Others¶

neurora.stuff module¶

a module for some simple but important processes

neurora.stuff.cluster_fdr_correct(p, p_threshold1, p_threshold2)¶

Cluster-wise FDR correction for fMRI RSA results

Parameters

• p (array) – The p-value map (3-D).

• p_threshold1 (string) – The voxel-wise p threshold.

• p_threshold2 (string) – The cluster-wise p threshold

Returns

clusterfdrp – The Cluster-wise FDR corrected p-value map.

Return type

array.

neurora.stuff.cluster_fwe_correct(p, p_threshold1, p_threshold2)¶

Cluster-wise FWE correction for fMRI RSA results

Parameters

• p (array) – The p-value map (3-D).

• p_threshold1 (string) – The voxel-wise p threshold.

• p_threshold2 (string) – The cluster-wise p threshold

Returns

clusterfwep – The Cluster-wise FWE corrected p-value map.

Return type

array.

neurora.stuff.clusterbased_permutation_1d_1samp_1sided(results, level=0, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=2, iter=1000)¶

1-sample & 1-sided cluster based permutation test for 2-D results

Parameters

• results (array) – A result matrix. The shape of results should be [n_subs, x]. n_subs represents the number of subjects.

• level (float. Default is 0.) – An expected value in null hypothesis. (Here, results > level)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 2.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

ps – The permutation test resultz, p-values. The shape of ps is [x]. The values in ps should be 0 or 1, which represent not significant point or significant point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_1d_1samp_2sided(results, level=0, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=2, iter=1000)¶

1-sample & 2-sided cluster based permutation test for 2-D results

Parameters

• results (array) – A result matrix. The shape of results should be [n_subs, x]. n_subs represents the number of subjects.

• level (float. Default is 0.) – An expected value in null hypothesis. (Here, results > level)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 2.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

ps – The permutation test resultz, p-values. The shape of ps is [x]. The values in ps should be 0 or 1 or -1, which represent not significant point or significantly greater point or significantly less point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_1d_1sided(results1, results2, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=2, iter=1000)¶

1-sample & 1-sided cluster based permutation test for 2-D results

Parameters

• results1 (array) – A result matrix under condition1. The shape of results1 should be [n_subs, x]. n_subs represents the number of subjects.

• results2 (array) – A result matrix under condition2. The shape of results2 should be [n_subs, x]. n_subs represents the number of subjects. (Here, results1 > results2)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 2.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

ps – The permutation test resultz, p-values. The shape of ps is [x]. The values in ps should be 0 or 1, which represent not significant point or significant point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_1d_2sided(results1, results2, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=2, iter=1000)¶

1-sample & 2-sided cluster based permutation test for 2-D results

Parameters

• results1 (array) – A result matrix under condition1. The shape of results1 should be [n_subs, x]. n_subs represents the number of subjects.

• results2 (array) – A result matrix under condition2. The shape of results2 should be [n_subs, x]. n_subs represents the number of subjects. (Here, results1 > results2)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 2.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

ps – The permutation test resultz, p-values. The shape of ps is [x]. The values in ps should be 0 or 1 or -1, which represent not significant point or significantly greater point or significantly less point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_2d_1samp_1sided(results, level=0, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=4, iter=1000)¶

1-sample & 1-sided cluster based permutation test for 2-D results

Parameters

• results (array) – A result matrix. The shape of results should be [n_subs, x1, x2]. n_subs represents the number of subjects.

• level (float. Default is 0.) – An expected value in null hypothesis. (Here, results > level)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 4.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value. The shape of p is [x1, x2]. The values in ps should be 0 or 1, which represent not significant point or significant point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_2d_1samp_2sided(results, level=0, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=4, iter=1000)¶

1-sample & 2-sided cluster based permutation test for 2-D results

Parameters

• results (array) – A result matrix. The shape of results should be [n_subs, x1, x2]. n_subs represents the number of subjects.

• level (float. Default is 0.) – A expected value in null hypothesis. (Here, results > level)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 4.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value. The shape of p is [x1, x2]. The values in ps should be 0 or 1 or -1, which represent not significant point or significantly greater point or significantly less point after cluster-based permutation test, respectively.

Return type

float

neurora.stuff.clusterbased_permutation_2d_1sided(results1, results2, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=4, iter=1000)¶

1-sided cluster based permutation test for 2-D results

Parameters

• results1 (array) – A result matrix under condition1. The shape of results1 should be [n_subs, x1, x2]. n_subs represents the number of subjects.

• results2 (array) – A result matrix under condition2. The shape of results2 should be [n_subs, x1, x2]. n_subs represents the number of subjects. (Here, results1 > results2)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 4.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value. The shape of p is [x1, x2]. The values in ps should be 0 or 1, which represent not significant point or significant point after cluster-based permutation test.

Return type

float

neurora.stuff.clusterbased_permutation_2d_2sided(results1, results2, p_threshold=0.05, clusterp_threshold=0.05, n_threshold=4, iter=1000)¶

2-sided cluster based permutation test for 2-D results

Parameters

• results1 (array) – A result matrix under condition1. The shape of results1 should be [n_subs, x1, x2]. n_subs represents the number of subjects.

• results2 (array) – A result matrix under condition2. The shape of results2 should be [n_subs, x1, x2]. n_subs represents the number of subjects. (Here, results1 > results2)

• p_threshold (float. Default is 0.05.) – The threshold of p-values.

• clusterp_threshold (float. Default is 0.05.) – The threshold of cluster-defining p-values.

• n_threshold (int. Default is 4.) – The threshold of number of values in one cluster (number of values per cluster > n_threshold).

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value. The shape of p is [x1, x2]. The values in ps should be 0 or 1 or -1, which represent not significant point or significantly greater point or significantly less point after cluster-based permutation test.

Return type

float

neurora.stuff.correct_by_threshold(img, threshold)¶

correct the fMRI RSA results by threshold

Parameters

• img (array) – A 3-D array of the fMRI RSA results. The shape of img should be [nx, ny, nz]. nx, ny, nz represent the shape of the fMRI-img.

• threshold (int) – The number of voxels used in correction. If threshold=n, only the similarity clusters consisting more than n voxels will be visualized.

Returns

img – A 3-D array of the fMRI RSA results after correction. The shape of img should be [nx, ny, nz]. nx, ny, nz represent the shape of the fMRI-img.

Return type

array

neurora.stuff.datamask(fmri_data, mask_data)¶

filter the data by a ROI mask

Parameters

• fmri_data (array) – The fMRI data. The shape of fmri_data is [nx, ny, nz]. nx, ny, nz represent the size of the fMRI data.

• mask_data (array) – The mask data. The shape of mask_data is [nx, ny, nz]. nx, ny, nz represent the size of the fMRI data.

Returns

newfmri_data – The new fMRI data. The shape of newfmri_data is [nx, ny, nz]. nx, ny, nz represent the size of the fMRI data.

Return type

array

neurora.stuff.fdr_correct(p, p_threshold)¶

FDR correction for fMRI RSA results

Parameters

• p (array) – The p-value map (3-D).

• p_threshold (string) – The p threshold.

Returns

fdrp – The FDR corrected p-value map.

Return type

array.

neurora.stuff.fwe_correct(p, p_threshold)¶

FWE correction for fMRI RSA results

Parameters

• p (array) – The p-value map (3-D).

• p_threshold (string) – The p threshold.

Returns

fwep – The FWE corrected p-value map.

Return type

array.

neurora.stuff.get_HOcort()¶

get HarvardOxford-cort-maxprob-thr0-1mm.nii.gz

Returns

path – The absolute file path of ‘HarvardOxford-cort-maxprob-thr0-1mm.nii.gz’

Return type

string

neurora.stuff.get_affine(file_name)¶

get the affine of the fMRI-img

Parameters

file_name (string) – The filename of a sample fMRI-img in your experiment

Returns

affine – The position information of the fMRI-image array data in a reference space.

Return type

array

neurora.stuff.get_bg_ch2()¶

get ch2.nii.gz

Returns

path – The absolute file path of ‘ch2.nii.gz’

Return type

string

neurora.stuff.get_bg_ch2bet()¶

get ch2bet.nii.gz

Returns

path – The absolute file path of ‘ch2bet.nii.gz’

Return type

string

neurora.stuff.get_cluster_index_1d_1sided(m)¶

Get 1-D & 1-sided cluster-index information from a vector

Parameters

m (array) – A significant vector. The values in m should be 0 or 1, which represent not significant point or significant point, respectively.

Returns

• index_v (array) – The cluster-index vector.

• index_n (int) – The number of clusters.

neurora.stuff.get_cluster_index_1d_2sided(m)¶

Get 1-D & 2-sided cluster-index information from a vector

Parameters

m (array) – A significant vector. The values in m should be 0 or 1 or -1, which represent not significant point or significantly higher point or significantly less point, respectively.

Returns

• index_v1 (array) – The “greater” cluster-index vector.

• index_n1 (int) – The number of “greater” clusters.

• index_v2 (array) – The “less” cluster-index vector.

• index_n2 (int) – The number of “less” clusters.

neurora.stuff.get_cluster_index_2d_1sided(m)¶

Get 2-D & 1-sided cluster-index information from a matrix

Parameters

m (array) – A significant matrix. The values in m should be 0 or 1, which represent not significant point or significant point, respectively.

Returns

• index_m (array) – The cluster-index matrix.

• index_n (int) – The number of clusters.

neurora.stuff.get_cluster_index_2d_2sided(m)¶

Get 2-D & 2-sided cluster-index information from a matrix

Parameters

m (array) – A significant matrix. The values in m should be 0 or 1 or -1, which represent not significant point or significantly higher point or significantly less point, respectively.

Returns

• index_m1 (array) – The “greater” cluster-index matrix.

• index_n1 (int) – The “greater” number of clusters.

• index_m2 (array) – The “less” cluster-index matrix.

• index_n2 (int) – The “less” number of clusters.

neurora.stuff.limtozero(x)¶

zero the value close to zero

Parameters

x (float) –

Return type

0

neurora.stuff.mask_to(mask, size, affine, filename=None)¶

convert mask data of certain template to your data template

Parameters

• mask (string) – The file path+filename for the mask of certain template.

• size (array or list [nx, ny, nz]) – The size of the fMRI-img in your experiments.

• affine (array or list) – The position information of the fMRI-image array data in a reference space.

• filename (string. Default is None - 'newmask.nii'.) – The file path+filename for the mask for your data template .nii file.

Notes

A result .nii file of new mask will be generated at the corresponding address of filename.

neurora.stuff.mniposition_to(mnipoint, affine)¶

project the position in MNI coordinate system to the position of a point in matrix coordinate system

Parameters

• point (list or array) – The position in MNI coordinate system.

• affine (array or list) – The position information of the fMRI-image array data in a reference space.

Returns

newpoint – The position in matrix coordinate system.

Return type

array

neurora.stuff.package_root = '/Users/zitonglu/opt/anaconda3/lib/python3.9/site-packages/neurora'¶

a function for zeroing the value close to zero

neurora.stuff.permutation_corr(v1, v2, method='spearman', iter=1000)¶

Conduct Permutation test for correlation coefficients

Parameters

• v1 (array) – Vector 1.

• v2 (array) – Vector 2.

• method (string 'spearman' or 'pearson' or 'kendall' or 'similarity' or 'distance'. Default is 'spearman'.) – The method to calculate the similarities. If method=’spearman’, calculate the Spearman Correlations. If method=’pearson’, calculate the Pearson Correlations. If methd=’kendall’, calculate the Kendall tau Correlations. If method=’similarity’, calculate the Cosine Similarities. If method=’distance’, calculate the Euclidean Distances.

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value.

Return type

float

neurora.stuff.permutation_test(v1, v2, iter=1000)¶

Conduct Permutation test

Parameters

• v1 (array) – Vector 1.

• v2 (array) – Vector 2.

• iter (int. Default is 1000.) – The times for iteration.

Returns

p – The permutation test result, p-value.

Return type

float

neurora.stuff.position_to_mni(point, affine)¶

project the position in matrix coordinate system to the position in MNI coordinate system

Parameters

• point (list or array) – The position in matrix coordinate system.

• affine (array or list) – The position information of the fMRI-image array data in a reference space.

Returns

newpoint – The position in MNI coordinate system.

Return type

array

neurora.stuff.show_progressbar(str, cur, total=100)¶

neurora.stuff.smooth_1d(x, n=5)¶

smoothing for 1-D results

Parameters

• x (array) – The results. The shape of x should be [n_sub, n_ts]. n_subs, n_ts represent the number of subjects and the number of time-points.

• n (int. Default is 5.) – The smoothing step is n.

Returns

x_smooth – The results after smoothing. The shape of x_smooth should be [n_subs, n_ts]. n_subs, n_ts represent the number of subjects and the number of time-points.

Return type

array

neurora.stuff.smooth_2d(x, n=5)¶

smoothing for 2-D results

Parameters

• x (array) – The results. The shape of x should be [n_sub, n_ts1, n_ts2]. n_subs represents the number of subjects. n_ts1 & n_ts2 represent the numbers of time-points.

• n (int. Default is 5.) – The smoothing step is n.

Returns

x_smooth – The results after smoothing. The shape of x should be [n_sub, n_ts1, n_ts2]. n_subs represents the number of subjects. n_ts1 & n_ts2 represent the numbers of time-points.

Return type

array