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MOT_ANA_ComputeParameters_DS.m
09f7a984c7ffmaster

MOT_ANA_ComputeParameters_DS.m
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			% Motion_Data: size T x 6 x n_subjects x n_sessions
			% TemporalMask_All has size T x n_subjects x n_sessions, with 1s if
			% scrubbed out
			function [Motion_Matrix,Motion_Matrix_Pos,Motion_Matrix_Neg,Space_Labels,...
			Time_Labels,Session_Labels] = MOT_ANA_ComputeParameters_DS(Motion_Data,...
			TemporalMask_All,n_TB,DS_Factor)



			n_subjects = size(Motion_Data,3);
			n_sessions = size(Motion_Data,4);

			% Differential
			DeltaMot(2:1200,:,:,:) = diff(Motion_Data,1);
			DeltaMot(1,:,:,:) = zeros(6,n_subjects,n_sessions);




			% Number of samples available
			samples_session = size(Motion_Data,1);

			% Number of samples to include in each temporal bin
			dur = floor(samples_session/n_TB);



			% Retains only the moments of positive/negative motion change; this is to verify
			% that positive and negative-valued motion moments are equally frequent
			DMPos = DeltaMot;
			DMPos(DMPos < 0) = NaN;

			DMNeg = DeltaMot;
			DMNeg(DMNeg > 0) = NaN;



			% Time at which we stand (in number of samples) and associated index
			current_time = 1;

			% To fill with the spatiotemporal information
			Motion_Matrix = [];

			Motion_Matrix_Pos = [];
			Motion_Matrix_Neg = [];

			% As long as we still consider a time lying within the session
			% duration...
			while current_time <= n_TB * dur

			% Separately for positive and negative instantaneous changes, samples
			% the data and sums

			idx_sess = 1;

			for ses = 1:n_sessions

			idx_fill = 1;

			for s = 1:n_subjects
			tmp_pos(idx_fill,1+((idx_sess-1)6):6+((idx_sess-1)6)) = nanmean(DMPos(~TemporalMask_All(current_time:current_time+dur-1,s,ses),:,s,ses));
			tmp_neg(idx_fill,1+((idx_sess-1)6):6+((idx_sess-1)6)) = nanmean(abs(DMNeg(~TemporalMask_All(current_time:current_time+dur-1,s,ses),:,s,ses)));

			tmp_nonscrubbed(idx_fill,1+((idx_sess-1)6):6+((idx_sess-1)6)) = mean(abs(DeltaMot(~TemporalMask_All(current_time:current_time+dur-1,s),:,s,ses)));

			idx_fill = idx_fill + 1;
			end

			idx_sess = idx_sess + 1;
			end

			Motion_Matrix = [Motion_Matrix,tmp_nonscrubbed];

			% output data matrices are filled in appropriately: each column
			% highlights one spatiotemporal index

			Motion_Matrix_Pos = [Motion_Matrix_Pos,tmp_pos];
			Motion_Matrix_Neg = [Motion_Matrix_Neg,tmp_neg];

			current_time = current_time + dur;

			clear tmp_nonscrubbed
			clear tmp_neg
			clear tmp_pos
			end

			%%%%% Construction of data labels

			n_max = size(Motion_Matrix,2);



			% Space
			Space_Labels(1,:) = [1:6:n_max-5];

			for t = 2:6
			Space_Labels(t,:) = 1 + Space_Labels(t-1,:);
			end

			% Time
			Time_Labels(1,:) = [1:6*n_sessions];

			for t = 2:n_TB
			Time_Labels(t,:) = 6*n_sessions + Time_Labels(t-1,:);
			end

			% Sessions
			Slstart = [1:n_sessions*6:n_max];

			Session_Labels = Slstart;
			for a = 1:n_TB-1
			Session_Labels = [Session_Labels,Slstart+a];
			end

			for ses = 2:n_sessions
			Session_Labels(ses,:) = 6*(ses-1) + Session_Labels(1,:);
			end


			end

MOT_ANA/Matlab_Scripts/Functions/MOT_ANA_ComputeParameters_DS.m09f7a984c7ffmaster

MOT_ANA_ComputeParameters_DS.mView Options

MOT_ANA/Matlab_Scripts/Functions/MOT_ANA_ComputeParameters_DS.m
09f7a984c7ffmaster

MOT_ANA_ComputeParameters_DS.m
View Options