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get_exp_info_block

PURPOSE ^

get_exp_info.m

SYNOPSIS ^

This is a script file.

DESCRIPTION ^

 get_exp_info.m

 Specifies some basic properties of an fMRI experiment design matrix. The default
 version of this script accommodates a slightly mutated 2 factor design with
 multiple levels in each factor. However, the number of factors/levels that enter
 into the design matrix can easily be modified either at this stage or in simulate_model_protos.m

 This script was originally written to prepare for a music cognition experiment
 in which there were trials with primes and targets.  Some of the nomenclature
 for the calculations of prime durations has been retained.  Simply think of
 'notes' as 'events' in a sequence of priming events preceding a target.

 This script will also spit back estimates of the experiment duration.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 % get_exp_info.m
0002 %
0003 % Specifies some basic properties of an fMRI experiment design matrix. The default
0004 % version of this script accommodates a slightly mutated 2 factor design with
0005 % multiple levels in each factor. However, the number of factors/levels that enter
0006 % into the design matrix can easily be modified either at this stage or in simulate_model_protos.m
0007 %
0008 % This script was originally written to prepare for a music cognition experiment
0009 % in which there were trials with primes and targets.  Some of the nomenclature
0010 % for the calculations of prime durations has been retained.  Simply think of
0011 % 'notes' as 'events' in a sequence of priming events preceding a target.
0012 %
0013 % This script will also spit back estimates of the experiment duration.
0014 %
0015 
0016 % 2003, Petr Janata
0017 
0018 clear tinfo
0019 
0020 ntt = 0;
0021 
0022 % Note: In this example, Factor 1 refers to the prime type and Factor 2 refers
0023 % to an attribute of the target.  Other target attributes exist in relation to
0024 % the prime, but these are handled at the next level of model construction, not
0025 % at the level of trial type specification.
0026 %
0027 % If you are not interested in prime/target trial structure, it is easy to
0028 % eliminate one or the other element by forcing the durations of the respective
0029 % element to zero and then not including that element as a condition in the
0030 % model (in simulate_model_protos.m).
0031 
0032 ntt = ntt+1;
0033 tinfo.id{ntt} = 'F1L1_F2L1';  % Factor 1, Level 1; Factor 2 Level 1
0034 tinfo.num_trials(ntt) = 2;
0035 
0036 ntt = ntt+1;
0037 tinfo.id{ntt} = 'F1L1_F2L2';  % Factor 1, Level 1; Factor 2 Level 2
0038 tinfo.num_trials(ntt) = 2;
0039 
0040 ntt = ntt+1;
0041 tinfo.id{ntt} = 'F1L2_F2L1';  % Factor 1, Level 2; Factor 2 Level 1
0042 tinfo.num_trials(ntt) = 2;
0043 
0044 ntt = ntt+1;
0045 tinfo.id{ntt} = 'F1L2_F2L2';    % Factor 1, Level 2; Factor 2 Level 2
0046 tinfo.num_trials(ntt) = 2;
0047 
0048 ntt = ntt+1;
0049 tinfo.id{ntt} = 'F1L3_F2L1';    % Factor 1, Level 3; Factor 2 Level 1
0050 tinfo.num_trials(ntt) = 0;
0051 
0052 ntt = ntt+1;
0053 tinfo.id{ntt} = 'F1L3_F2L2';    % Factor 1, Level 3; Factor 2 Level 2
0054 tinfo.num_trials(ntt) = 0;
0055 
0056 ntt = ntt+1;
0057 tinfo.id{ntt} = 'F1L4_F2L1';    % Factor 1, Level 4; Factor 2 Level 1
0058 tinfo.num_trials(ntt) = 0;
0059 
0060 ntt = ntt+1;
0061 tinfo.id{ntt} = 'F1L4_F2L2';    % Factor 1, Level 4; Factor 2 Level 2
0062 tinfo.num_trials(ntt) = 0;
0063 
0064 ntt = ntt+1;
0065 tinfo.id{ntt} = 'Sil';            % Silence (blank trials)
0066 tinfo.num_trials(ntt) = 0; 
0067 
0068 trials_per_iteration = sum(tinfo.num_trials);
0069 
0070 num_iterations = 1;  % Number of iterations through the number of trials
0071                      % specified above
0072 
0073 nslices = 18;                % number of slices to acquire
0074 min_ms_per_slice = 100;  % The fastest sampling rate we can go at, i.e. 10 slices/s
0075 
0076 TR = nslices*min_ms_per_slice;        % The experiment's TR
0077 
0078 notes_per_seq = 1;  % Number of events in priming sequence
0079 ms_per_note = 30000;  % Duration (ms) of each event in priming sequence
0080 resp_period = 15000*2;  % Amount of time (ms) allowed for response to target
0081 prime_dur = (notes_per_seq-1)*ms_per_note;  % Duration of prime
0082 
0083 cue_dur = 500;  % Duration of trial type cue
0084 cue_stim_soa_range = [1000 2000];    % Range of asynchronies (ms) between
0085                                         % trial type cue and trial onset
0086 inter_trial_interval_range = [1*TR 3*TR]*0; % range of times between trials
0087 
0088 total_trials = trials_per_iteration * num_iterations
0089 stim_dur = notes_per_seq*ms_per_note + resp_period; % overall trial duration
0090 
0091 % Create randomized list of trial type cue to stimulus onset asynchronies
0092 cue_stim_soa_list = ...
0093     rand(1,total_trials)*diff(cue_stim_soa_range)+min(cue_stim_soa_range);
0094 trial_durs = cue_stim_soa_list + stim_dur;
0095 pure_stim_time = sum(trial_durs);
0096 
0097 % Create randomized list of inter-trial intervals
0098 iti_list = rand(1,total_trials)*diff(inter_trial_interval_range)+min(inter_trial_interval_range);
0099 total_jitter_time = sum(iti_list);
0100 
0101 total_time_s = (pure_stim_time+total_jitter_time)/1000
0102 total_time_min = total_time_s/60
0103 
0104 % Construct trial orders for each iteration
0105 
0106 trial_type_list = [];
0107 for itt = 1:ntt
0108   trial_type_list(end+1:end+tinfo.num_trials(itt)) = itt;
0109 end % for itt
0110 
0111 % Randomly permute the trial type list
0112 trial_list = trial_type_list(randperm(length(trial_type_list)));
0113 
0114 % Calculate trial onsets
0115 onsets = cumsum(iti_list + trial_durs)-(iti_list(1)+trial_durs(1));
0116 
0117 % Number of volumes collected for each iteration through the trials
0118 scans_per_iteration = ceil((total_time_s*1000/num_iterations)/TR)

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