analysis(matlab): convert previous (Forouzan) study to our long format
Add forouzan_load_data.m (joins raw/Data_Forouzan.csv + raw/Total_Success.csv) and make_forouzan_data.m, producing analysis/forouzan_reach_data.csv in our curated column format (subject, group, day, success, total). Group is mapped Anodal -> b2_f (tDCS), Control -> a2_f (control); Hand ignored; day 0-indexed (Session 1 = day 0 = paper 'Day 1'); early-stopped sessions (total=0) dropped. Conversion validated by refitting the paper's own LME on the converted data: interaction t(227)=2.66, F(1)=7.09, p=0.0083 -- reproduces the paper's reported t(227)=2.68, F(1)=7.12, p=0.008, N=24 (12 Anodal / 12 Control). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Experiments DB Dev
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function T = forouzan_load_data()
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%FOROUZAN_LOAD_DATA Load the previous (Forouzan) tDCS reaching study into our
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% long format, with group labels matching our convention.
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%
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% T = FOROUZAN_LOAD_DATA() reads two raw files under analysis/matlab/raw/:
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% Data_Forouzan.csv long, fill-down RatName; per rat/session: success,
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% failure, total (attempts). Sessions 1..10; rats that
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% stopped early have blank success and total = 0
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% (encoded #DIV/0!), which are dropped.
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% Total_Success.csv per-rat Group (Anodal | Control) and Hand.
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%
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% and returns a table with the same variables as TDCS_LOAD_DATA:
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% subject (string) rat name
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% group (categorical) 'b2_f' for Anodal (tDCS), 'a2_f' for Control
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% day (double) Session - 1, so day 0 = the paper's "Day 1"
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% (matching our 0-indexed day convention)
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% success (double) successful reaches in the session
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% total (double) total attempts in the session
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%
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% Hand is ignored per request. The "_f" suffix marks this as the Forouzan
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% (previous) dataset, distinct from our Electrode-Box-A2/B2 animals.
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thisDir = fileparts(mfilename('fullpath'));
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longFile = fullfile(thisDir, 'raw', 'Data_Forouzan.csv');
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grpFile = fullfile(thisDir, 'raw', 'Total_Success.csv');
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% --- group map: rat -> Anodal|Control -> b2_f|a2_f -----------------------
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G = readtable(grpFile, 'VariableNamingRule', 'preserve');
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ratNames = string(G{:, 1}); % col 1 = Rat_Name (BOM-safe by index)
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rawGroup = string(G{:, end}); % last col = Group (Anodal|Control)
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% Keep only the real Anodal/Control rows (drops any header/blank artifact that
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% readtable mis-parses as data because of the numeric 0..9 column headers).
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keep = ismember(rawGroup, ["Anodal", "Control"]);
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ratNames = ratNames(keep);
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rawGroup = rawGroup(keep);
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label = repmat("a2_f", numel(ratNames), 1);
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label(rawGroup == "Anodal") = "b2_f"; % Control -> a2_f (default), Anodal -> b2_f
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grpMap = containers.Map(cellstr(ratNames), cellstr(label));
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% --- parse the long success/total file (fill-down RatName) ---------------
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lines = readlines(longFile);
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lines = lines(strlength(strip(lines)) > 0);
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lines(1) = erase(lines(1), char(65279)); % strip UTF-8 BOM from the header
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subj = strings(0, 1); day = []; success = []; total = [];
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curRat = "";
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for i = 2:numel(lines) % skip header row
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p = strsplit(lines(i), ',', 'CollapseDelimiters', false);
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name = strtrim(p(1));
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if strlength(name) > 0
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curRat = name;
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end
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suc = str2double(p(3));
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tot = str2double(p(5));
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if isnan(suc) || isnan(tot) || tot == 0 % no session that day -> drop
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continue
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end
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subj(end + 1, 1) = curRat; %#ok<AGROW>
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day(end + 1, 1) = str2double(p(2)) - 1; %#ok<AGROW> Session 1 -> day 0
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success(end + 1, 1) = suc; %#ok<AGROW>
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total(end + 1, 1) = tot; %#ok<AGROW>
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end
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% --- attach group and finalise -------------------------------------------
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grp = strings(numel(subj), 1);
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for i = 1:numel(subj)
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if ~isKey(grpMap, char(subj(i)))
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error('forouzan_load_data:missingRat', ...
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'Rat "%s" in Data_Forouzan.csv has no group in Total_Success.csv.', subj(i));
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end
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grp(i) = grpMap(char(subj(i)));
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end
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T = table(subj, categorical(grp), day, success, total, ...
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'VariableNames', {'subject', 'group', 'day', 'success', 'total'});
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T = sortrows(T, {'subject', 'day'});
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end
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@@ -0,0 +1,38 @@
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function make_forouzan_data()
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%MAKE_FOROUZAN_DATA Convert the previous (Forouzan) study to our long format.
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% MAKE_FOROUZAN_DATA() calls FOROUZAN_LOAD_DATA and writes the curated long
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% table to analysis/forouzan_reach_data.csv (same columns as our
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% analysis/tdcs_reach_data.csv: subject, group, day, success, total), with
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% group in {a2_f (Control), b2_f (Anodal)}. It then prints a descriptives
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% summary and, as a conversion sanity-check, fits the paper's LME
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% (behavior ~ stim + day + stim:day + (1|rat)) and compares the interaction
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% to the paper's reported values (t(227)=2.68, F(1)=7.12, p=0.008, N=24).
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thisDir = fileparts(mfilename('fullpath'));
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T = forouzan_load_data();
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outFile = fullfile(fileparts(thisDir), 'forouzan_reach_data.csv');
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writetable(T, outFile);
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fprintf('Wrote %s (%d rows)\n\n', outFile, height(T));
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fprintf('%-6s %6s %10s %12s %10s %8s\n', ...
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'group', 'nRats', 'nSessions', 'mean_success', 'mean_rate', 'max_day');
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for g = categories(T.group)'
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r = T.group == g{1};
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fprintf('%-6s %6d %10d %12.1f %10.3f %8d\n', g{1}, ...
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numel(unique(T.subject(r))), sum(r), mean(T.success(r)), ...
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sum(T.success(r)) / sum(T.total(r)), max(T.day(r)));
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end
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% --- conversion sanity-check: the paper's own LME on the converted data ---
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tbl = table(T.success, T.day, double(T.group == 'b2_f'), categorical(T.subject), ...
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'VariableNames', {'behavior', 'day', 'stim', 'rat'});
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m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
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C = m.Coefficients; A = anova(m);
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ii = strcmp(C.Name, 'day:stim'); ai = strcmp(A.Term, 'day:stim');
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fprintf(['\nPaper-LME check (behavior ~ stim + day + stim:day + (1|rat), N=%d):\n' ...
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' stim x day interaction: t(%d)=%.2f, F(1)=%.2f, p=%.4g\n' ...
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' paper reported: t(227)=2.68, F(1)=7.12, p=0.008\n'], ...
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numel(unique(tbl.rat)), C.DF(ii), C.tStat(ii), A.FStat(ai), C.pValue(ii));
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end
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