V1 as a SINGLE ROI (pool across eccentricities)
% -------------------------------------------------------------
% For each polar index, pool vertices across all bin radii,
% then compute β, CIs, and fitted parameters.
% -------------------------------------------------------------
% Ensure PolarCoords(1) is positive to match seed_PolarRad polarity
if PolarCoords(1) < 0
PolarCoords(1) = abs(PolarCoords(1));
end
for coord = 1:length(x_ycoords0_1ROI) % LHM, UVM, RHM, LVM
% Gather all vertex indices for this meridian across bins
IA = []; IA = {vtxidx(ismember([vtxidx.seed_PolarRad]',PolarCoords(coord))).inclvtx};
% Mean β and CI (per contrast) for pooled V1-wide ROI
for c = 1:size(BetaVals_ECC,2) % For each contrast level
% disp(['Polar = ' num2str(PolarCoords(coord)) ', Contrast = ' num2str(c)])
Data_VFrois_1ROI(subs,CSFCondition,c,coord) = nanmean(BetaVals_ECC(CSFCondition,c,vertcat(IA{:})),3);
if length(vertcat(IA{:}))>2
if length(vertcat(IA{:})) - sum(isnan(squeeze(BetaVals_ECC(CSFCondition,c,vertcat(IA{:})))))>2
CIData_VFrois_1ROI(subs,CSFCondition,c,coord,:) = bootci(1000,@nanmean,BetaVals_ECC(CSFCondition,c,vertcat(IA{:})));
else
CIData_VFrois_1ROI(subs,CSFCondition,c,coord,:) = NaN;
end
else
CIData_VFrois_1ROI(subs,CSFCondition,c,coord,:) = NaN;
end
end
% Mean fitted cortical contrast sensitivity params and counts
FitParams_VFrois_1ROI(subs,CSFCondition,coord,:) = [squeeze(nanmean(x_slope_intercept(CSFCondition,vertcat(IA{:}),:),2));squeeze(nanmean(x_exponent(CSFCondition,vertcat(IA{:}))))];
FitParams_VFrois_1ROI_nvals(subs,CSFCondition,coord,:) = numel(x_slope_intercept(CSFCondition,vertcat(IA{:}),2));
% Mean pRF size
VFrois_1ROI_pRFSize(subs,CSFCondition,coord) = nanmean(pRFSigma_ECC(vertcat(IA{:})));
end
% Restore first PolarCoords sign if it was modified
if PolarCoords(1) > 0
PolarCoords(1) = -(PolarCoords(1));
end
%% -------------------------------------------------------------
% 7.3 CARDINALS ONLY — add HORIZONTAL vs VERTICAL pooled entries
% -------------------------------------------------------------
% Adds two extra pooled polar indices:
% • HM = LHM ∪ RHM
% • VM = UVM ∪ LVM
% Then re-compute pooled β, CI, and fitted parameters for each.
% -------------------------------------------------------------
if sum(ismember(PolarCoords,[LVM,UVM,LHM,RHM])) > 1
% Identify vtxidx positions for HM and VM (across bins)
Horizontal_1ROI = find([vtxidx.seed_PolarRad] == 0 | abs([vtxidx.seed_PolarRad]) == pi);
Vertical_1ROI = find(abs([vtxidx.seed_PolarRad]) == pi/2);
tmp = []; tmp = {vtxidx(Horizontal_1ROI).inclvtx};
Vtx_Hori = vertcat(tmp{:});
tmp = []; tmp = {vtxidx(Vertical_1ROI).inclvtx};
Vtx_Vert = vertcat(tmp{:});
% Append two extra pooled polar indices at the end:
% index = length(PolarCoords)+1 -> HM
% index = length(PolarCoords)+2 -> VM
for c = 1:size(BetaVals_ECC,2) % For each contrast level
Data_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+1) = nanmean(BetaVals_ECC(CSFCondition,c,Vtx_Hori),3);
Data_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+2) = nanmean(BetaVals_ECC(CSFCondition,c,Vtx_Vert),3);
if length(Vtx_Hori)>2
if length(Vtx_Vert) - sum(isnan(squeeze(BetaVals_ECC(CSFCondition,c,Vtx_Vert))))>2
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+1,:) = bootci(1000,@nanmean,BetaVals_ECC(CSFCondition,c,Vtx_Hori));
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+2,:) = bootci(1000,@nanmean,BetaVals_ECC(CSFCondition,c,Vtx_Vert));
else
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+1,:) = NaN;
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+2,:) = NaN;
end
else
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+1,:) = NaN;
CIData_VFrois_1ROI(subs,CSFCondition,c,length(PolarCoords)+2,:) = NaN;
end
end
% Compute fitted parameters for pooled HM and VM
Horizontal_1ROI = find(PolarCoords == -pi | PolarCoords == 0);
Vertical_1ROI = find(abs(PolarCoords) == pi/2);
% HM
FitParams_VFrois_1ROI(subs,CSFCondition,length(PolarCoords)+1,:) = squeeze(nanmean(FitParams_VFrois_1ROI(subs,CSFCondition,Horizontal_1ROI,:),3));
FitParams_VFrois_1ROI_nvals(subs,CSFCondition,length(PolarCoords)+1) = squeeze(nanmean(FitParams_VFrois_1ROI_nvals(subs,CSFCondition,Horizontal_1ROI),3));
VFrois_1ROI_pRFSize(subs,CSFCondition,length(PolarCoords)+1) = squeeze(nanmean(VFrois_1ROI_pRFSize(subs,CSFCondition,Horizontal_1ROI),3));
% VM
FitParams_VFrois_1ROI(subs,CSFCondition,length(PolarCoords)+2,:) = squeeze(nanmean(FitParams_VFrois_1ROI(subs,CSFCondition,Vertical_1ROI,:),3));
FitParams_VFrois_1ROI_nvals(subs,CSFCondition,length(PolarCoords)+2) = squeeze(nanmean(FitParams_VFrois_1ROI_nvals(subs,CSFCondition,Vertical_1ROI),3));
VFrois_1ROI_pRFSize(subs,CSFCondition,length(PolarCoords)+2) = squeeze(nanmean(VFrois_1ROI_pRFSize(subs,CSFCondition,Vertical_1ROI),3));
%% ---------------------------------------------------------
% 7.4 OPTIONAL PLOTS — pRF ROI locations + cortical contrast sensitivity per anisotropy
% ---------------------------------------------------------
if PlotInd
disp(['... ' num2str(SFLabels(CSFCondition)) 'cpd: Linear Equation Fit (R = a+b*C.^p) – Along the meridians'])
% Set up figure
idxFigs = idxFigs+1;
figure(idxFigs);
set(gcf,'Color','w','Position', [0 0 Rect(3)/2 Rect(4)])
SizePoint = 20;
if exist('s','var')
clear s
end
% 3 anisotropy panels: [HVA, VMA, LRHM]
for ani = 1:3
subplot(3,2,ani+(ani-1));
% ---- Left column: pRF locations contributing to each ROI
ax = polarscatter(pRFx_y_Polar,pRFx_y_ECC,SizePoint,[0.9 0.9 0.9],'filled');
PolarIdx = [];
if ani == 1 % HVA
colour = repmat([0.4 0.72 0.16; 0.5 0 0.5],length(Radius_seedroi),1); % [LHM, RHM], [UVM, LVM]
PolarIdx(1,:) = find([vtxidx.seed_PolarRad] == 0 | abs([vtxidx.seed_PolarRad]) == pi);
PolarIdx(2,:) = find(abs([vtxidx.seed_PolarRad]) == pi/2);
elseif ani == 2 % VMA
colour = repmat([0 0 1; 1 0 0],length(Radius_seedroi),1); % UVM, LVM
PolarIdx(1,:) = find([vtxidx.seed_PolarRad] == pi/2);
PolarIdx(2,:) = find([vtxidx.seed_PolarRad] == -pi/2);
elseif ani == 3 % LRHM
colour = repmat([0 0.7 0.3; 0.75 0.75 0],length(Radius_seedroi),1); % LHM, RHM
PolarIdx(1,:) = find(abs([vtxidx.seed_PolarRad]) == pi);
PolarIdx(2,:) = find([vtxidx.seed_PolarRad] == 0);
end
hold on
for i = 1:size(PolarIdx,1)
tmp = {vtxidx(PolarIdx(i,:)).inclvtx};
polarscatter(pRFx_y_Polar(vertcat(tmp{:})),pRFx_y_ECC(vertcat(tmp{:})),SizePoint,colour(i,:),'filled','MarkerEdgeColor','k');
end
set(ax.Parent,'FontSize',14,'LineWidth',2,'Layer','top',...
'GridAlpha',0.5,'GridColor',[0.15 0.15 0.15],'MinorGridAlpha',1,'MinorGridColor',[0.15 0.15 0.15],...
'RColor',[0.15 0.15 0.15],'RTick',[2 5:5:Eccentricity],'RTickLabelRotation',0,...
'ThetaColor',[0.15 0.15 0.15],'ThetaTickLabel',{'0','30','60','90','120','150','±180','-150','-120','-90','-60','-30'})
% ---- Right column: Cortical contrast sensitivity estimates (slopes) with CI & fit curves
PolarIdx = [];
if ani == 1
disp('....... Horizontal versus Vertical Anisotropy ')
colour = [255 176 0; 100 143 255]/255; % Horizontal, Vertical
PolarIdx(1,:) = length(PolarCoords)+1;
PolarIdx(2,:) = length(PolarCoords)+2;
LegendLabel = {'HM','VM'};
TitleLabel = 'HVA';
elseif ani == 2
disp('....... Vertical Meridians Anisotropy ')
colour = [32 189 84; 212 22 180]/255; % Upper, Lower
PolarIdx(1,:) = find(PolarCoords == pi/2);
PolarIdx(2,:) = find(PolarCoords == -pi/2);
LegendLabel = {'UVM','LVM'};
TitleLabel = 'VMA';
elseif ani == 3
disp('....... Left-Right Horizontal Meridians Anisotropy ')
colour = [0 0 0; 128 128 128]/255; % Left, Right
PolarIdx(1,:) = find(PolarCoords == -pi);
PolarIdx(2,:) = find(PolarCoords == 0);
LegendLabel = {'LHM','RHM'};
TitleLabel = 'LRHM';
end
% Plot mean BETA value for each coordinates ROI
subplot(3,2,2*ani); hold on
if exist('e','var'); clear e; end
% Mean β ± 95% CI across contrasts
for i = 1:size(PolarIdx,1)
pos = abs(squeeze(Data_VFrois_1ROI(subs,CSFCondition,:,PolarIdx(i,:))) - squeeze(CIData_VFrois_1ROI(subs,CSFCondition,:,PolarIdx(i,:),2)));
neg = abs(squeeze(Data_VFrois_1ROI(subs,CSFCondition,:,PolarIdx(i,:))) - squeeze(CIData_VFrois_1ROI(subs,CSFCondition,:,PolarIdx(i,:),1)));
e(i) = errorbar(sqrt(C),squeeze(Data_VFrois_1ROI(subs,CSFCondition,:,PolarIdx(i,:))),neg,pos,MarkerType{CSFCondition},'LineWidth',1,'Color',colour(i,:));
% Overlay fit using pooled V1-wide parameters
C_fit = linspace(min(C), max(C), 100);
func = @(x, C) x(1) + x(2)*C.^x(3); % linear fit
R_fit = func(squeeze(FitParams_VFrois_1ROI(subs,CSFCondition,PolarIdx(i,:),:)), C_fit);
s(CSFCondition) = plot(sqrt(C_fit), R_fit,lineStyle{CSFCondition},'Color',colour(i,:),'LineWidth',2);
end
% Axis Parameters
ylabel(VariableLabel);
legend(e,LegendLabel,'Location','NorthWest');
xlabel('Contrast (%)');% xlabel('Contrast^(^0^.^5^)');
xlim([0 max(C)]); % From 0-1 (normalised values)
xticks(sqrt(C));
xticklabels(num2str(C*100));
ylim(YAxisLim); % ylim([floor(min(min(Beta_VFrois))) ceil(max(max(Beta_VFrois)))])
set(gca,'FontSize',14,'LineWidth',2,'box','off','XTickLabelRotation',45)
axis square;
title([TitleLabel ': ' num2str(ECCBoundaries(1)) ' – ' num2str(ECCBoundaries(end)) 'º'])
end
end
end % if cardinals present
end % CSFCondition loop
%% -------------------------------------------------------------
% 7.5 Polar Angle — Back-project slopes per bin & polar coord
% -------------------------------------------------------------
% Paints fitted slope (b) values per (bin, polar) patch.
% -------------------------------------------------------------
if strcmp(answer,'Low & High SF') || strcmp(answer3,'Quadrant')
% Build a dense response vector per vertex from ROI fits
if PlotInd
idxCoords = 0;
Response = zeros(size(BetaVals_ECC,1),length(pRFx_y_ECC));
for bin = 1:length(Radius_seedroi)
for coord = 1:length(PolarCoords) % Will ignore the added values if Cardinals
idxCoords = idxCoords+1;
for CSFCondition = 1:size(BetaVals_ECC,1)
Response(CSFCondition,vtxidx(idxCoords).inclvtx) = squeeze(FitParams_VFrois(subs,CSFCondition,bin,coord,2));
end
end
end
idxFigs = idxFigs+1;
figure(idxFigs);
set(gcf,'Color','w','Position',[0 0 Rect(3)/2 Rect(4)/2]);
[th,r] = cart2pol(x_ycoords0(:,1),x_ycoords0(:,2));
for CSFCondition = 1:size(BetaVals_ECC,1)
% Arrange slope values per (polar, ecc) in matrix form
Data = [];
Data = squeeze(FitParams_VFrois(subs,CSFCondition,:,1:length(PolarCoords),2)); % [bin × polar × param(b)]
Data = Data'; % [Polar x Eccentricity]
Data(isnan(Data)) = 0; % treat missing as 0 slope
subplot(1,size(BetaVals_ECC,1),CSFCondition)
% Draw wedge patches for each seed
for coord = 1:length(x_ycoords0) % LHM, RHM, UVM, LVM
% This function creates a patch from polar coordinates
th0 = th(coord)-a_search_rad;
rh0 = r(coord)-r_search(coord);
th1 = th(coord) + a_search_rad;
rh1 = r(coord)+r_search(coord);
a1 = linspace(th0,th0);
r1 = linspace(rh0,rh1);
a2 = linspace(th0,th1);
r2 = linspace(rh1,rh1);
a3 = linspace(th1,th1);
r3 = linspace(rh1,rh0);
a4 = linspace(th1,th0);
r4 = linspace(rh0,rh0);
[X,Y]=pol2cart([a1,a2,a3,a4],[r1,r2,r3,r4]);
s2vf(subs,CSFCondition,coord) = patch(X,Y,Data(coord)); % Note: patch function takes text or matrix color def
end
axis([-Eccentricity Eccentricity -Eccentricity Eccentricity])
axis square;
xticks([-Eccentricity:Eccentricity/2:Eccentricity])
yticks([-Eccentricity:Eccentricity/2:Eccentricity])
ax = gca;
ax.Color = colorBck;
ax.AmbientLightColor = [0 0 0];
title(strcat(num2str(SFLabels(CSFCondition)),'cpd'))
colormap(colorMap);
cb = colorbar; % show color bar
cb.Ticks = ceil(linspace(0,max(CLim),4));
ylabel(cb,['Slope'],'Rotation',270,'Position',[4 max(CLim)/2 0]);
set(gca,'FontSize',14,'LineWidth',2,'CLim',CLim);
end
end
end
%% -------------------------------------------------------------
% 7.6 Polar Angle — Back-project (1 ROI, V1-wide)
% -------------------------------------------------------------
% For Low & High SF, pool across eccentricity and paint a
% single wedge per cardinal meridian using the V1-wide slopes.
% -------------------------------------------------------------
if strcmp(answer,'Low & High SF')
if PlotInd
% Ensure PolarCoords(1) is positive for indexing below
if PolarCoords(1) < 0; PolarCoords(1) = abs(PolarCoords(1)); end
Response_1ROI = NaN(size(BetaVals_ECC,1),length(pRFx_y_ECC));
% Assign pooled (across ecc) slope per meridian to all vertices in that meridian
for coord = 1:length(PolarCoords) % Will ignore the added values for HVA (5) and VMA (6) if Cardinals
idxCoords = find([vtxidx.seed_PolarRad] == PolarCoords(coord));
for CSFCondition = 1:size(BetaVals_ECC,1)
tmp = []; tmp = {vtxidx(idxCoords).inclvtx};
Response_1ROI(CSFCondition,vertcat(tmp{:})) = nanmean(squeeze(FitParams_VFrois(subs,CSFCondition,:,coord,2)),1);
end
end
idxFigs = idxFigs+1;
figure(idxFigs);
set(gcf,'Color','w','Position',[0 0 Rect(3)/2 Rect(4)/2]);
[th_1ROI,r_1ROI] = cart2pol(x_ycoords0_1ROI(:,1),x_ycoords0_1ROI(:,2));
for CSFCondition = 1:size(BetaVals_ECC,1)
% Pooled slope per polar (mean across bins)
Data = [];
Data = nanmean(squeeze(FitParams_VFrois(subs,CSFCondition,:,1:length(PolarCoords),2)),1);
Data = Data'; % [Polar x Eccentricity]
subplot(1,size(BetaVals_ECC,1),CSFCondition)
% Draw a single wedge per cardinal meridian
for coord = 1:length(x_ycoords0_1ROI) % LHM, RHM, UVM, LVM
% This function creates a patch from polar coordinates
th0 = th_1ROI(coord)-a_search_rad;
rh0 = r_1ROI(coord)-r_search_1ROI(coord);
th1 = th_1ROI(coord) + a_search_rad;
rh1 = r_1ROI(coord)+r_search_1ROI(coord);
a1 = linspace(th0,th0);
r1 = linspace(rh0,rh1);
a2 = linspace(th0,th1);
r2 = linspace(rh1,rh1);
a3 = linspace(th1,th1);
r3 = linspace(rh1,rh0);
a4 = linspace(th1,th0);
r4 = linspace(rh0,rh0);
[X,Y]=pol2cart([a1,a2,a3,a4],[r1,r2,r3,r4]);
s2vf(subs,CSFCondition,coord) = patch(X,Y,Data(coord)); % Note: patch function takes text or matrix color def
end
axis([-Eccentricity Eccentricity -Eccentricity Eccentricity])
axis square;
xticks([-Eccentricity:Eccentricity/2:Eccentricity])
yticks([-Eccentricity:Eccentricity/2:Eccentricity])
ax = gca;
ax.Color = colorBck;
title(strcat(num2str(SFLabels(CSFCondition)),'cpd'))
colormap(colorMap);
cb = colorbar; % show color bar
cb.Ticks = ceil(linspace(0,max(CLim),4));
ylabel(cb,['Slope'],'Rotation',270,'Position',[4 max(CLim)/2 0]);
set(gca,'FontSize',14,'LineWidth',2,'CLim',CLim);
end
% Restore PolarCoords sign if modified
if PolarCoords(1) > 0; PolarCoords(1) = -PolarCoords(1); end
end
end
end
end % if 'Low & High SF' || exist(answer3)
end % SECTION 7
%% =========================================================================
% SECTION 8 — GROUP LEVEL (ONLY for "Low & High SF")
% -------------------------------------------------------------------------
% This section computes group-level summaries and figures if:
% • At least 3 subjects were selected
% • The chosen condition is 'Low & High SF'
%
% It produces:
% • Group mean cortical contrast sensitivity per eccentricity bin (with 95% CI and subject overlays)
% • Group back-projection of eccentricity-binned slopes (b) to visual space
% • Group anisotropy plots (HVA/VMA/LRHM) from V1-wide pooled ROIs
% • Group back-projection of V1-wide polar slopes to visual space
%
% Shapes reminder of inputs aggregated here:
% Data_ECC(sub, bin, SF, contrast)
% FitParams_ECC(sub, SF, bin, [a b p])
%
% Data_VFrois(sub, SF, contrast, bin, polar)
% FitParams_VFrois(sub, SF, bin, polar, [a b p])
%
% Data_VFrois_1ROI(sub, SF, contrast, polar)
% FitParams_VFrois_1ROI(sub, SF, polar, [a b p])
% =========================================================================
if length(unique(selectfn(indx))) >= 3 & strcmp(answer,'Low & High SF')
idxFigs = idxFigs+1;
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
disp('%%%%%%%%%%%%%%%%%%%%%%%% GROUP LEVEL %%%%%%%%%%%%%%%%%%%%%%%%')
disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
% Containers for group means and CIs
MeanBeta_VFrois = []; MeanCIBeta_VFrois = [];
MeanBeta_VFrois_1ROI = []; MeanCIBeta_VFrois_1ROI = [];
MeanFitParams_VFrois = []; MeanFitParams_VFrois_1ROI = [];
MeanFitParams_ECC = [];
% Use the specific contrast set selected for plotting at group level
C = ContrastToPlot'/100;
%% ---------------------------------------------------------------------
% 8.1 Group summaries for Polar ROIs across eccentricity bins
% ----------------------------------------------------------------------
for CSFCondition = 1:size(Data_VFrois,2)
% Grand mean across subjects
% Data_VFrois: [sub, SF, contrast, bin, polar]
MeanBeta_VFrois(CSFCondition,:,:,:) = squeeze(nanmean(Data_VFrois(:,CSFCondition,:,:,:),1));
MeanBeta_VFrois_1ROI(CSFCondition,:,:) = squeeze(nanmean(Data_VFrois_1ROI(:,CSFCondition,:,:),1));
MeanFitParams_VFrois(CSFCondition,:,:,:) = squeeze(nanmean(FitParams_VFrois(:,CSFCondition,:,:,:),1));
MeanFitParams_VFrois_1ROI(CSFCondition,:,:) = squeeze(nanmean(FitParams_VFrois_1ROI(:,CSFCondition,:,:),1));
% Bootstrap CI across subjects for each (contrast, bin, polar)
for bin = 1:length(Radius_seedroi)
for c = 1:length(ContrastToPlot)
for coord = 1:size(Data_VFrois,5)
% Extract subject samples for this cell
if sum(isnan(Data_VFrois(:,CSFCondition,c,bin,coord))) >= 3
MeanCIBeta_VFrois(CSFCondition,c,bin,coord,:) = NaN;
else
MeanCIBeta_VFrois(CSFCondition,c,bin,coord,:) = squeeze(bootci(1000,@nanmean,Data_VFrois(:,CSFCondition,c,bin,coord)))';
end
end
end
end
end
%% ---------------------------------------------------------------------
% 8.2 Group summaries for V1-wide pooled ROIs (single-ROI)
% ----------------------------------------------------------------------
for CSFCondition = 1:size(Data_VFrois,2)
% Grand mean across subjects
% Data_VFrois_1ROI: [sub, SF, contrast, polar]
MeanBeta_VFrois_1ROI(CSFCondition,:,:) = squeeze(nanmean(Data_VFrois_1ROI(:,CSFCondition,:,:),1));
MeanFitParams_VFrois_1ROI(CSFCondition,:,:) = squeeze(nanmean(FitParams_VFrois_1ROI(:,CSFCondition,:,:),1));
% Bootstrap CI across subjects for each (contrast, bin, polar)
for bin = 1:length(Radius_seedroi)
for c = 1:length(ContrastToPlot)
for coord = 1:size(Data_VFrois_1ROI,4)
if sum(isnan(Data_VFrois_1ROI(:,CSFCondition,c,coord))) >= 3
MeanCIBeta_VFrois_1ROI(CSFCondition,c,coord,:) = NaN;
else
MeanCIBeta_VFrois_1ROI(CSFCondition,c,coord,:) = squeeze(bootci(1000,@nanmean,Data_VFrois_1ROI(:,CSFCondition,c,coord)))';
end
end
end
end
end
%% ---------------------------------------------------------------------
% 8.3 Group summaries for Eccentricity-binned cortical contrast sensitivity
% ----------------------------------------------------------------------
MeanCIBetaVals_ECC = []; % [SF, bin, contrast, CI=2]
if exist('s','var'); clear s; end
% Colour by SF condition (two-colour palette repeated if needed)
colour_ecc = repmat([0 0 255; 255 0 0]/255,round(size(Data_ECC,3)/2),1);
for CSFCondition = 1:size(Data_ECC,3)
disp(['... ' num2str(SFLabels(CSFCondition)) 'cpd: Linear Equation Fit (R = a+b*C.^p): Eccentricity bin level'])
% Prepare grouped figure canvas (grid by eccentricity bins)
if CSFCondition == 1
idxFigs = idxFigs+1;
ncols = 5;
[q,r] = quorem(sym(length(BinTick)),sym(ncols));
nlines = double(q);
if r > 0
nlines = nlines+1;
end
if nlines <= 4
figure(idxFigs); set(gcf,'Color','w','Position',[0 0 Rect(3) Rect(4)*nlines/4]);
else
figure(idxFigs); set(gcf,'Color','w','Position',Rect);
end
end
% Loop over eccentricity bins
for band = 1:length(BinECC)-1
% Group mean β across subjects (per contrast)
% Data_ECC: [sub, bin, SF, contrast]
R = []; R = squeeze(nanmean(Data_ECC(:,band,CSFCondition,:),1));
% Group CI across subjects for β
y = []; y = squeeze(Data_ECC(:,band,CSFCondition,:));
MeanCIBetaVals_ECC(CSFCondition,band,:,:) = bootci(1000,@nanmean,y)';
% Group mean fit params across subjects (a,b,p)
MeanFitParams_ECC(CSFCondition,band,:) = squeeze(nanmean(FitParams_ECC(:,CSFCondition,band,:),1));
% ---- Plot in corresponding bin subplot ----
hold on; subplot(nlines,ncols,band);
% Error bars from group CI
upper = []; upper = abs(R-squeeze(MeanCIBetaVals_ECC(CSFCondition,band,:,2)));
lower = []; lower = abs(R-squeeze(MeanCIBetaVals_ECC(CSFCondition,band,:,1)));
errorbar(sqrt(C),R,lower,upper,'Marker','o','LineStyle','none','LineWidth',1,'Color',colour_ecc(CSFCondition,:));
% Group fit overlay
hold on;
C_fit = linspace(min(C), max(C), 100);
func = @(x, C) x(1) + x(2)*C.^x(3); % linear fit
R_fit = func(MeanFitParams_ECC(CSFCondition,band,:), C_fit);
s(CSFCondition) = plot(sqrt(C_fit), R_fit,'-','Color',colour_ecc(CSFCondition,:),'LineWidth',2);
% Individual subject fits overlaid faintly
for subs = 1:size(FitParams_ECC,1)
R_fit_ind = func(FitParams_ECC(subs,CSFCondition,band,:), C_fit);
plot(sqrt(C_fit), R_fit_ind,'-','Color',[colour_ecc(CSFCondition,:) 0.2],'LineWidth',1);
end
% Formatting
if CSFCondition == length(unique(SFLabels))
legend([s],strcat(num2str(unique(SFLabels)),repmat('cpd',length(unique(SFLabels)),1)),'Location','NorthWest','box','off')
end
xlabel('Contrast (%)');% xlabel('Contrast^(^0^.^5^)');
xlim([0 max(C)]); % From 0-1 (normalised values)
xticks(sqrt(C));
xticklabels(num2str(C*100));
ylabel('\beta-value');
ylim([-5 15]); axis square;
title(num2str(SFLabels(CSFCondition)))
title([num2str(BinECC(band)) '–' num2str(BinECC(band+1)) 'º'])
set(gca,'LineWidth',2,'FontSize',14,'box','off')
end
end
%% ---------------------------------------------------------------------
% 8.4 Group back-projection to visual space — Eccentricity bins
% ----------------------------------------------------------------------
idxFigs = idxFigs+1;
figure(idxFigs);
set(gcf,'Color','w','Position', [0 0 Rect(3) Rect(4)/3])
[th,r] = cart2pol(x_ycoords0(:,1),x_ycoords0(:,2));
YAxisLim = [0 12]; % colour scale for slopes
for CSFCondition = 1:size(MeanFitParams_ECC,1)
% Mean slope (b) across eccentricity bins for this SF
R = []; R = squeeze(nanmean(MeanFitParams_ECC(CSFCondition,:,2),1)); % slope b only
subplot(1,size(MeanFitParams_ECC,1),CSFCondition)
idx = 0;
% Draw wedge patches; advance R index every 4 polar patches
for coord = 1:length(x_ycoords0) % LHM, RHM, UVM, LVM
th0 = th(coord)-a_search_rad;
rh0 = r(coord)-r_search(coord);
th1 = th(coord)+a_search_rad;
rh1 = r(coord)+r_search(coord);
a1 = linspace(th0,th0);
r1 = linspace(rh0,rh1);
a2 = linspace(th0,th1);
r2 = linspace(rh1,rh1);
a3 = linspace(th1,th1);
r3 = linspace(rh1,rh0);
a4 = linspace(th1,th0);
r4 = linspace(rh0,rh0);
[X,Y]=pol2cart([a1,a2,a3,a4],[r1,r2,r3,r4]);
if mod(coord,4) == 1 % Change Data value after each 4 coordinates (Left, Right, Up, Down)
idx = idx+1;
s_ecc_avg(CSFCondition,coord) = patch(X,Y,R(idx)); % Note: patch function takes text or matrix color def
else
s_ecc_avg(CSFCondition,coord) = patch(X,Y,R(idx)); % Note: patch function takes text or matrix color def
end
end
% Formatting
axis([-Eccentricity Eccentricity -Eccentricity Eccentricity])
axis square;
xticks([-Eccentricity:Eccentricity/2:Eccentricity])
yticks([-Eccentricity:Eccentricity/2:Eccentricity])
ax = gca;
ax.Color = [1 1 1];
title(strcat(num2str(SFLabels(CSFCondition)),'cpd'))
colormap(colorMap);
cb = colorbar; % show color bar
ylabel(cb,['Slope'],'Rotation',270,'Position',[4 YAxisLim(2)/2 0]);
set(gca,'FontSize',22,'LineWidth',2,'CLim',YAxisLim);
end
%% ---------------------------------------------------------------------
% 8.5 Group anisotropy plots — V1-wide pooled (HVA, VMA, LRHM)
% ----------------------------------------------------------------------
nlines = 1;
ncols = 3;
for CSFCondition = 1:size(MeanCIBeta_VFrois_1ROI,1)
% Open figure
idxFigs = idxFigs+1;
figure(idxFigs);
idxPrevious = 0;
set(gcf,'Color','w','Position', [0 0 Rect(3)*2/3 Rect(4)/2])
FontSize = 22;
YAxisLim = [-5 15];
if exist('s','var'); clear s; end
% Three panels: HVA, VMA, LRHM
for ani = 1:3
% Plot anisotropy data
PolarIdx = [];
% Choose ROI pairs, colours, labels, titles
if ani == 1
disp(['....... ' num2str(SFLabels(CSFCondition)) 'cpd: Horizontal versus Vertical Anisotropy '])
colour = [102 184 42; 128 0 128]/255; % Horizontal, Vertical
PolarIdx(1,:) = length(PolarCoords)+1;
PolarIdx(2,:) = length(PolarCoords)+2;
LegendLabel = {'HM','VM'};
TitleLabel = 'HVA';
elseif ani == 2
disp(['....... ' num2str(SFLabels(CSFCondition)) 'cpd: Vertical Meridians Anisotropy '])
colour = [0 0 255; 255 0 0]/255; % Upper, Lower
PolarIdx(1,:) = find(PolarCoords == pi/2);
PolarIdx(2,:) = find(PolarCoords == -pi/2);
LegendLabel = {'UVM','LVM'};
TitleLabel = 'VMA';
elseif ani == 3
disp(['....... ' num2str(SFLabels(CSFCondition)) 'cpd: Left-Right Horizontal Meridians Anisotropy '])
colour = [0 0 0; 128 128 128]/255; % Left, Right
PolarIdx(1,:) = find(PolarCoords == -pi);
PolarIdx(2,:) = find(PolarCoords == 0);
LegendLabel = {'LHM','RHM'};
TitleLabel = 'LRHM';
end
subplot(nlines,ncols,ani)
if exist('e','var'); clear e; end
for i = 1:size(PolarIdx,1)
hold on
% Mean ± 95% CI across subjects
R = squeeze(MeanBeta_VFrois_1ROI(CSFCondition,:,PolarIdx(i,:)))';
pos = abs(R - squeeze(MeanCIBeta_VFrois_1ROI(CSFCondition,:,PolarIdx(i,:),2))');
neg = abs(R - squeeze(MeanCIBeta_VFrois_1ROI(CSFCondition,:,PolarIdx(i,:),1))');
e(i) = errorbar(sqrt(C),R,neg,pos,'o','LineWidth',1,'Color',colour(i,:));
% Fit using group mean params
C_fit = linspace(min(C), max(C), 100);
func = @(x, C) x(1) + x(2)*C.^x(3); % linear fit
R_fit = func(MeanFitParams_VFrois_1ROI(CSFCondition,PolarIdx(i,:),:), C_fit);
s(CSFCondition) = plot(sqrt(C_fit), R_fit,'-','Color',colour(i,:),'LineWidth',2);
% Individual subjects (faint) for visual variability
for subs = 1:size(FitParams_VFrois_1ROI,1)
R_fit_ind = func(squeeze(FitParams_VFrois_1ROI(subs,CSFCondition,PolarIdx(i,:),:)), C_fit);
plot(sqrt(C_fit), R_fit_ind,'-','Color',[colour(i,:) 0.2],'LineWidth',1);
end
end
% Formatting
l = legend(e,LegendLabel,'Location','NorthWest');
ylabel(VariableLabel); ylim(YAxisLim);
xlabel('Contrast (%)');
xlim([0 max(C)]);
xticks(sqrt(C));
xticklabels(num2str(C*100));
axis square
title([TitleLabel ': ' num2str(ECCBoundaries(bin)) '–' num2str(ECCBoundaries(bin+1)) 'º']);
set(gca,'FontSize',FontSize,'LineWidth',2,'box','off','XTickLabelRotation',45)
end
end
%% ---------------------------------------------------------------------
% 8.6 Group back-projection to visual space — V1-wide polar
% ----------------------------------------------------------------------
idxFigs = idxFigs+1;
figure(idxFigs);
set(gcf,'Color','w','Position', [0 0 Rect(3) Rect(4)/3])
[th_1ROI,r_1ROI] = cart2pol(x_ycoords0_1ROI(:,1),x_ycoords0_1ROI(:,2));
YAxisLim = [0 12];
for CSFCondition = 1:size(MeanFitParams_VFrois_1ROI,1)
% Group mean slope (b) per cardinal meridian
Data = [];
Data = squeeze(MeanFitParams_VFrois_1ROI(CSFCondition,:,2))';
% Data needs to be length(PolarCoords)*length(Radius_seedroi)
% Draw a wedge per meridian (pooled across eccentricities)
subplot(1,size(MeanFitParams_VFrois_1ROI,1),CSFCondition)
for coord = 1:length(x_ycoords0_1ROI) % LHM, RHM, UVM, LVM
% This function creates a patch from polar coordinates
th0 = th_1ROI(coord)-a_search_rad;
rh0 = r_1ROI(coord)-r_search_1ROI(coord);
th1 = th_1ROI(coord)+a_search_rad;
rh1 = r_1ROI(coord)+r_search_1ROI(coord);
a1 = linspace(th0,th0);
r1 = linspace(rh0,rh1);
a2 = linspace(th0,th1);
r2 = linspace(rh1,rh1);
a3 = linspace(th1,th1);
r3 = linspace(rh1,rh0);
a4 = linspace(th1,th0);
r4 = linspace(rh0,rh0);
[X,Y]=pol2cart([a1,a2,a3,a4],[r1,r2,r3,r4]);
s_ecc_avg(CSFCondition,coord) = patch(X,Y,Data(coord));
end
% Formatting
axis([-Eccentricity Eccentricity -Eccentricity Eccentricity])
axis square;
xticks([-Eccentricity:Eccentricity/2:Eccentricity])
yticks([-Eccentricity:Eccentricity/2:Eccentricity])
ax = gca;
ax.Color = [1 1 1];
title(strcat(num2str(SFLabels(CSFCondition)),'cpd'))
colormap(colorMap);
cb = colorbar;
ylabel(cb,['Slope'],'Rotation',270,'Position',[4 YAxisLim(2)/2 0]);
set(gca,'FontSize',22,'LineWidth',2,'CLim',YAxisLim);
end
end % GROUP LEVEL

BackProjection_Slopes_Controls.m at commit 0014b8f, under MIT · at the source

Overview

Authors: Hugo T Chow-Wing-Bom^1,2, Matteo Lisi³, Noah C Benson⁴, Freya Lygo-Frett⁵, Patrick Yu-Wai-Man^1,6,7,8, Frederic Dick^2,9, Roni O Maimon-Mor¹, Tessa M Dekker^1,2,9

ORCID iDs: Hugo T Chow-Wing-Bom, Noah C Benson, Frederic Dick, Roni O Maimon-Mor, Tessa M Dekker

Institutions: University College London (United Kingdom); Birkbeck, University of London (United Kingdom); University of London (United Kingdom); Royal Holloway University of London (United Kingdom); University of Washington (United States); Moorfields Eye Hospital NHS Foundation Trust (United Kingdom); University of Cambridge (United Kingdom); Cambridge University Hospitals NHS Foundation Trust (United Kingdom); Addenbrooke's Hospital (United Kingdom); MRC Mitochondrial Biology Unit (United Kingdom)

Journal: eLife, volume 14, article RP105930

Dates: published online 2 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.105930 · PMID 42826108 · PMCID PMC13633074 · OpenAlex W4409001708

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.7554/elife.105930.2

Status: code verified (how the code is found and verified)

Categories: fMRI (modality), human (organism), systems (subfield)

Methods: Connectivity, fMRI & imaging, Physiology & signal measures, Preprocessing

Keywords: Human

MeSH: Brain Mapping*, Contrast Sensitivity*, Magnetic Resonance Imaging*, Primary Visual Cortex*, Visual Fields*, Adult, Female, Humans, Male, Middle Aged, Scotoma, Young Adult (* major topic)

Topic: Visual perception and processing mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Wellcome Trust (10.35802/306332); Moorfields Eye Charity (PhD Studentship GR001315)

Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Peripheral vision is crucial for daily activities and quality of life, yet traditional measures of visual function like visual acuity primarily assess central vision. Visual field tests can evaluate peripheral vision but require extended focus and precise fixation, often challenging for patients with severe sight loss. Functional MRI (fMRI) with population receptive field (pRF) mapping offers a non-invasive way to map scotomas but relies on single contrast levels and accurate fixation. We developed an fMRI-based approach to measure contrast sensitivity across the visual field without requiring precise fixation. Combining large-field stimulation with varying spatial frequencies and contrast levels, and either pRF mapping or a structure-based retinotopic atlas, we modeled contrast sensitivity in the primary visual cortex (V1) over a large (40 deg) expanse of the visual field. In seven normally sighted participants, we characterized differences in V1 sensitivity across eccentricities and visual quadrants, finding reliable and reproducible patterns at individual and session levels. To test robustness to fixation variability, we investigated how varying levels of eye movement affected V1 sensitivity patterns in two participants: cortical sensitivity patterns were largely preserved despite eye movements, particularly at low spatial frequencies. This suggests our approach can accommodate several degrees of fixation instability, making it suitable for populations with unstable or biased fixation for whom visual field maps are harder to acquire behaviorally (e.g., patients with dense central scotoma or strabismus). Additionally, our method effectively visualized simulated and disease-linked sensitivity loss at the cortical level. Crucially, these results could be largely recovered using the structure-based retinotopic atlas, eliminating the need for pRF mapping and precise fixation, although with reduced sensitivity. This approach, integrating large-field stimulation with a retinotopic atlas, offers a promising tool for monitoring vision loss and recovery across a range of visual impairments, addressing a significant challenge in current clinical assessments.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

HugoCWB/ChowWingBomEtAl_2025_eLife

License: MIT

State: the link answers, verified on 5 October 2026

Evidence: files inventoried

Commit: 0014b8fa0797ece45b1bf5cadca99a20e4999f25, 16 April 2026

Languages: MATLAB (4)

Size: 6 files, 4 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file

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

Tools: Statistics and Machine Learning Toolbox (3 files), Optimization Toolbox (1 file)

Availability: 1 check, the latest on 5 October 2026: the link answers

5 October 2026: the link answers

6 files

BackProjection_Slopes_Controls.m, MATLAB, 1,606 lines, 4 matches
BackProjection_Slopes_Patient.m, MATLAB, 655 lines, 3 matches
Reliability_Slopes.m, MATLAB, 150 lines
dolinearfit_SqCdata.m, MATLAB, 102 lines
LICENSE, License, 21 lines
README.md, Text, 72 lines

At the source: github.com/HugoCWB/ChowWingBomEtAl_2025_eLife

hugocwb/adjustecctool

License: MIT

State: the link answers, verified on 5 October 2026

Evidence: files inventoried

Commit: 85bba2af3530612050f0166f20343594395e3f30, 25 March 2026

Languages: Python (2)

Size: 7 files, 2 scripts

Software Heritage: not archived

Found in: the references

Holds: README, license file, environment (Dockerfile, pyproject.toml)

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

Tools: NumPy (2 files), Matplotlib (1 file)

Availability: 1 check, the latest on 5 October 2026: the link answers

5 October 2026: the link answers

4 files

src/eccen_adjust/__init__.py, Python, 396 lines, 3 matches
src/eccen_adjust/__main__.py, Python, 120 lines
LICENSE, License, 21 lines
README.md, Text, 117 lines

At the source: archive.softwareheritage.org/swh:1:dir:040551a10851989858bc085f62241cb838b26024;origin=https://github.com/HugoCWB/AdjustEccTool;visit=swh:1:snp:a1104936c5c1c1b0ac9711b6ad406e7273cd14a9;anchor=swh:1:rev:85bba2af3530612050f0166f20343594395e3f30

Zenodo 18770065

License: GPL-3.0

State: the link answers, verified on 5 October 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data availability”

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

Availability: 1 check, the latest on 5 October 2026: the link answers (HTTP 200)

5 October 2026: the link answers (HTTP 200)

At the source: doi.org/10.5281/zenodo.18770065

The paper's code and data availability statement is in the Data section.

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:

3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
6 scripts, each with its path and the digest of its content;
10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

zenodo:19051439, at Zenodo; found in “Data availability”

Data availability

All post-processed and anonymized data are available on Zenodo (https://doi.org/10.5281/zenodo.19051439). Codes to reproduce the figures from the provided dataset are available on GitHub (https://github.com/HugoCWB/ChowWingBomEtAl_2025_eLife, copy archived at Chow-Wing-Bom, 2026b). Codes to adjust the Benson maps' eccentricity are available on GitHub (https://github.com/HugoCWB/AdjustEccTool, copy archived at Chow-Wing-Bom and Benson, 2026a). A Docker image for the Benson eccentricity adjustment toolbox is also available on Zenodo (https://doi.org/10.5281/zenodo.18770065). The original raw data cannot be shared publicly because of participant consent and ethical approval restrictions. Access can be requested by emailing the corresponding author, Dr Hugo Chow-Wing-Bom, or the senior author, Prof Tessa Dekker. Requests should include a brief project proposal and will be assessed by Prof Dekker and the study team. Access will be granted subject to a data sharing agreement between the requesting organisation and UCL.

The following datasets were generated:

Chow-Wing-Bom H, Lisi M, Benson N, Lygo-Frett F, Yu-Wai-Man P, Dick F, Maison-Mor R, Dekker T. 2026. Dataset for Chow-Wing-Bom et al. 2025, eLife. Zenodo.

Chow-Wing-Bom H, Benson N. 2026. Benson Map Eccentricity Adjustment Tool. Zenodo.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 6 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 1 keyword, 12 MeSH terms, 2 funders, 48 references.

Cite

This paper

Chow-Wing-Bom, H. T., Lisi, M., Benson, N. C., Lygo-Frett, F., Yu-Wai-Man, P., Dick, F., Maimon-Mor, R. O., & Dekker, T. M. (2026). Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI. eLife, 14, RP105930. https://doi.org/10.7554/elife.105930

BibTeX

@article{chowwingbom2026mapping, author = {Chow-Wing-Bom, Hugo T and Lisi, Matteo and Benson, Noah C and Lygo-Frett, Freya and Yu-Wai-Man, Patrick and Dick, Frederic and Maimon-Mor, Roni O and Dekker, Tessa M}, title = {{Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI}}, journal = {eLife}, year = {2026}, month = oct, volume = {14}, pages = {RP105930}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.105930}, url = {https://doi.org/10.7554/elife.105930}, pmid = {42826108}, pmcid = {PMC13633074} }

RIS

TY - JOUR AU - Chow-Wing-Bom, Hugo T AU - Lisi, Matteo AU - Benson, Noah C AU - Lygo-Frett, Freya AU - Yu-Wai-Man, Patrick AU - Dick, Frederic AU - Maimon-Mor, Roni O AU - Dekker, Tessa M TI - Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI T2 - eLife J2 - eLife PY - 2026 DA - 2026/10/02 VL - 14 SP - RP105930 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.105930 UR - https://doi.org/10.7554/elife.105930 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.105930", "type": "article-journal", "title": "Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI", "container-title": "eLife", "author": [ { "family": "Chow-Wing-Bom", "given": "Hugo T" }, { "family": "Lisi", "given": "Matteo" }, { "family": "Benson", "given": "Noah C" }, { "family": "Lygo-Frett", "given": "Freya" }, { "family": "Yu-Wai-Man", "given": "Patrick" }, { "family": "Dick", "given": "Frederic" }, { "family": "Maimon-Mor", "given": "Roni O" }, { "family": "Dekker", "given": "Tessa M" } ], "container-title-short": "eLife", "volume": "14", "page": "RP105930", "DOI": "10.7554/elife.105930", "PMID": "42826108", "PMCID": "PMC13633074", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.105930", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 2 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1101/2024.10.29.619403 [code]: Mapping Visual Contrast Sensitivity and Vision Loss Across the Visual Field with Model-Based fMRI
Journal: bioRxiv (preprint)
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, Matplotlib, 1 other tool, Zenodo 19051439, fMRI, 31 references, 5 authors
[2] doi:10.1038/s41467-022-31041-9 [code]: Linking individual differences in human primary visual cortex to contrast sensitivity around the visual field.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, systems, 12 references
[3] doi:10.1371/journal.pcbi.1013599 [code]: Unpacking the V1 map: Differential covariation of preferred spatial frequency and cortical magnification across spatial dimensions.
Journal: PLoS computational biology
In common: Matplotlib, NumPy, fMRI, systems, 11 references
[4] doi:10.34133/csbj.0042 [code]: Using Steady-State Visual Evoked Potentials to Characterize Wide-Ranging Retinopathy Linked to <i>CRB1</i>: Implications for Clinical Trials.
Journal: Computational and structural biotechnology journal
In common: Matplotlib, NumPy, 3 references, 2 authors
[5] doi:10.7554/elife.84205 [code]: Cortical magnification eliminates differences in contrast sensitivity across but not around the visual field.
Journal: eLife
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, 9 references
[6] doi:10.1038/s41467-023-37280-8 [code]: Comparing retinotopic maps of children and adults reveals a late-stage change in how V1 samples the visual field.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, Matplotlib, NumPy, systems, 8 references
[7] doi:10.1101/2025.11.27.690210 [code]: Predicting functional topography of the human visual cortex from cortical anatomy at scale
Journal: bioRxiv (preprint)
In common: Matplotlib, NumPy, systems, 5 references, author Noah C Benson
[8] doi:10.1002/hbm.70140 [code]: Biases in Volumetric Versus Surface Analyses in Population Receptive Field Mapping.
Journal: Human brain mapping
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, Matplotlib, 1 other tool, fMRI, systems, 6 references
[9] doi:10.1038/s41467-025-63795-3 [code]: Spatial attention selectively alters visual cortical representation during target anticipation.
Journal: Nature communications
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, Matplotlib, 1 other tool, 7 references
[10] doi:10.1073/pnas.2507810122: Visual adaptation stronger at the horizontal than the vertical meridian: Linking performance with V1 cortical surface area.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: 9 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.7554/elife.105930.svg)](https://openscicode.org/badge/doi/10.7554/elife.105930)
<!-- oscr-badge:end -->

reStructuredText

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.7554/elife.105930.svg
   :target: https://openscicode.org/badge/doi/10.7554/elife.105930
   :alt: OSCR

.. oscr-badge:end

HTML

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.7554/elife.105930"><img src="https://openscicode.org/badge/doi/10.7554/elife.105930.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

HugoCWB/ChowWingBomEtAl_2025_eLife: README.md on GitHub

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.

Mapping human visual contrast sensitivity and vision loss across the visual field with model-based fMRI.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

HugoCWB/ChowWingBomEtAl_2025_eLife

hugocwb/adjustecctool

Zenodo 18770065

Tracing map

Data

Datasets cited

Data availability

Versions

Version 1, 6 October 2026: the first record

Cite

This paper

Similar papers

Contribute

Claim this paper

Correct its record

Validate its tracing map

Add the badge to the code's README

Request its removal

Discussion, reproductions, activity