Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank.

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The 18 matches

[1] § Materials and methods › Brain MRI › Diffusion-weighted MRI (dwMRI) ↔ 3_MRI_preprocessing/01_GetMRIData_dwMRI_IDP.ipynb, lines 1399–1506 · score 0.89 · free water volume, diffusion tensor fitting, diffusion tensor mode, orientation dispersion, volume fraction, head motion
[2] § Materials and methods › Brain MRI › MRI confounds ↔ 3_MRI_preprocessing/01_GetMRIData_dwMRI_IDP.ipynb, lines 1399–1506 · score 0.86 · median absolute, acquisition date, brain position, head motion, volumetric scaling, intensity scaling
[3] § Materials and methods › Brain MRI › MRI confounds ↔ 3_MRI_preprocessing/04_GetMRIData_rsMRI_IDP_FullPartCorr_Confounds.ipynb, lines 1129–1315 · score 0.86 · median absolute, acquisition date, head motion, volumetric scaling, intensity scaling, UK Biobank
[4] § Materials and methods › Brain MRI › Structural MRI (sMRI) ↔ 3_MRI_preprocessing/03_GetMRIData_sMRI.ipynb, lines 16–48 · score 0.85 · a2009s, FreeSurfer ASEG, FreeSurfer Desikan, subcortical volumes, matter volumes, brain volume
[5] § Materials and methods › Mental health ↔ 2_Mental_Health/01_GetMHData.ipynb, lines 1847–1989 · score 0.81 · nervous system, mental distress, Mental health, addictions, bipolar, mania
[6] § Materials and methods › Data › Cognition › Transformations of cognitive scores ↔ 1_Cognitive_preprocessing/04_GetGFactor_SingleSplit.Rmd, lines 33–47 · score 0.79 · incorrect matches, Prospective Memory, Pairs Matching, complete numeric, Reaction, log
[7] § Materials and methods › Data › Cognition › Transformations of cognitive scores ↔ 1_Cognitive_preprocessing/02_CogData_Descriptive_5Folds.ipynb, lines 23–34 · score 0.78 · incorrect matches, Prospective Memory, Pairs Matching, complete numeric, Reaction, log
[8] § Materials and methods › Mental health › Description of composite measures ↔ 2_Mental_Health/01_GetMHData.ipynb, lines 1847–1989 · score 0.77 · Hazardous alcohol, drinking alcohol, Alcohol dependence, moderate, AUDIT, log
[9] § Materials and methods › Brain MRI › Diffusion-weighted MRI (dwMRI) ↔ 3_MRI_preprocessing/01_GetMRIData_dwMRI_IDP.ipynb, lines 1384–1397 · score 0.65 · FA skeleton, tracts common, white matter tracts, DTI, TBSS, voxel
[10] § Materials and methods › Mental health › Description of composite measures ↔ 4_PLS/02_1_PLS_MH_SingleSplit.ipynb, lines 199–296 · score 0.64 · recurrent depression, depression triggered, GAD, loss, lifetime, harm
[11] § Materials and methods › Data › Cognition › Transformations of cognitive scores ↔ 1_Cognitive_preprocessing/03_GetGFactor_5Folds.Rmd, lines 268–296 · score 0.62 · Fluid Intelligence score, Picture Vocabulary, trail, cognitive
[12] § Materials and methods › Data › Cognition › Transformations of cognitive scores ↔ 1_Cognitive_preprocessing/04_GetGFactor_SingleSplit.Rmd, lines 92–115 · score 0.62 · Fluid Intelligence score, Picture Vocabulary, trail, cognitive
[13] § Materials and methods › Data analysis › Machine learning ↔ 4_PLS/02_1_PLS_MH_SingleSplit.ipynb, lines 661–713 · score 0.59 · absolute error, squared error, MSE, MAE, R2, Pearson
[14] § Materials and methods › Data analysis › Machine learning ↔ 4_PLS/01_PLS_MH_5Folds.ipynb, lines 74–137 · score 0.59 · absolute error, squared error, MSE, MAE, R2, fold
[15] § Materials and methods › Mental health › Description of composite measures ↔ 2_Mental_Health/01_GetMHData.ipynb, lines 1475–1509 · score 0.57 · Probable Depression Status, PDS, Anxiety, Health, scores
[16] § Results › Predictive modeling › Mental health ↔ 2_Mental_Health/01_GetMHData.ipynb, lines 1548–1691 · score 0.55 · mental distress, mental health, wellbeing, cannabis, happiness, unusual
[17] § Materials and methods › Feature importance ↔ 4_PLS/01_PLS_MH_5Folds.ipynb, lines 190–232 · score 0.51 · Pearson correlations, predicting cognition, mental health, fold, transformation
[18] § Materials and methods › Brain MRI › Resting-state functional MRI (rsMRI) ↔ 3_MRI_preprocessing/04_GetMRIData_rsMRI_IDP_FullPartCorr_Confounds.ipynb, lines 1400–1462 · score 0.50 · intensity scaling, artefact, echo, temporal, motion, Preprocessing

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The authors' code

Jupyter notebook · 1,989 lines · 81 KB · MIT · 4 matches

# %%
import pandas as pd
import ukbiobank.utils.utils
from ukbiobank.utils import loadCsv
from ukbiobank.utils import addFields
from ukbiobank.utils.utils import fieldIdsToNames
import matplotlib.pyplot as plt
import numpy as np
# %%
csv_path = '/UK_BB/ukbbdata/ukbb_oct23/ukb.csv'
ukb = ukbiobank.ukbio(ukb_csv=csv_path)
# %%
# Diagnoses main ICD10
diagnoses_df = ukbiobank.utils.utils.loadCsv(ukbio=ukb, fields=['eid', 41202])
diagnoses_main_ukb = addFields(ukbio=ukb, df=diagnoses_df, fields=['eid', 41202])
diagnoses_main_icd = ukbiobank.utils.utils.fieldIdsToNames(ukbio=ukb, df=diagnoses_main_ukb)
diagnoses_main_icd.to_csv('/UK_BB/diagnoses/diagnoses_main_icd[ukb].csv', index=False)
# %%
# Diagnoses ICD10
diagnoses_df = ukbiobank.utils.utils.loadCsv(ukbio=ukb, fields=['eid', 41270])
diagnoses_ukb = addFields(ukbio=ukb, df=diagnoses_df, fields=['eid', 41270])
diagnoses_icd = ukbiobank.utils.utils.fieldIdsToNames(ukbio=ukb, df=diagnoses_ukb)
diagnoses_icd.to_csv('/UK_BB/diagnoses/diagnoses_icd.csv', index=False)
# %%
# Mental Health Instance 2 and Online
# Instance 2
dfmh_2 = ukbiobank.utils.utils.loadCsv(ukbio=ukb, fields=['eid',
20002,
20126,
20122,
20127,
20124,
20125,
20123,
1920,
1930,
1940,
1950,
1960,
1970,
1980,
1990,
2000,
2010,
2020,
2030,
2040,
4526,
4537,
4548,
4559,
4570,
4581,
2050,
2060,
2070,
2080,
2090,
2100,
4598,
4609,
4620,
4631,
5375,
5386,
4642,
4653,
6156,
5663,
5674,
6145,
1031,
6160,
2110,
1031,
6160,
2110], instance=2)
# Online
dfmh_online = ukbiobank.utils.utils.loadCsv(ukbio=ukb, fields=['eid',
20002,
20499,
20500,
20544,
20446,
20441,
20547,
20433,
20434,
20445,
20447,
20532,
20435,
20438,
20449,
20450,
20436,
20439,
20440,
20442,
20448,
20518,
20510,
20507,
20519,
20514,
20511,
20513,
20508,
20534,
20546,
20437,
20533,
20517,
20535,
20536,
20502,
20501,
20492,
20548,
20493,
20550,
20419,
20541,
20429,
20421,
20425,
20537,
20539,
20427,
20418,
20423,
20420,
20422,
20540,
20543,
20428,
20505,
20512,
20506,
20509,
20516,
20515,
20520,
20426,
20542,
20549,
20417,
20538,
20552,
20431,
20406,
20401,
20456,
20457,
20503,
20504,
20551,
20404,
20415,
20432,
20414,
20403,
20416,
20413,
20407,
20412,
20409,
20408,
20411,
20405,
20410,
20455,
20453,
20454,
20461,
20462,
20468,
20474,
20463,
20466,
20471,
20477,
20467,
20470,
20476,
20465,
20473,
20489,
20488,
20487,
20490,
20491,
20522,
20523,
20521,
20524,
20525,
20531,
20529,
20526,
20530,
20528,
20527,
20497,
20498,
20495,
20496,
20494,
20479,
20485,
20486,
20480,
20482,
20481,
20553,
20554,
20483,
20484,
20458,
20459,
20460,
1031,
6160,
2110
])
# %%
# Add fields
# Instance 2
mh_i2 = addFields(ukbio=ukb, df=dfmh_2, fields=['eid',
20002,
20126,
20122,
20127,
20124,
20125,
20123,
1920,
1930,
1940,
1950,
1960,
1970,
1980,
1990,
2000,
2010,
2020,
2030,
2040,
4526,
4537,
4548,
4559,
4570,
4581,
2050,
2060,
2070,
2080,
2090,
2100,
4598,
4609,
4620,
4631,
5375,
5386,
4642,
4653,
6156,
5663,
5674,
6145,
1031,
6160,
2110,
1031,
6160,
2110], instances=2)
# Online
mh_online = addFields(ukbio=ukb, df=dfmh_online, fields=['eid',
20002,
20499,
20500,
20544,
20446,
20441,
20547,
20433,
20434,
20445,
20447,
20532,
20435,
20438,
20449,
20450,
20436,
20439,
20440,
20442,
20448,
20518,
20510,
20507,
20519,
20514,
20511,
20513,
20508,
20534,
20546,
20437,
20533,
20517,
20535,
20536,
20502,
20501,
20492,
20548,
20493,
20550,
20419,
20541,
20429,
20421,
20425,
20537,
20539,
20427,
20418,
20423,
20420,
20422,
20540,
20543,
20428,
20505,
20512,
20506,
20509,
20516,
20515,
20520,
20426,
20542,
20549,
20417,
20538,
20552,
20431,
20406,
20401,
20456,
20457,
20503,
20504,
20551,
20404,
20415,
20432,
20414,
20403,
20416,
20413,
20407,
20412,
20409,
20408,
20411,
20405,
20410,
20455,
20453,
20454,
20461,
20462,
20468,
20474,
20463,
20466,
20471,
20477,
20467,
20470,
20476,
20465,
20473,
20489,
20488,
20487,
20490,
20491,
20522,
20523,
20521,
20524,
20525,
20531,
20529,
20526,
20530,
20528,
20527,
20497,
20498,
20495,
20496,
20494,
20479,
20485,
20486,
20480,
20482,
20481,
20553,
20554,
20483,
20484,
20458,
20459,
20460,
1031,
6160,
2110])
# %%
# Convert Field IDs to Field Names
# Instance 2
mh_i2_names = ukbiobank.utils.utils.fieldIdsToNames(ukbio=ukb, df=mh_i2)
# Online
mh_online_names = ukbiobank.utils.utils.fieldIdsToNames(ukbio=ukb, df=mh_online)
# Merge Mental Health Instance 2 and Mental Health Online
mh_i2_online = pd.merge(mh_i2_names, mh_online_names, on="eid", suffixes=('', '_drop'))
mh_i2_online.columns.to_list()
mh_i2_online = mh_i2_online.loc[:, ~mh_i2_online.columns.str.endswith('_drop')]
mh_i2_online.to_csv('/UK_BB/mental_health/mh_instance2_online.csv', index=False)
# %% [markdown]
# ## Manage NaNs
# %%
# Drop NAs
mh_diagnoses_drop_na = mh_i2_online.dropna(subset=[
"Ever manic/hyper for 2 days-2.0",
"Worry too long after embarrassment-2.0",
"Loneliness, isolation-2.0",
"Risk taking-2.0",
"Happiness-2.0",
"Guilty feelings-2.0",
"Mood swings-2.0",
"Sensitivity / hurt feelings-2.0",
"Suffer from 'nerves'-2.0",
"Frequency of depressed mood in last 2 weeks-2.0",
"Seen doctor (GP) for nerves, anxiety, tension or depression-2.0",
"Frequency of unenthusiasm / disinterest in last 2 weeks-2.0",
"Work/job satisfaction-2.0",
"Family relationship satisfaction-2.0",
"Frequency of tenseness / restlessness in last 2 weeks-2.0",
"Miserableness-2.0",
"Ever highly irritable/argumentative for 2 days-2.0",
"Ever depressed for a whole week-2.0",
"Ever unenthusiastic/disinterested for a whole week-2.0",
"Seen a psychiatrist for nerves, anxiety, tension or depression-2.0",
"Fed-up feelings-2.0",
"Friendships satisfaction-2.0",
"Illness, injury, bereavement, stress in last 2 years-2.0",
"Worrier / anxious feelings-2.0",
"Financial situation satisfaction-2.0",
"Health satisfaction-2.0",
"Frequency of tiredness / lethargy in last 2 weeks-2.0",
"Nervous feelings-2.0",
"Irritability-2.0",
"Tense / 'highly strung'-2.0",
"Able to pay rent/mortgage as an adult-0.0",
"Sexual interference by partner or ex-partner without consent as an adult-0.0",
"Recent worrying too much about different things-0.0",
"Belittlement by partner or ex-partner as an adult-0.0",
"Ever taken cannabis-0.0",
"Physical violence by partner or ex-partner as an adult-0.0",
"Been in a confiding relationship as an adult-0.0",
"Recent feelings of tiredness or low energy-0.0",
"Recent changes in speed/amount of moving or speaking-0.0",
"Ever worried more than most people would in similar situation-0.0",
"Diagnosed with life-threatening illness-0.0",
"Victim of physically violent crime-0.0",
"Witnessed sudden violent death-0.0",
"Victim of sexual assault-0.0",
"Trouble falling or staying asleep, or sleeping too much-0.0",
"Ever felt worried, tense, or anxious for most of a month or longer-0.0",
"Frequency of drinking alcohol-0.0",
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0",
"Ever addicted to any substance or behaviour-0.0",
"Been involved in combat or exposed to war-zone-0.0",
"Recent restlessness-0.0",
"Been in serious accident believed to be life-threatening-0.0",
"Recent lack of interest or pleasure in doing things-0.0",
"Sexually molested as a child-0.0",
"Physically abused by family as a child-0.0",
"Felt hated by family member as a child-0.0",
"Ever contemplated self-harm-0.0",
"Ever had prolonged feelings of sadness or depression-0.0",
"Ever self-harmed-0.0",
"Someone to take to doctor when needed as a child-0.0",
"Ever thought that life not worth living-0.0",
"Recent trouble relaxing-0.0",
"Ever believed in an un-real conspiracy against self-0.0",
"Ever heard an un-real voice-0.0",
"Belief that own life is meaningful-0.0",
"General happiness with own health-0.0",
"General happiness-0.0",
"Ever believed in un-real communications or signs-0.0",
"Ever had prolonged loss of interest in normal activities-0.0",
"Ever seen an un-real vision-0.0",
"Repeated disturbing thoughts of stressful experience in past month-0.0",
"Recent thoughts of suicide or self-harm-0.0",
"Recent feelings of foreboding-0.0",
"Recent poor appetite or overeating-0.0",
"Recent feelings of depression-0.0",
"Avoided activities or situations because of previous stressful experience in past month-0.0",
"Recent inability to stop or control worrying-0.0",
"Recent trouble concentrating on things-0.0",
"Recent feelings of inadequacy-0.0",
"Recent feelings or nervousness or anxiety-0.0",
"Felt loved as a child-0.0",
"Felt very upset when reminded of stressful experience in past month-0.0",
"Ever sought or received professional help for mental distress-0.0",
"Ever had period of mania / excitability-0.0",
"Ever had period extreme irritability-0.0",
"Recent easy annoyance or irritability-0.0",
"Ever suffered mental distress preventing usual activities-0.0"], axis=0).reset_index(drop=True)
mh_diagnoses_drop_na.to_csv('/UK_BB/mental_health/mh_diagnoses_drop_na.csv', index=False)
# %% [markdown]
# ## Extract diagnoses
# %%
targets = pd.read_csv('/UK_BB/cognitive/target.csv')
mh = pd.merge(mh_diagnoses_drop_na, targets['eid'], on = 'eid')
mh.to_csv('/UK_BB/mental_health/mh_instance2_online_matched_to_targets.csv', index=False)
# %%
mh = pd.read_csv('/Cog-Ment/CSVs/2024/mental_health/mh_instance2_online_matched_to_targets.csv')
# %%
# Extract and combine noncncer diagnoses
mh_diagnoses = mh.copy()
# Set a 'noncaner' column: Filter columns that start with 'Non-cancer illness code, self-reported'
noncancer_cols = mh_diagnoses.filter(like='Non-cancer illness code, self-reported-').columns
# Concatenate the values in the columns into a single string, separated by ';'
mh_diagnoses["noncancer"] = mh_diagnoses[noncancer_cols].astype(str).apply(lambda x: x.str.cat(sep=";"), axis=1)
# Extract diagnoses
mh_diagnoses["Epilepsy"] = mh_diagnoses["noncancer"].str.contains("1264").astype(int)
mh_diagnoses["Migraine"] = mh_diagnoses["noncancer"].str.contains("1265").astype(int)
mh_diagnoses["Depression"] = mh_diagnoses["noncancer"].str.contains("1286").astype(int)
mh_diagnoses["Anxiety/panic attacks"] = mh_diagnoses["noncancer"].str.contains("1287").astype(int)
mh_diagnoses["Chronic/degenerative neurological problem & other neurological problem"] = mh_diagnoses["noncancer"].str.contains("|".join(["1258","1434"])).astype(int)
mh_diagnoses["Head & spinal & peripheral nerve injury, Spinal cord & peripheral nerve disorder & cranial nerve problem/palsy & infection of NS & Cerebral palsy"] = mh_diagnoses["noncancer"].str.contains("|".join(["1254",
"1251","1249","1244", "1433", "1267","1394","1266"])).astype(int)
mh_diagnoses["Nervous breakdown, Schizophrenia, self-harm/suicide attempt, mania/bipolar, alcohol/substance abuse/dependency, PTSD, eating disorder, OCD"] = mh_diagnoses["noncancer"].str.contains("|".join(["1289","1288","1289",
"1290","1291","1408", "1410", "1409", "1469", "1470", "1615"])).astype(int)
mh_diagnoses["Stress, insomnia"] = mh_diagnoses["noncancer"].str.contains("|".join(["1614", "1616"])).astype(int)
# Merge them into bigger groups
mh_diagnoses["Neurological problem, NS injury, epilepsy"] = mh_diagnoses[["Epilepsy", "Head & spinal & peripheral nerve injury, Spinal cord & peripheral nerve disorder & cranial nerve problem/palsy & infection of NS & Cerebral palsy", "Chronic/degenerative neurological problem & other neurological problem"]].any(axis=1).astype(int) #.sum(axis=1).astype(int)
mh_diagnoses["Stress, insomnia, migraine, nervous/mental problems"] = mh_diagnoses[["Stress, insomnia", "Migraine", "Nervous breakdown, Schizophrenia, self-harm/suicide attempt, mania/bipolar, alcohol/substance abuse/dependency, PTSD, eating disorder, OCD"]].any(axis=1).astype(int)
# Drop 'Non-cancer illness' and other columns
mh_diagnoses = mh_diagnoses.drop(columns=[col for col in mh_diagnoses.columns if 'Non-cancer illness' in col])
mh_diagnoses = mh_diagnoses.drop(columns=["Epilepsy", "Head & spinal & peripheral nerve injury, Spinal cord & peripheral nerve disorder & cranial nerve problem/palsy & infection of NS & Cerebral palsy", "Chronic/degenerative neurological problem & other neurological problem"])
mh_diagnoses = mh_diagnoses.drop(columns=["Stress, insomnia", "Migraine", "Nervous breakdown, Schizophrenia, self-harm/suicide attempt, mania/bipolar, alcohol/substance abuse/dependency, PTSD, eating disorder, OCD"])
mh_diagnoses.to_csv('/UK_BB/mental_health/mh_noncancer_diagnoses.csv', index=False)
# %%
mh = mh_diagnoses.copy()
# %% [markdown]
# ## PHQ9
# %%
# PHQ9
# Calculate the score ingonoring -818
# PHQ: 20514, 20510, 20517, 20519, 20511, 20507, 20508, 20518, 20513: sum and subtract 9 to get the score
# Recent lack of interest or pleasure in doing things-0.0
# Recent feelings of depression
# Trouble falling or staying asleep, or sleeping too much
# Recent feelings of tiredness or low energy
# Recent poor appetite or overeating
# Recent feelings of inadequacy
# Recent trouble concentrating on things
# Recent changes in speed/amount of moving or speaking
# Recent thoughts of suicide or self-harm
# -818 Prefer not to answer
# 1 Not at all
# 2 Several days
# 3 More than half the days
# 4 Nearly every day
# Replace -818 with 1 only for PHQ!
mh[["Recent lack of interest or pleasure in doing things-0.0", "Recent feelings of depression-0.0", "Trouble falling or staying asleep, or sleeping too much-0.0",
"Recent feelings of tiredness or low energy-0.0", "Recent poor appetite or overeating-0.0", "Recent feelings of inadequacy-0.0",
"Recent trouble concentrating on things-0.0", "Recent changes in speed/amount of moving or speaking-0.0", "Recent thoughts of suicide or self-harm-0.0"]] = mh[["Recent lack of interest or pleasure in doing things-0.0", "Recent feelings of depression-0.0", "Trouble falling or staying asleep, or sleeping too much-0.0",
"Recent feelings of tiredness or low energy-0.0", "Recent poor appetite or overeating-0.0", "Recent feelings of inadequacy-0.0",
"Recent trouble concentrating on things-0.0", "Recent changes in speed/amount of moving or speaking-0.0", "Recent thoughts of suicide or self-harm-0.0"]].replace({-818: 1})
# Check if replaced correctly
print((mh[["Recent lack of interest or pleasure in doing things-0.0", "Recent feelings of depression-0.0", "Trouble falling or staying asleep, or sleeping too much-0.0",
"Recent feelings of tiredness or low energy-0.0", "Recent poor appetite or overeating-0.0", "Recent feelings of inadequacy-0.0",
"Recent trouble concentrating on things-0.0", "Recent changes in speed/amount of moving or speaking-0.0", "Recent thoughts of suicide or self-harm-0.0"]] == -818).sum())
mh[["Recent lack of interest or pleasure in doing things-0.0", "Recent feelings of depression-0.0", "Trouble falling or staying asleep, or sleeping too much-0.0",
"Recent feelings of tiredness or low energy-0.0", "Recent poor appetite or overeating-0.0", "Recent feelings of inadequacy-0.0",
"Recent trouble concentrating on things-0.0", "Recent changes in speed/amount of moving or speaking-0.0", "Recent thoughts of suicide or self-harm-0.0"]].min()
# %%
# Calculate PHQ9 for after replacing -818 with 1
mh["PHQ9"] = mh[[
"Recent lack of interest or pleasure in doing things-0.0",
"Recent feelings of depression-0.0",
"Trouble falling or staying asleep, or sleeping too much-0.0",
"Recent feelings of tiredness or low energy-0.0",
"Recent poor appetite or overeating-0.0",
"Recent feelings of inadequacy-0.0",
"Recent trouble concentrating on things-0.0",
"Recent changes in speed/amount of moving or speaking-0.0",
"Recent thoughts of suicide or self-harm-0.0"
]].sum(axis=1, skipna=True).astype(int)
# Standardize PHQ to 0-27
mh["PHQ9"] = mh["PHQ9"].subtract(9)
print('PHQ9 MIN:', mh["PHQ9"].min())
print('PHQ9 MAX:', mh["PHQ9"].max())
# %% [markdown]
# ## Depression ever
# %% [markdown]
# At least one core symptom of depression, most or all of the day on most or all days for a two week period, with at least five depressive symptoms that represent a change from usual occurring over the same time-scale, with some or a lot of impairment.
#
# - Persistent sadness (20446) = Yes OR Loss of interest (20441) = Yes - CORE
# - AND How much of day (20436) = Most of day or All day long
# - AND Did you feel this way (20439) = Almost every day or Every day
# - AND Impairment (20440) = Somewhat or A lot
# - AND Total number of symptoms endorsed (core and others) >= 5:
#
# - Persistent sadness (core) 20446; Loss of interest (core) 20441; PLUS
# - Tired or low energy 20449;
# - Gain or loss of weight 20536 = Gain, Loss or Gain and loss;
# - Sleep change 20532;
# - Trouble concentrating 20435;
# - Feeling worthless 20450;
# - Thinking about death 20437
# %%
depr_ever_scores = []
# Loop through each participant's variables
for i in range(len(mh)):
if (mh['Ever had prolonged feelings of sadness or depression-0.0'].iloc[i] > 0 or mh['Ever had prolonged loss of interest in normal activities-0.0'].iloc[i] > 0) and mh['Fraction of day affected during worst episode of depression-0.0'].iloc[i] > 2 and mh['Frequency of depressed days during worst episode of depression-0.0'].iloc[i] > 1 and mh['Impact on normal roles during worst period of depression-0.0'].iloc[i] > 1:
num_symptoms = sum(j > 0 for j in [
mh['Feelings of tiredness during worst episode of depression-0.0'].iloc[i],
mh['Weight change during worst episode of depression-0.0'].iloc[i],
mh['Did your sleep change?-0.0'].iloc[i],
mh['Difficulty concentrating during worst depression-0.0'].iloc[i],
mh['Feelings of worthlessness during worst period of depression-0.0'].iloc[i],
mh['Thoughts of death during worst depression-0.0'].iloc[i],
mh['Ever had prolonged feelings of sadness or depression-0.0'].iloc[i],
mh['Ever had prolonged loss of interest in normal activities-0.0'].iloc[i]
])
if num_symptoms >= 5:
depr_ever_score = 1
else:
depr_ever_score = 0
else:
depr_ever_score = 0
depr_ever_scores.append(depr_ever_score)
mh = mh.assign(Depression_ever=depr_ever_scores)
# %% [markdown]
# ## Subthreshol depression
# %%
# Replace non-responders
print(mh["Ever had prolonged feelings of sadness or depression-0.0"].median())
mh["Ever had prolonged feelings of sadness or depression-0.0"] = mh["Ever had prolonged feelings of sadness or depression-0.0"].replace({-818:1, -121:1})
# %%
depr_sub_scores = []
for i in range(len(mh)):
diagnosed_with_depression = any(mh[f'Mental health problems ever diagnosed by a professional-0.{j}'].iloc[i] == 11 for j in range(1, 17))
if mh['Depression_ever'].iloc[i] == 0 and (diagnosed_with_depression or mh['Depression'].iloc[i] == 1 or mh['Ever had prolonged feelings of sadness or depression-0.0'].iloc[i] > 0 or mh['Ever had prolonged loss of interest in normal activities-0.0'].iloc[i] > 0 or mh['PHQ9'].iloc[i] > 5):
depr_sub_score = 1
else:
depr_sub_score = 0
depr_sub_scores.append(depr_sub_score)
mh = mh.assign(Depression_subthreshold=depr_sub_scores)
# %% [markdown]
# ## Bipolar I
# %%
bipolar_1_scores = []
for i in range(len(mh)):
if mh['Depression_ever'].iloc[i] == 1 and (mh['Ever had period of mania / excitability-0.0'].iloc[i] > 0 or mh['Ever had period extreme irritability-0.0'].iloc[i] > 0) and mh['Longest period of mania or irritability-0.0'].iloc[i] == 3 and mh['Severity of problems due to mania or irritability-0.0'].iloc[i] == 1:
num_symptoms = sum(j > 0 for j in [
mh['Manifestations of mania or irritability-0.1'].iloc[i], mh['Manifestations of mania or irritability-0.2'].iloc[i],
mh['Manifestations of mania or irritability-0.3'].iloc[i], mh['Manifestations of mania or irritability-0.4'].iloc[i],
mh['Manifestations of mania or irritability-0.5'].iloc[i], mh['Manifestations of mania or irritability-0.6'].iloc[i],
mh['Manifestations of mania or irritability-0.7'].iloc[i], mh['Manifestations of mania or irritability-0.8'].iloc[i]])
if (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 0 and num_symptoms >= 4) or (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 1 and num_symptoms >= 3):
bipolar_1_score = 1
else:
bipolar_1_score = 0
else:
bipolar_1_score = 0
bipolar_1_scores.append(bipolar_1_score)
mh = mh.assign(Bipolar_1=bipolar_1_scores)
# %% [markdown]
# ## Bipolar II
# %%
bipolar_2_scores = []
for i in range(len(mh)):
if mh['Depression_ever'].iloc[i] == 1 and (mh['Ever had period of mania / excitability-0.0'].iloc[i] > 0 or mh['Ever had period extreme irritability-0.0'].iloc[i] > 0) and mh['Longest period of mania or irritability-0.0'].iloc[i] == 3:
num_symptoms = sum(j > 0 for j in [
mh['Manifestations of mania or irritability-0.1'].iloc[i], mh['Manifestations of mania or irritability-0.2'].iloc[i],
mh['Manifestations of mania or irritability-0.3'].iloc[i], mh['Manifestations of mania or irritability-0.4'].iloc[i],
mh['Manifestations of mania or irritability-0.5'].iloc[i], mh['Manifestations of mania or irritability-0.6'].iloc[i],
mh['Manifestations of mania or irritability-0.7'].iloc[i], mh['Manifestations of mania or irritability-0.8'].iloc[i]])
if (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 0 and num_symptoms >= 4) or (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 1 and num_symptoms >= 3):
bipolar_2_score = 1
else:
bipolar_2_score = 0
else:
bipolar_2_score = 0
bipolar_2_scores.append(bipolar_2_score)
mh = mh.assign(Bipolar_2=bipolar_2_scores)
# %% [markdown]
# ## Depression single episode
# %%
depression_single = [1 if (mh['Depression_ever'].iloc[i] == 1 and mh['Bipolar_1'].iloc[i] == 0 and mh['Lifetime number of depressed periods-0.0'].iloc[i] == 1) else 0 for i in range(len(mh))]
mh = mh.assign(Depression_single=depression_single)
# %% [markdown]
# ## Recurrent depression
# %%
depression_recurrent= [1 if (mh['Depression_ever'].iloc[i] == 1 and mh['Bipolar_1'].iloc[i] == 0 and (mh['Lifetime number of depressed periods-0.0'].iloc[i] > 1 or mh['Lifetime number of depressed periods-0.0'].iloc[i] == -999)) else 0 for i in range(len(mh))]
mh = mh.assign(Depression_recurrent=depression_recurrent)
# %% [markdown]
# ## Depression single episode triggered by loss
# %%
depression_by_loss= [1 if (mh['Depression_single'].iloc[i] == 1 and mh['Depression possibly related to stressful or traumatic event-0.0'].iloc[i] == 1) else 0 for i in range(len(mh))]
mh = mh.assign(Depression_triggered_by_loss=depression_by_loss)
# %% [markdown]
# ## Current depression
# %%
depr_current = []
for i in range(len(mh)):
if mh['Depression_ever'].iloc[i] == 1:
num_sumptoms_main = sum(1 for j in [
mh['Recent lack of interest or pleasure in doing things-0.0'].iloc[i],
mh['Recent feelings of depression-0.0'].iloc[i],
mh['Trouble falling or staying asleep, or sleeping too much-0.0'].iloc[i],
mh['Recent feelings of tiredness or low energy-0.0'].iloc[i],
mh['Recent poor appetite or overeating-0.0'].iloc[i],
mh['Recent feelings of inadequacy-0.0'].iloc[i],
mh['Recent trouble concentrating on things-0.0'].iloc[i],
mh['Recent changes in speed/amount of moving or speaking-0.0'].iloc[i]
] if j > 2)
num_sumptoms_sui = sum(1 for m in [mh['Recent thoughts of suicide or self-harm-0.0'].iloc[i]] if m > 1)
num_symptoms_all = num_sumptoms_main + num_sumptoms_sui
if num_symptoms_all >= 5:
deprcurr_score = 1
else:
deprcurr_score = 0
else:
deprcurr_score = 0
depr_current.append(deprcurr_score)
mh = mh.assign(Depression_current=depr_current)
# %% [markdown]
# ## Current severe depression
# %%
depr_current_severe = [1 if (mh['Depression_current'].iloc[i] == 1 and mh['PHQ9'].iloc[i] > 15) else 0 for i in range(len(mh))]
mh = mh.assign(Depression_current_severe=depr_current_severe)
# %% [markdown]
# ## Mania
# %%
mania_scores = []
for i in range(len(mh)):
if (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 1 or mh['Ever had period extreme irritability-0.0'].iloc[i] == 1) and mh['Longest period of mania or irritability-0.0'].iloc[i] == 3:
num_symptoms = sum(j > 0 for j in [
mh['Manifestations of mania or irritability-0.1'].iloc[i], mh['Manifestations of mania or irritability-0.2'].iloc[i],
mh['Manifestations of mania or irritability-0.3'].iloc[i], mh['Manifestations of mania or irritability-0.4'].iloc[i],
mh['Manifestations of mania or irritability-0.5'].iloc[i], mh['Manifestations of mania or irritability-0.6'].iloc[i],
mh['Manifestations of mania or irritability-0.7'].iloc[i], mh['Manifestations of mania or irritability-0.8'].iloc[i]])
if (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 1 and num_symptoms >= 3) or (mh['Ever had period of mania / excitability-0.0'].iloc[i] == 0 and num_symptoms >= 4):
mania_score = 1
else:
mania_score = 0
else:
mania_score = 0
mania_scores.append(mania_score)
mh = mh.assign(Mania=bipolar_1_scores)
# %%
# Replace non-respondes with median
mh[["Ever had prolonged loss of interest in normal activities-0.0", "Ever had period extreme irritability-0.0",
"Ever had period of mania / excitability-0.0", "Ever felt worried, tense, or anxious for most of a month or longer-0.0",
"Ever worried more than most people would in similar situation-0.0", "Ever taken cannabis-0.0", "Ever had prolonged feelings of sadness or depression-0.0"]].median()
mh[["Ever had prolonged loss of interest in normal activities-0.0", "Ever had period extreme irritability-0.0",
"Ever had period of mania / excitability-0.0", "Ever felt worried, tense, or anxious for most of a month or longer-0.0",
"Ever worried more than most people would in similar situation-0.0", "Ever taken cannabis-0.0"]] = mh[[
"Ever had prolonged loss of interest in normal activities-0.0", "Ever had period extreme irritability-0.0",
"Ever had period of mania / excitability-0.0", "Ever felt worried, tense, or anxious for most of a month or longer-0.0",
"Ever worried more than most people would in similar situation-0.0", "Ever taken cannabis-0.0"]].replace({-818:0, -121:0})
# %% [markdown]
# ## GAD-7 Score
# %%
print(mh[["Recent feelings or nervousness or anxiety-0.0", "Recent inability to stop or control worrying-0.0",
"Recent worrying too much about different things-0.0", "Recent trouble relaxing-0.0", "Recent restlessness-0.0",
"Recent easy annoyance or irritability-0.0", "Recent feelings of foreboding-0.0"]].median())
mh[["Recent feelings or nervousness or anxiety-0.0", "Recent inability to stop or control worrying-0.0",
"Recent worrying too much about different things-0.0", "Recent trouble relaxing-0.0", "Recent restlessness-0.0",
"Recent easy annoyance or irritability-0.0", "Recent feelings of foreboding-0.0"]] = mh[["Recent feelings or nervousness or anxiety-0.0", "Recent inability to stop or control worrying-0.0",
"Recent worrying too much about different things-0.0", "Recent trouble relaxing-0.0", "Recent restlessness-0.0",
"Recent easy annoyance or irritability-0.0", "Recent feelings of foreboding-0.0"]].replace({-818: 1})
mh["GAD7"] = mh[["Recent feelings or nervousness or anxiety-0.0", "Recent inability to stop or control worrying-0.0",
"Recent worrying too much about different things-0.0", "Recent trouble relaxing-0.0", "Recent restlessness-0.0",
"Recent easy annoyance or irritability-0.0", "Recent feelings of foreboding-0.0"]].sum(axis=1).astype(int).subtract(7)
print('GAD7 MIN',mh["GAD7"].min())
print('GAD7 MAX',mh["GAD7"].max())
# %% [markdown]
# ## GAD ever
# %%
gad_ever_scores = []
for i in range(len(mh)):
worried_tense = mh['Ever felt worried, tense, or anxious for most of a month or longer-0.0'].iloc[i] # yes == 1
duration = mh['Longest period spent worried or anxious-0.0'].iloc[i] # >=6 / == -999
most_days = mh['Worried most days during period of worst anxiety-0.0'].iloc[i] #yes == 1
more_than_most = mh['Ever worried more than most people would in similar situation-0.0'].iloc[i]
stronger_than_most = mh['Stronger worrying (than other people) during period of worst anxiety-0.0'].iloc[i] #yes == 1
more_than_one = mh['Number of things worried about during worst period of anxiety-0.0'].iloc[i] # >1 (2More than one thing)
diff_worries = mh['Multiple worries during worst period of anxiety-0.0'].iloc[i] #yes == 1
diff_stop = mh['Difficulty stopping worrying during worst period of anxiety-0.0'].iloc[i] #yes == 1
freq_stop= mh['Frequency of inability to stop worrying during worst period of anxiety-0.0'].iloc[i] #often
freq_control = mh['Frequency of difficulty controlling worry during worst period of anxiety-0.0'].iloc[i] #often
interference = mh['Impact on normal roles during worst period of anxiety-0.0'].iloc[i] # Some or A lot
if worried_tense == 1 and (duration >= 6 or duration == -999) and most_days ==1 and (more_than_most == 1 or stronger_than_most == 1) and (more_than_one == 2 or diff_worries == 1) and (diff_stop==1 or freq_stop==3 or freq_control==3) and interference > 1:
num_symptoms = sum(g > 0 for g in [
mh['Restless during period of worst anxiety-0.0'].iloc[i],
mh['Keyed up or on edge during worst period of anxiety-0.0'].iloc[i],
mh['Easily tired during worst period of anxiety-0.0'].iloc[i],
mh['Difficulty concentrating during worst period of anxiety-0.0'].iloc[i],
mh['More irritable than usual during worst period of anxiety-0.0'].iloc[i],
mh['Tense, sore, or aching muscles during worst period of anxiety-0.0'].iloc[i],
mh['Frequent trouble falling or staying asleep during worst period of anxiety-0.0'].iloc[i]
])
if num_symptoms >= 3:
gad_ever_score = 1
else:
gad_ever_score = 0
else:
gad_ever_score = 0
gad_ever_scores.append(gad_ever_score)
mh = mh.assign(GAD_ever = gad_ever_scores)
# %% [markdown]
# ## Current anxiety
# %%
gad_current = [1 if (mh['GAD_ever'].iloc[i] == 1 and mh['GAD7'].iloc[i] >= 10) else 0 for i in range(len(mh))]
mh = mh.assign(GAD_current = gad_current)
# %% [markdown]
# ## Current mild anxiety
# %%
gad_current_mild = [1 if (mh['GAD_ever'].iloc[i] == 1 and mh['GAD7'].iloc[i] >= 5 and mh['GAD7'].iloc[i] < 10) else 0 for i in range(len(mh))] # [5,10)
mh = mh.assign(GAD_current_mild = gad_current_mild)
# %% [markdown]
# ## Current moderate anxiety
# %%
gad_current_moderate = [1 if (mh['GAD_ever'].iloc[i] == 1 and mh['GAD7'].iloc[i] >= 10 and mh['GAD7'].iloc[i] < 15) else 0 for i in range(len(mh))] # [10,15)
mh = mh.assign(GAD_current_moderate = gad_current_moderate)
# %% [markdown]
# ## Current severe anxiety
# %%
gad_current_severe = [1 if (mh['GAD_ever'].iloc[i] == 1 and mh['GAD7'].iloc[i] >= 15) else 0 for i in range(len(mh))]# [15]
mh = mh.assign(GAD_current_severe = gad_current_severe)
# %% [markdown]
# ## PCL
# %% [markdown]
# Sum of scores on questions representing the core symptoms of PTSD (subtract 5 to adjust)
#
#
# Score 1-5 and sum
# - 20497 Repeated disturbing thoughts of stressful experience in past month
# - 20498 Felt very upset when reminded of stressful experience in past month
# - 20495 Avoided activities or situations because of previous stressful experience in past month
# - 20496 Felt distant from other people in past month
# - 20494 Felt irritable or had angry outbursts in past month
#
#
# -818 Prefer not to answer / 0 Not at all / 1 A little bit / 2 Moderately / 3 Quite a bit / 4 Extremely
#
# - 20508 Trouble concentrating (scored 1-4)
#
# -818 Prefer not to answer / 1 Not at all / 2 Several days / 3 More than half the days / 4 Nearly every day
#
#
# Sum {
# - 20497 Repeated disturbing thoughts of stressful experience in past month,
# - 20498 Felt very upset when reminded of stressful experience in past month,
# - 20495 Avoided activities or situations because of previous stressful experience in past month,
# - 20496 Felt distant from other people in past month,
# - 20494 Felt irritable or had angry outbursts in past month}
#
# - initially scored 0 to 4, replace -818 with 0 (median)
#
# - -818 Prefer not to answer / 1 Not at all / 2 Several days / 3 More than half the days / 4 Nearly every day
#
# - {20508 Trouble concentrating}
# - scored 1,2,3,4
# %%
# PCL6
# Medians
print(mh[['Repeated disturbing thoughts of stressful experience in past month-0.0', 'Felt very upset when reminded of stressful experience in past month-0.0',
'Avoided activities or situations because of previous stressful experience in past month-0.0', 'Felt distant from other people in past month-0.0',
'Felt irritable or had angry outbursts in past month-0.0']].median())
print(mh["Recent trouble concentrating on things-0.0"].median())
mh[['Repeated disturbing thoughts of stressful experience in past month-0.0',
'Felt very upset when reminded of stressful experience in past month-0.0',
'Avoided activities or situations because of previous stressful experience in past month-0.0',
'Felt distant from other people in past month-0.0',
'Felt irritable or had angry outbursts in past month-0.0']] = mh[['Repeated disturbing thoughts of stressful experience in past month-0.0',
'Felt very upset when reminded of stressful experience in past month-0.0',
'Avoided activities or situations because of previous stressful experience in past month-0.0', 'Felt distant from other people in past month-0.0',
'Felt irritable or had angry outbursts in past month-0.0']].replace({-818: 0})
mh["Recent trouble concentrating on things-0.0"] = mh["Recent trouble concentrating on things-0.0"].replace(-818,1)
mh["PCL5"] = mh[['Repeated disturbing thoughts of stressful experience in past month-0.0', 'Felt very upset when reminded of stressful experience in past month-0.0',
'Avoided activities or situations because of previous stressful experience in past month-0.0', 'Felt distant from other people in past month-0.0',
'Felt irritable or had angry outbursts in past month-0.0']].sum(axis=1).astype(int)
mh["PCL5"] = mh["PCL5"].add(5)
# Calculate PCL6
mh["PCL6"] = mh[['PCL5', 'Recent trouble concentrating on things-0.0']].sum(axis=1).astype(int)
print('PCL6 MIN', mh["PCL6"].min())
print('PCL6 MAX', mh["PCL6"].max())
# %% [markdown]
# ## PTSD
# %%
mh["PTSD"] = [1 if (mh["PCL6"].iloc[i] >=14) else 0 for i in range(len(mh))]
mh = mh.rename(columns={'Self-harm: Life not worth living': 'Self-harm: Ever thought life not worth living', 'Ever thought that life not worth living': 'Frequency of "life not worth living" thoughts'})
# %% [markdown]
# ## AUDIT
# %% [markdown]
# AUDIT total score was created by taking the sum of items 1–10 for all participants, including those who endorsed currently never drinking alcohol (as they could still endorse past alcohol harm on items 9 and 10). We also created AUDIT subdomain scores by aggregating the scores from items 1–3, which include the information pertaining to alcohol consumption (AUDIT-C, N = 121,604), and from items 4–10, which indexes the information pertaining to alcohol problems (AUDIT-P, N = 121,604).
# %%
# Replace -818 with median
# scored 0 to 4
mh["Frequency of drinking alcohol-0.0"] = mh["Frequency of drinking alcohol-0.0"].replace(-818, mh["Frequency of drinking alcohol-0.0"].median())
# scored 1 to 5 / 0-2
columns_to_replace = [
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0',
'Ever been injured or injured someone else through drinking alcohol-0.0',
'Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0'
]
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
mh[[
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0',
'Ever been injured or injured someone else through drinking alcohol-0.0',
'Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0'
]].min()
# %%
# Recode 0,1,2 to 0,2,4
mh[[
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]] = mh[[
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]].replace(1, 2)
mh[[
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]] = mh[[
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]].replace(2, 4)
# %%
# Replace NaN with 0
mh[['Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0']] = mh[['Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0']].replace(np.nan, 0)
mh[mh["Frequency of drinking alcohol-0.0"] == 0][[
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0'
]].isna().sum()
# %%
# Recode 1-5 to 0-4
mh[['Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0']] = mh[['Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0']].replace({1:0, 2:1, 3:2, 4:3, 5:4})
# %%
# AUDIT
mh["AUDIT"] = mh[["Frequency of drinking alcohol-0.0",
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0',
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]].sum(axis=1).astype(int)
# (log)AUDIT
mh["AUDIT_log"] = np.log1p(mh["AUDIT"])
# %% [markdown]
# ## AUDIT-C
#
# Scores from items 1–3, which include the information pertaining to alcohol consumption
# %%
mh["AUDIT_C"] = mh[["Frequency of drinking alcohol-0.0",
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of consuming six or more units of alcohol-0.0']].sum(axis=1).astype(int)
mh["AUDIT_C_log"] = np.log1p(mh["AUDIT_C"])
# %% [markdown]
# ## AUDIT-P
#
# Items 4–10, which indexes the information pertaining to alcohol problems
# %%
mh["AUDIT_P"] = mh[['Frequency of inability to cease drinking in last year-0.0',
'Frequency of failure to fulfil normal expectations due to drinking alcohol in last year-0.0',
'Frequency of needing morning drink of alcohol after heavy drinking session in last year-0.0',
'Frequency of feeling guilt or remorse after drinking alcohol in last year-0.0',
'Frequency of memory loss due to drinking alcohol in last year-0.0',
"Ever been injured or injured someone else through drinking alcohol-0.0",
"Ever had known person concerned about, or recommend reduction of, alcohol consumption-0.0"]].sum(axis=1).astype(int)
mh["AUDIT_P_log"] = np.log1p(mh["AUDIT_P"])
# %% [markdown]
# ## Hazardous / Harmful Alcohol Use
# %%
mh["Hazardous_Alcohol_Use"] = np.where(mh["AUDIT"] >= 8, 1, 0)
# Alcohol dependence
mh["Alcohol_Dependence"] = np.where(mh["AUDIT"] >= 15, 1, 0)
mh["Alcohol_depencence_ever"] = np.where(mh["Ever physically dependent on alcohol-0.0"] == 1, 1, 0)
# %% [markdown]
# ## Cannabis
# %%
mh["Ever taken cannabis-0.0"] = mh["Ever taken cannabis-0.0"].replace(np.nan, mh["Ever taken cannabis-0.0"].median())
mh["Cannabis ever"] = np.where(mh["Ever taken cannabis-0.0"] > 0, 1, 0)
mh['Frequency of taking cannabis'] = mh["Ever taken cannabis-0.0"]
#20454[Maximum frequency of taking cannabis] frequency = every day {4}
mh["Cannabis daily"] = np.where(mh["Maximum frequency of taking cannabis-0.0"] == 4, 1, 0)
# %% [markdown]
# ## Childhood adverse events
# %%
# Replace -818 with median
columns_to_replace = ['Felt loved as a child-0.0', 'Physically abused by family as a child-0.0', 'Felt hated by family member as a child-0.0',
'Sexually molested as a child-0.0', 'Someone to take to doctor when needed as a child-0.0']
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
# %%
# Based on answers to the five questions of Childhood Trauma Screen (CTS), all scored 1-5. A score over the threshold on any question is screen positive
mh[['Felt loved as a child-0.0', 'Physically abused by family as a child-0.0', 'Felt hated by family member as a child-0.0',
'Sexually molested as a child-0.0', 'Someone to take to doctor when needed as a child-0.0']] = mh[['Felt loved as a child-0.0',
'Physically abused by family as a child-0.0', 'Felt hated by family member as a child-0.0',
'Sexually molested as a child-0.0', 'Someone to take to doctor when needed as a child-0.0']].replace({0:1, 1:2, 2:3, 3:4, 4:5})
# %%
mh['Childhood_adverse_events'] = [1 if (mh['Felt loved as a child-0.0'].iloc[i] <= 3 or mh['Physically abused by family as a child-0.0'].iloc[i] >= 2 or
mh['Felt hated by family member as a child-0.0'].iloc[i] >= 2 or mh['Sexually molested as a child-0.0'].iloc[i] >= 2 or mh['Someone to take to doctor when needed as a child-0.0'].iloc[i] <= 4) else 0 for i in range(len(mh))]
# %% [markdown]
# ## Adult adverse events
# %%
# Replace -818 with median
columns_to_replace = ['Been in a confiding relationship as an adult-0.0',
'Physical violence by partner or ex-partner as an adult-0.0',
'Belittlement by partner or ex-partner as an adult-0.0',
'Sexual interference by partner or ex-partner without consent as an adult-0.0',
'Able to pay rent/mortgage as an adult-0.0']
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
# %%
# Recode
mh[['Been in a confiding relationship as an adult-0.0',
'Physical violence by partner or ex-partner as an adult-0.0',
'Belittlement by partner or ex-partner as an adult-0.0',
'Sexual interference by partner or ex-partner without consent as an adult-0.0',
'Able to pay rent/mortgage as an adult-0.0']] = mh[['Been in a confiding relationship as an adult-0.0',
'Physical violence by partner or ex-partner as an adult-0.0',
'Belittlement by partner or ex-partner as an adult-0.0',
'Sexual interference by partner or ex-partner without consent as an adult-0.0',
'Able to pay rent/mortgage as an adult-0.0']].replace({0:1, 1:2, 2:3, 3:4, 4:5})
# %%
mh['Adult_adverse_events'] = [1 if (mh['Been in a confiding relationship as an adult-0.0'].iloc[i] <= 3 or mh['Physical violence by partner or ex-partner as an adult-0.0'].iloc[i] >= 2 or
mh['Belittlement by partner or ex-partner as an adult-0.0'].iloc[i] >= 2 or mh['Sexual interference by partner or ex-partner without consent as an adult-0.0'].iloc[i] >= 2 or mh['Able to pay rent/mortgage as an adult-0.0'].iloc[i] <= 4) else 0 for i in range(len(mh))]
# %% [markdown]
# ## Catastrophic trauma
# %%
# Replace -818 with median
columns_to_replace = ['Victim of sexual assault-0.0',
'Victim of physically violent crime-0.0',
'Been in serious accident believed to be life-threatening-0.0',
'Witnessed sudden violent death-0.0',
'Diagnosed with life-threatening illness-0.0',
'Been involved in combat or exposed to war-zone-0.0']
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
# %%
mh['Catastrophic_trauma'] = [1 if (mh['Victim of sexual assault-0.0'].iloc[i] > 0 or
mh['Victim of physically violent crime-0.0'].iloc[i] > 0 or
mh['Been in serious accident believed to be life-threatening-0.0'].iloc[i] > 0 or
mh['Witnessed sudden violent death-0.0'].iloc[i] > 0 or
mh['Diagnosed with life-threatening illness-0.0'].iloc[i] > 0 or
mh['Been involved in combat or exposed to war-zone-0.0'].iloc[i] > 0) else 0 for i in range(len(mh))]
# %% [markdown]
# ## Wellbeing
# %%
# Replace -818 with median
columns_to_replace = ["General happiness-0.0", "General happiness with own health-0.0", "Belief that own life is meaningful-0.0"]
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
mh[column] = mh[column].replace(-121, column_median)
# %%
mh[["General happiness-0.0",
"General happiness with own health-0.0"]] = mh[["General happiness-0.0", "General happiness with own health-0.0"]].replace({1:6, 2:5, 3:4, 4:3, 5:2, 6:1})
# %%
# Was:
#1Extremely happy
#2Very happy
#3Moderately happy
#4Moderately unhappy
#5Very unhappy
#6Extremely unhappy
#Become
#1Extremely unhappy
#2Very unhappy
#3Moderately unhappy
#4Moderately happy
#5Very happy
#6Extremely happy
# %%
# Wellbeing
mh["Wellbeing"] = mh[["General happiness-0.0", "General happiness with own health-0.0", "Belief that own life is meaningful-0.0"]].sum(axis=1).astype(int)
# %% [markdown]
# ## Unusual experience
# %%
mh[["Ever believed in an un-real conspiracy against self-0.0",
"Ever believed in un-real communications or signs-0.0",
"Ever heard an un-real voice-0.0", "Ever seen an un-real vision-0.0"]] = mh[[
"Ever believed in an un-real conspiracy against self-0.0",
"Ever believed in un-real communications or signs-0.0",
"Ever heard an un-real voice-0.0", "Ever seen an un-real vision-0.0"]].replace({-818:0, -121:0})
# %%
# Unusual experience
# Heard unreal voice 20463 = yes
# OR Saw unreal vision 20471 = yes
# OR Believed unreal conspiracy 20468 = yes
# OR Believed unreal communication or signs 20474 = yes
unexp = [(mh['Ever heard an un-real voice-0.0'].iloc[i], mh['Ever seen an un-real vision-0.0'].iloc[i],
mh['Ever believed in an un-real conspiracy against self-0.0'].iloc[i],
mh['Ever believed in un-real communications or signs-0.0'].iloc[i]) for i in range(len(mh))]
unusualexp = [1 if (u[0] > 0 or u[1] > 0 or u[2] > 0 or u[3] > 0) else 0 for u in unexp]
#%% Add to the df
mh = mh.assign(Unusual_exp=unusualexp)
# Recent unusual experience
# Frequency in last year 20467>0
ususrec = [(mh['Frequency of unusual or psychotic experiences in past year-0.0'].iloc[i]) for i in range(len(mh))]
unusualexprecent = [1 if (u>0) else 0 for u in ususrec]
mh = mh.assign(Unusual_exp_recent=unusualexprecent)
# Unusual_exp_recent
Unusual_exp_recent = mh.loc[mh['Unusual_exp_recent'] == 1][['Frequency of unusual or psychotic experiences in past year-0.0']]
# %% [markdown]
# ## Self-harm
# %%
# Life not worth living
# 20479 life NWL = yes (1 or 2)
mh["Self-harm: Life not worth living"] = np.where(mh["Ever thought that life not worth living-0.0"] > 0, 1, 0)
# NWL
NWL = mh.loc[mh['Self-harm: Life not worth living'] == 1][["Ever thought that life not worth living-0.0"]]
# Self harm
# 20480 Self harmed = Yes
mh["Self-harmed"] = np.where(mh["Ever self-harmed-0.0"] > 0, 1, 0)
#%% SH
SH = mh.loc[mh['Self-harmed'] == 1][["Ever self-harmed-0.0"]]
# Non-suicidal self-harm
# 20480 Self harmed = Yes
# 20483 Attempted suicide = No
# 51 said "prefer not to answer"
nonsuish = [(mh['Ever self-harmed-0.0'].iloc[i], mh['Ever attempted suicide-0.0'].iloc[i]) for i in range(len(mh))]
nonsuiselfharm = [1 if (s[0] > 0 and s[1] == 0) else 0 for s in nonsuish]
mh = mh.assign(Non_suicidal_SH=nonsuiselfharm)
# Non_suicidal self-harm
Non_suicidal_SH = mh.loc[mh['Non_suicidal_SH'] == 1][['Ever self-harmed-0.0', 'Ever attempted suicide-0.0']]
# Suicide attempt
# 20483 Attempted suicide = Yes
mh["Sui_attempt"] = np.where(mh["Ever attempted suicide-0.0"] > 0, 1, 0)
Sui_attempt = mh.loc[mh['Sui_attempt'] == 1][["Ever attempted suicide-0.0"]]
# Replace
mh[["Ever thought that life not worth living-0.0",
"Ever contemplated self-harm-0.0", "Ever self-harmed-0.0"]] = mh[["Ever thought that life not worth living-0.0",
"Ever contemplated self-harm-0.0", "Ever self-harmed-0.0"]].replace({-818:0, -121:0})
# %% [markdown]
# ## Addictions
# %%
mh["Ever addicted to any substance or behaviour-0.0"] = mh["Ever addicted to any substance or behaviour-0.0"].replace({-818:0, -121:0})
mh["Addiction_ever"] = np.where(mh["Ever addicted to any substance or behaviour-0.0"] == 1, 1, 0)
mh['Substance_addiction'] = [1 if (sa[0] == 1 or sa[1] == 1 or sa[2] == 1) else 0 for sa in [(mh['Ever addicted to alcohol-0.0'].iloc[i], mh['Ever addicted to illicit or recreational drugs-0.0'].iloc[i],
mh['Ever addicted to prescription or over-the-counter medication-0.0'].iloc[i]) for i in range(len(mh))]]
mh['Current_addiction'] = [1 if (c[0] == 1 or c[1]== 1 or c[2] == 1 or c[3] == 1) else 0 for c in [(mh['Ongoing addiction or dependence on illicit or recreational drugs-0.0'].iloc[i], mh['Ongoing addiction or dependence to over-the-counter medication-0.0'].iloc[i],
mh['Ongoing addiction to alcohol-0.0'].iloc[i], mh['Ongoing behavioural or miscellanous addiction-0.0'].iloc[i]) for i in range(len(mh))]]
# %% [markdown]
# ## Any distress
# %%
columns_to_replace = ["Ever sought or received professional help for mental distress-0.0", "Ever suffered mental distress preventing usual activities-0.0"]
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
mh[column] = mh[column].replace(-121, column_median)
# %%
any_distress = []
for i in range(len(mh)):
if mh['Ever sought or received professional help for mental distress-0.0'].iloc[i] == 1 or mh['Ever suffered mental distress preventing usual activities-0.0'].iloc[i] == 1 or mh['Depression_ever'].iloc[i] == 1 or mh['GAD_ever'].iloc[i] == 1 or mh['Ever addicted to any substance or behaviour-0.0'].iloc[i] == 1 or mh['Mania'].iloc[i] == 1 or mh['Bipolar_1'].iloc[i] == 1 or mh['Bipolar_2'].iloc[i] == 1 or mh['PTSD'].iloc[i] == 1 or mh['Unusual_exp'].iloc[i] == 1 or mh['Unusual_exp_recent'].iloc[i] == 1 or mh['Self-harmed'].iloc[i] or any(mh[f'Mental health problems ever diagnosed by a professional-0.{j}'].iloc[i] > 0 for j in range(1, 17)):
any_distress_score = 1
else:
any_distress_score = 0
any_distress.append(any_distress_score)
mh = mh.assign(Any_distress=any_distress)
#mh["Any_distress"] = [1 if mh['Ever sought or received professional help for mental distress-0.0'].iloc[i] == 1 or mh['Ever suffered mental distress preventing usual activities-0.0'].iloc[i] == 1 or mh['Depression_ever'].iloc[i] == 1 or mh['GAD_ever'].iloc[i] == 1 or mh['Addiction_ever'].iloc[i] == 1 or mh['Mania'].iloc[i] == 1 or mh['Bipolar_1'].iloc[i] == 1 or mh['Bipolar_2'].iloc[i] == 1 or mh['PTSD'].iloc[i] == 1 or mh['Unusual_exp'].iloc[i] == 1 or mh['Unusual_exp_recent'].iloc[i] == 1 or mh['Self-harmed'].iloc[i] or any(mh[f'Mental health problems ever diagnosed by a professional-0.{j}'].iloc[i] > 0 for j in range(1, 17)) else 0 for i in range(len(mh))]
# %% [markdown]
# ## Happiness
# %%
columns_to_replace = ["Happiness-2.0", "Family relationship satisfaction-2.0", "Financial situation satisfaction-2.0", "Friendships satisfaction-2.0", "Work/job satisfaction-2.0", "Health satisfaction-2.0"]
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-1, column_median)
mh[column] = mh[column].replace(-3, column_median)
mh[["Happiness-2.0",
"Family relationship satisfaction-2.0",
"Financial situation satisfaction-2.0",
"Friendships satisfaction-2.0",
"Work/job satisfaction-2.0",
"Health satisfaction-2.0"]] = mh[["Happiness-2.0",
"Family relationship satisfaction-2.0",
"Financial situation satisfaction-2.0",
"Friendships satisfaction-2.0",
"Work/job satisfaction-2.0",
"Health satisfaction-2.0"]].replace({1:6, 2:5, 3:4, 4:3, 5:2, 6:1, 7:0})
# %%
columns_to_replace = [
'Recent poor appetite or overeating-0.0',
'Recent trouble concentrating on things-0.0',
'Recent changes in speed/amount of moving or speaking-0.0',
'Frequency of taking cannabis',
'Trouble falling or staying asleep, or sleeping too much-0.0',
'Recent feelings of tiredness or low energy-0.0',
'Recent lack of interest or pleasure in doing things-0.0',
'Recent feelings of depression-0.0',
'Recent feelings of inadequacy-0.0',
'Ever had period of mania / excitability-0.0',
'Ever had period extreme irritability-0.0',
'Recent thoughts of suicide or self-harm-0.0'
]
for column in columns_to_replace:
column_median = mh[column].median()
mh[column] = mh[column].replace(-818, column_median)
mh[column] = mh[column].replace(-121, column_median)
# %% [markdown]
# ## NS-12
# %% [markdown]
# N-12 (0–12)
#
# - Mood swings
# - Miserableness 1930
# - Irritability 1940
# - Sensitivity/hurt feelings 1950
# - Fed-up feelings 1960
# - Nervous feelings 1970
# - Worrier/anxious feelings 1980
# - Tense/“highly strung” 1990
# - Worry too long after embarrassment 2000
# - Suffer from “nerves” 2010
# - Loneliness, isolation 2020
# - Guilty feelings 2030
# %%
# N-12 (0–12)
print(mh[["Mood swings-2.0", "Miserableness-2.0", "Irritability-2.0", "Sensitivity / hurt feelings-2.0",
"Fed-up feelings-2.0", "Nervous feelings-2.0", "Worrier / anxious feelings-2.0", "Tense / 'highly strung'-2.0",
"Worry too long after embarrassment-2.0", "Suffer from 'nerves'-2.0", "Loneliness, isolation-2.0", "Guilty feelings-2.0"]].median())
# Replace - 1 / -3 with 0
mh[["Mood swings-2.0", "Miserableness-2.0", "Irritability-2.0", "Sensitivity / hurt feelings-2.0",
"Fed-up feelings-2.0", "Nervous feelings-2.0", "Worrier / anxious feelings-2.0", "Tense / 'highly strung'-2.0",
"Worry too long after embarrassment-2.0", "Suffer from 'nerves'-2.0", "Loneliness, isolation-2.0", "Guilty feelings-2.0"]] = mh[["Mood swings-2.0", "Miserableness-2.0", "Irritability-2.0", "Sensitivity / hurt feelings-2.0",
"Fed-up feelings-2.0", "Nervous feelings-2.0", "Worrier / anxious feelings-2.0", "Tense / 'highly strung'-2.0",
"Worry too long after embarrassment-2.0", "Suffer from 'nerves'-2.0", "Loneliness, isolation-2.0", "Guilty feelings-2.0"]].replace({-1:0, -3:0})
mh['NS12'] = mh[["Mood swings-2.0", "Miserableness-2.0", "Irritability-2.0", "Sensitivity / hurt feelings-2.0",
"Fed-up feelings-2.0", "Nervous feelings-2.0", "Worrier / anxious feelings-2.0", "Tense / 'highly strung'-2.0",
"Worry too long after embarrassment-2.0", "Suffer from 'nerves'-2.0", "Loneliness, isolation-2.0", "Guilty feelings-2.0"]].sum(axis=1).astype(int)
print(mh['NS12'].min())
print(mh['NS12'].max())
# %% [markdown]
# ## Probable depression status
# %% [markdown]
# Probable depression status
#
# - Ever depressed 4598 OR Ever unenthusiastic/disinterested 4631
# - Duration of the longest period of depression 4609
# - Duration of the longest period of unenthusiasm/disinterest 5375
# - Seen doctor (GP) for nerves, anxiety, tension, and depression 2090
# - Seen psychiatrist for nerves, anxiety, tension, and depression 2100
#
#
# - Depression status was set to 1 if subjects responded
# - yes to variable IDs 4598 OR 4631 (ever depressed ever unenthusiastic/disinterested),
# - AND reported a duration of at least 1 week to variable IDs 4609 OR 5375 (depressionjunenthusiasm/disinterest)
# - AND had seen either a GP or psychiatrist for nerves, anxiety, tension, depression (i.e., responded yes to variable IDs, 2090 or 2100)
# %%
# Probable depression status
pdslist = [(mh['Ever depressed for a whole week-2.0'].iloc[i],
mh['Ever unenthusiastic/disinterested for a whole week-2.0'].iloc[i],
mh['Longest period of depression-2.0'].iloc[i],
mh['Longest period of unenthusiasm / disinterest-2.0'].iloc[i],
mh['Seen doctor (GP) for nerves, anxiety, tension or depression-2.0'].iloc[i],
mh['Seen a psychiatrist for nerves, anxiety, tension or depression-2.0'].iloc[i]) for i in range(len(mh))]
pds = []
for p in pdslist:
if (p[0] > 0 or p[1] > 0) and (p[2] >= 1 or p[3] >= 1) and (p[4] > 0 or p[5] > 0):
pds_score = 1
else:
pds_score = 0
pds.append(pds_score)
mh = mh.assign(PDS=pds)
# %%
# Replace -1/-3 - 0
mh[["Risk taking-2.0", "Seen a psychiatrist for nerves, anxiety, tension or depression-2.0", "Seen doctor (GP) for nerves, anxiety, tension or depression-2.0",
"Ever unenthusiastic/disinterested for a whole week-2.0", "Ever highly irritable/argumentative for 2 days-2.0", "Ever manic/hyper for 2 days-2.0"]] = mh[["Risk taking-2.0",
"Seen a psychiatrist for nerves, anxiety, tension or depression-2.0", "Seen doctor (GP) for nerves, anxiety, tension or depression-2.0",
"Ever unenthusiastic/disinterested for a whole week-2.0", "Ever highly irritable/argumentative for 2 days-2.0", "Ever manic/hyper for 2 days-2.0"]].replace({-1:0, -3:0})
# -1/-3 - 1
mh["Ever depressed for a whole week-2.0"] = mh["Ever depressed for a whole week-2.0"].replace({-1:3, -3:3})
# %% [markdown]
# ## Recent depressive symptoms
# %% [markdown]
# RDS-4 (4-16):
#
# - Frequency of depressed mood in last 2 weeks 2050
# - Frequency of unenthusiasm/disinterest in last 2 weeks 2060
# - Frequency of tenseness/restlessness in last 2 weeks 2070
# - Frequency of tiredness/lethargy in last 2 weeks 2080
# %%
# RDS-4 (4-16):
print(mh[['Frequency of depressed mood in last 2 weeks-2.0','Frequency of unenthusiasm / disinterest in last 2 weeks-2.0',
'Frequency of tenseness / restlessness in last 2 weeks-2.0', 'Frequency of tiredness / lethargy in last 2 weeks-2.0']].median())
# Replace -3 and -1
mh[['Frequency of depressed mood in last 2 weeks-2.0','Frequency of unenthusiasm / disinterest in last 2 weeks-2.0',
'Frequency of tenseness / restlessness in last 2 weeks-2.0', 'Frequency of tiredness / lethargy in last 2 weeks-2.0']] = mh[['Frequency of depressed mood in last 2 weeks-2.0','Frequency of unenthusiasm / disinterest in last 2 weeks-2.0',
'Frequency of tenseness / restlessness in last 2 weeks-2.0', 'Frequency of tiredness / lethargy in last 2 weeks-2.0']].replace({-1:1, -3:1})
# Calculate the score
mh['RDS4'] = mh[['Frequency of depressed mood in last 2 weeks-2.0','Frequency of unenthusiasm / disinterest in last 2 weeks-2.0',
'Frequency of tenseness / restlessness in last 2 weeks-2.0', 'Frequency of tiredness / lethargy in last 2 weeks-2.0']].sum(axis=1).astype(int)
print('RDS MIN', mh['RDS4'].min())
print('RDS MAX', mh['RDS4'].max())
# %% [markdown]
# # Select scores
# %%
mh_main = mh[['eid',
'Anxiety/panic attacks',
'NS12',
'PCL6',
'PHQ9',
'PDS',
'RDS4',
'Depression',
'Self-harm: Life not worth living', #Self-harm: Ever thought life not worth living" yes/no
'Sui_attempt',
'Self-harmed',
'Non_suicidal_SH',
'Unusual_exp',
'Unusual_exp_recent',
'Repeated disturbing thoughts of stressful experience in past month-0.0',
'Felt very upset when reminded of stressful experience in past month-0.0',
'Avoided activities or situations because of previous stressful experience in past month-0.0',
'Ever thought that life not worth living-0.0', #'Frequency of "life not worth living" thoughts',
'Ever contemplated self-harm-0.0',
'Ever had prolonged feelings of sadness or depression-0.0',
'Ever had prolonged loss of interest in normal activities-0.0',
'Ever felt worried, tense, or anxious for most of a month or longer-0.0',
'Ever worried more than most people would in similar situation-0.0',
'Ever addicted to any substance or behaviour-0.0',
'Ever believed in an un-real conspiracy against self-0.0',
'Ever believed in un-real communications or signs-0.0',
'Ever heard an un-real voice-0.0',
'Ever seen an un-real vision-0.0',
'Mood swings-2.0',
'Miserableness-2.0',
'Irritability-2.0',
'Sensitivity / hurt feelings-2.0',
'Fed-up feelings-2.0',
'Nervous feelings-2.0',
'Worrier / anxious feelings-2.0',
"Tense / 'highly strung'-2.0",
'Worry too long after embarrassment-2.0',
"Suffer from 'nerves'-2.0",
'Loneliness, isolation-2.0',
'Guilty feelings-2.0',
'Risk taking-2.0',
'Frequency of depressed mood in last 2 weeks-2.0',
'Frequency of unenthusiasm / disinterest in last 2 weeks-2.0',
'Frequency of tenseness / restlessness in last 2 weeks-2.0',
'Frequency of tiredness / lethargy in last 2 weeks-2.0',
'Seen a psychiatrist for nerves, anxiety, tension or depression-2.0',
'Seen doctor (GP) for nerves, anxiety, tension or depression-2.0',
'Ever depressed for a whole week-2.0',
'Ever unenthusiastic/disinterested for a whole week-2.0',
'Ever highly irritable/argumentative for 2 days-2.0',
'Ever manic/hyper for 2 days-2.0',
#"Diagnoses 'F'",
#"Diagnoses 'G'",
'Neurological problem, NS injury, epilepsy',
'Stress, insomnia, migraine, nervous/mental problems',
'Ever had period of mania / excitability-0.0',
'Ever had period extreme irritability-0.0',
'Recent feelings of inadequacy-0.0',
'Recent trouble concentrating on things-0.0',
'Recent feelings of depression-0.0',
'Recent poor appetite or overeating-0.0',
'Recent thoughts of suicide or self-harm-0.0',
'Recent lack of interest or pleasure in doing things-0.0',
'Trouble falling or staying asleep, or sleeping too much-0.0',
'Recent changes in speed/amount of moving or speaking-0.0',
'Recent feelings of tiredness or low energy-0.0',
'Depression_ever',
'Depression_subthreshold',
'Bipolar_1',
'Bipolar_2',
'Depression_single',
'Depression_recurrent',
'Depression_triggered_by_loss',
'Depression_current',
'Depression_current_severe',
#'Mania',
'Recent easy annoyance or irritability-0.0',
'Recent feelings or nervousness or anxiety-0.0',
'Recent inability to stop or control worrying-0.0',
'Recent feelings of foreboding-0.0',
'Recent trouble relaxing-0.0',
'Recent restlessness-0.0',
'Recent worrying too much about different things-0.0',
'GAD7',
'GAD_ever',
'GAD_current',
'GAD_current_mild',
'GAD_current_moderate',
'GAD_current_severe',
'PTSD',
'Amount of alcohol drunk on a typical drinking day-0.0',
'Frequency of drinking alcohol-0.0',
'Frequency of consuming six or more units of alcohol-0.0',
'AUDIT',
'AUDIT_log',
'AUDIT_C',
'AUDIT_P',
'AUDIT_C_log',
'AUDIT_P_log',
'Hazardous_Alcohol_Use',
'Alcohol_Dependence',
'General happiness-0.0',
'General happiness with own health-0.0',
'Belief that own life is meaningful-0.0',
'Felt hated by family member as a child-0.0',
'Physically abused by family as a child-0.0',
'Felt loved as a child-0.0',
'Sexually molested as a child-0.0',
'Someone to take to doctor when needed as a child-0.0',
'Ever sought or received professional help for mental distress-0.0',
'Ever suffered mental distress preventing usual activities-0.0',
'Belittlement by partner or ex-partner as an adult-0.0',
'Been in a confiding relationship as an adult-0.0',
'Physical violence by partner or ex-partner as an adult-0.0',
'Sexual interference by partner or ex-partner without consent as an adult-0.0',
'Able to pay rent/mortgage as an adult-0.0',
'Been in serious accident believed to be life-threatening-0.0',
'Been involved in combat or exposed to war-zone-0.0',
'Diagnosed with life-threatening illness-0.0',
'Victim of physically violent crime-0.0',
'Witnessed sudden violent death-0.0',
'Victim of sexual assault-0.0',
#'Addiction_ever',
'Alcohol_depencence_ever',
'Substance_addiction',
'Current_addiction',
'Cannabis ever',
'Frequency of taking cannabis',
'Cannabis daily',
'Childhood_adverse_events',
'Adult_adverse_events',
'Catastrophic_trauma',
'Wellbeing',
'Any_distress',
'Friendships satisfaction-2.0',
'Financial situation satisfaction-2.0',
'Happiness-2.0',
'Family relationship satisfaction-2.0',
'Health satisfaction-2.0']]
mh_main.to_csv('/Cog-Ment/CSVs/2024/mental_health/mental_health_no_diagnoses_all_scores.csv', index=False)
# %% [markdown]
# # ICD Diagnoses
# %%
# Work out ICD diagnoses
diag_cols = ['Diagnoses - main ICD10-0.0',
'Diagnoses - main ICD10-0.1',
'Diagnoses - main ICD10-0.2',
'Diagnoses - main ICD10-0.3',
'Diagnoses - main ICD10-0.4',
'Diagnoses - main ICD10-0.5',
'Diagnoses - main ICD10-0.6',
'Diagnoses - main ICD10-0.7',
'Diagnoses - main ICD10-0.8',
'Diagnoses - main ICD10-0.9',
'Diagnoses - main ICD10-0.10',
'Diagnoses - main ICD10-0.11',
'Diagnoses - main ICD10-0.12',
'Diagnoses - main ICD10-0.13',
'Diagnoses - main ICD10-0.14',
'Diagnoses - main ICD10-0.15',
'Diagnoses - main ICD10-0.16',
'Diagnoses - main ICD10-0.17',
'Diagnoses - main ICD10-0.18',
'Diagnoses - main ICD10-0.19',
'Diagnoses - main ICD10-0.20',
'Diagnoses - main ICD10-0.21',
'Diagnoses - main ICD10-0.22',
'Diagnoses - main ICD10-0.23',
'Diagnoses - main ICD10-0.24',
'Diagnoses - main ICD10-0.25',
'Diagnoses - main ICD10-0.26',
'Diagnoses - main ICD10-0.27',
'Diagnoses - main ICD10-0.28',
'Diagnoses - main ICD10-0.29',
'Diagnoses - main ICD10-0.30',
'Diagnoses - main ICD10-0.31',
'Diagnoses - main ICD10-0.32',
'Diagnoses - main ICD10-0.33',
'Diagnoses - main ICD10-0.34',
'Diagnoses - main ICD10-0.35',
'Diagnoses - main ICD10-0.36',
'Diagnoses - main ICD10-0.37',
'Diagnoses - main ICD10-0.38',
'Diagnoses - main ICD10-0.39',
'Diagnoses - main ICD10-0.40',
'Diagnoses - main ICD10-0.41',
'Diagnoses - main ICD10-0.42',
'Diagnoses - main ICD10-0.43',
'Diagnoses - main ICD10-0.44',
'Diagnoses - main ICD10-0.45',
'Diagnoses - main ICD10-0.46',
'Diagnoses - main ICD10-0.47',
'Diagnoses - main ICD10-0.48',
'Diagnoses - main ICD10-0.49',
'Diagnoses - main ICD10-0.50',
'Diagnoses - main ICD10-0.51',
'Diagnoses - main ICD10-0.52',
'Diagnoses - main ICD10-0.53',
'Diagnoses - main ICD10-0.54',
'Diagnoses - main ICD10-0.55',
'Diagnoses - main ICD10-0.56',
'Diagnoses - main ICD10-0.57',
'Diagnoses - main ICD10-0.58',
'Diagnoses - main ICD10-0.59',
'Diagnoses - main ICD10-0.60',
'Diagnoses - main ICD10-0.61',
'Diagnoses - main ICD10-0.62',
'Diagnoses - main ICD10-0.63',
'Diagnoses - main ICD10-0.64',
'Diagnoses - main ICD10-0.65',
'Diagnoses - main ICD10-0.66',
'Diagnoses - main ICD10-0.67',
'Diagnoses - main ICD10-0.68',
'Diagnoses - main ICD10-0.69',
'Diagnoses - main ICD10-0.70',
'Diagnoses - main ICD10-0.71',
'Diagnoses - main ICD10-0.72',
'Diagnoses - main ICD10-0.73',
'Diagnoses - main ICD10-0.74',
'Diagnoses - main ICD10-0.75',
'Diagnoses - main ICD10-0.76',
'Diagnoses - main ICD10-0.77',
'Diagnoses - main ICD10-0.78',
'Diagnoses - main ICD10-0.79']
# F
diagnoses_main_icd["all_diag"] = diagnoses_main_icd[diag_cols].astype(str).apply(lambda x: x.str.cat(sep=","), axis=1)
diagnoses_main_icd["F0 Organic (incl. symptomatic, mental disorders)"] = diagnoses_main_icd["all_diag"].str.contains("F0").astype(int)
diagnoses_main_icd["F1 Mental and behavioural disorders due to psychoactive substance use"] = diagnoses_main_icd["all_diag"].str.contains("F1").astype(int)
diagnoses_main_icd["F2 Schizophrenia, schizotypal and delusional disorders"] = diagnoses_main_icd["all_diag"].str.contains("F2").astype(int)
diagnoses_main_icd["F3 Mood [affective] disorders"] = diagnoses_main_icd["all_diag"].str.contains("F3").astype(int)
diagnoses_main_icd["F4 Neurotic, stress-related and somatoform disorders"] = diagnoses_main_icd["all_diag"].str.contains("F4").astype(int)
diagnoses_main_icd["F5 Behavioural syndromes associated with physiological disturbances and physical factors"] = diagnoses_main_icd["all_diag"].str.contains("F5").astype(int)
diagnoses_main_icd["F6 Disorders of adult personality and behaviour"] = diagnoses_main_icd["all_diag"].str.contains("F6").astype(int)
diagnoses_main_icd["F7 Mental retardation"] = diagnoses_main_icd["all_diag"].str.contains("F7").astype(int)
diagnoses_main_icd["F8 Disorders of psychological development"] = diagnoses_main_icd["all_diag"].str.contains("F8").astype(int)
diagnoses_main_icd["F90-F98 Behavioural and emotional disorders with onset usually occurring in childhood and adolescence"] = diagnoses_main_icd["all_diag"].str.contains("F9[0-8]").astype(int)
diagnoses_main_icd["F99 Unspecified mental disorder"] = diagnoses_main_icd["all_diag"].str.contains("F99").astype(int)
# G
diagnoses_main_icd["G0 Inflammatory diseases of the central nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G0").astype(int)
diagnoses_main_icd["G1 Systemic atrophies primarily affecting the central nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G1").astype(int)
diagnoses_main_icd["G2 Extrapyramidal and movement disorders"] = diagnoses_main_icd["all_diag"].str.contains("G2").astype(int)
diagnoses_main_icd["G30-G32 Other degenerative diseases of the nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G3[0-2]").astype(int)
diagnoses_main_icd["G35-G37 Demyelinating diseases of the central nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G3[5-7]").astype(int)
diagnoses_main_icd["G4 Episodic and paroxysmal disorders"] = diagnoses_main_icd["all_diag"].str.contains("G4").astype(int)
diagnoses_main_icd["G5 Nerve, nerve root and plexus disorders"] = diagnoses_main_icd["all_diag"].str.contains("G5").astype(int)
diagnoses_main_icd["G6 Polyneuropathies and other disorders of the peripheral nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G6").astype(int)
diagnoses_main_icd["G7 Diseases of myoneural junction and muscle"] = diagnoses_main_icd["all_diag"].str.contains("G7").astype(int)
diagnoses_main_icd["G8 Cerebral palsy and other paralytic syndromes"] = diagnoses_main_icd["all_diag"].str.contains("G8").astype(int)
diagnoses_main_icd["G9 Other disorders of the nervous system"] = diagnoses_main_icd["all_diag"].str.contains("G9").astype(int)
# Combine diagnoses
diagnoses_main_icd["Diagnoses 'F'"] = diagnoses_main_icd[['F0 Organic (incl. symptomatic, mental disorders)',
'F1 Mental and behavioural disorders due to psychoactive substance use',
'F2 Schizophrenia, schizotypal and delusional disorders',
'F3 Mood [affective] disorders',
'F4 Neurotic, stress-related and somatoform disorders',
'F5 Behavioural syndromes associated with physiological disturbances and physical factors',
'F6 Disorders of adult personality and behaviour',
'F7 Mental retardation',
'F8 Disorders of psychological development',
'F90-F98 Behavioural and emotional disorders with onset usually occurring in childhood and adolescence',
'F99 Unspecified mental disorder']].sum(axis=1).astype(int)
diagnoses_main_icd["Diagnoses 'G'"] = diagnoses_main_icd[["G0 Inflammatory diseases of the central nervous system",
"G1 Systemic atrophies primarily affecting the central nervous system",
"G2 Extrapyramidal and movement disorders",
"G30-G32 Other degenerative diseases of the nervous system",
"G35-G37 Demyelinating diseases of the central nervous system",
"G4 Episodic and paroxysmal disorders",
"G5 Nerve, nerve root and plexus disorders",
"G6 Polyneuropathies and other disorders of the peripheral nervous system",
"G7 Diseases of myoneural junction and muscle",
"G8 Cerebral palsy and other paralytic syndromes",
"G9 Other disorders of the nervous system"]].sum(axis=1).astype(int)
diagnoses_main_icd_FG = diagnoses_main_icd[['eid', "Diagnoses 'F'", "Diagnoses 'G'"]]
diagnoses_main_icd_FG.to_csv('/UK_BB/diagnoses/diagnoses_main_icd_FG.csv', index=False)
# %% [markdown]
# # Merge Diagnoses with Scores and rename
# %%
mh_full = mh_main.drop(columns=['AUDIT','AUDIT_C','AUDIT_P'])
mh_full = pd.merge(mh_full, diagnoses_main_icd_FG, on = 'eid')
mh_full.to_csv('/UK_BB/mental_health/mental_health_full.csv')
mh_full.shape
# %%
mh_full.columns.to_list()
# %%
# Rename columns
new_names = ['eid',
'Diagnoses: Anxiety/panic attacks',
'NS-12',
'PCL-6',
'PHQ-9',
'PDS',
'RDS-4',
'Diagnoses: Depression',
'Self-harm: Ever thought life not worth living',
'Ever attempted suicide',
'Ever self-harmed',
'Ever self-harmed (non-suicidal)',
'Unusual experience',
'Recent unusual experience',
'Repeated disturbing thoughts of stressful experience in past month',
'Felt very upset when reminded of stressful experience in past month',
'Avoided activities or situations because of previous stressful experience in past month',
"Frequency of 'life not worth living' thoughts",
'Lifetime frequency of contemplating self-harm',
'Ever had prolonged feelings of sadness or depression',
'Ever had prolonged loss of interest in normal activities',
'Ever felt worried, tense, or anxious for most of a month or longer',
'Ever worried more than most people would in similar situation',
'Ever addicted to any substance or behaviour',
'Ever believed in an un-real conspiracy against self',
'Ever believed in un-real communications or signs',
'Ever heard an un-real voice',
'Ever seen an un-real vision',
'Mood swings',
'Miserableness',
'Irritability',
'Sensitivity / hurt feelings',
'Fed-up feelings',
'Nervous feelings',
'Worrier / anxious feelings',
"Tense / 'highly strung'",
'Worry too long after embarrassment',
"Suffer from 'nerves'",
'Loneliness, isolation',
'Guilty feelings',
'Risk taking',
'Frequency of depressed mood in last 2 weeks',
'Frequency of unenthusiasm / disinterest in last 2 weeks',
'Frequency of tenseness / restlessness in last 2 weeks',
'Frequency of tiredness / lethargy in last 2 weeks',
'Seen a psychiatrist for nerves, anxiety, tension or depression',
'Seen doctor (GP) for nerves, anxiety, tension or depression',
'Ever depressed for a whole week',
'Ever unenthusiastic/disinterested for a whole week',
'Ever highly irritable/argumentative for 2 days',
'Ever manic/hyper for 2 days',
'Diagnoses: Neurological problem, NS injury, epilepsy',
'Diagnoses: Stress, insomnia, migraine, nervous/mental problems',
'Ever had period of mania / excitability',
'Ever had period extreme irritability',
'Recent feelings of inadequacy',
'Recent trouble concentrating on things',
'Recent feelings of depression',
'Recent poor appetite or overeating',
'Recent thoughts of suicide or self-harm',
'Recent lack of interest or pleasure in doing things',
'Trouble falling or staying asleep, or sleeping too much',
'Recent changes in speed/amount of moving or speaking',
'Recent feelings of tiredness or low energy',
'Depression ever',
'Subthreshold depression',
'Bipolar I',
'Bipolar II',
'Depression single episode',
'Recurrent depression',
'Depression triggered by loss',
'Current depression',
'Current severe depression',
# Mania score
'Recent easy annoyance or irritability',
'Recent feelings or nervousness or anxiety',
'Recent inability to stop or control worrying',
'Recent feelings of foreboding',
'Recent trouble relaxing',
'Recent restlessness',
'Recent worrying too much about different things',
'GAD-7',
'GAD ever',
'Current GAD',
'Current GAD mild',
'Current GAD moderate',
'Current GAD severe',
'PTSD',
'Amount of alcohol drunk on a typical drinking day',
'Frequency of drinking alcohol',
'Frequency of consuming six or more units of alcohol',
'(log)AUDIT',
'(log)AUDIT-C',
'(log)AUDIT-P',
'Hazardous alcohol use (AUDIT≥8)',
'Alcohol dependence (AUDIT≥15)',
'General happiness',
'General happiness with own health',
'Belief that own life is meaningful',
'Felt hated by family member as a child',
'Physically abused by family as a child',
'Felt loved as a child',
'Sexually molested as a child',
'Someone to take to doctor when needed as a child',
'Ever sought or received professional help for mental distress',
'Ever suffered mental distress preventing usual activities',
'Belittlement by partner or ex-partner as an adult',
'Been in a confiding relationship as an adult',
'Physical violence by partner or ex-partner as an adult',
'Sexual interference by partner or ex-partner without consent as an adult',
'Able to pay rent/mortgage as an adult',
'Been in serious accident believed to be life-threatening',
'Been involved in combat or exposed to war-zone',
'Diagnosed with life-threatening illness',
'Victim of physically violent crime',
'Witnessed sudden violent death',
'Victim of sexual assault',
# Addiction ever
'Physical alcohol dependence ever',
'Substance addiction',
'Current addiction',
'Cannabis ever',
'Lifertime frequency of taking cannabis',
'Cannabis daily',
'Childhood adverse events',
'Adult adverse events',
'Catastrophic trauma',
'Wellbeing',
'Any distress',
'Friendships satisfaction',
'Financial situation satisfaction',
'Happiness',
'Family relationship satisfaction',
'Health satisfaction',
'Mental and behavioural disorders',
'Diseases of the nervous system'
]
mh_rename = mh_full.copy()
mh_rename.columns = new_names
mh_rename.to_csv('/Cog-Ment/CSVs/2024/mental_health/mental_health_full_renamed.csv', index=False)

01_GetMHData.ipynb at commit 633419e, under MIT · at the source

Overview

Authors: Irina Buianova¹, Mateus Silvestrin^2,3, Jeremiah D Deng⁴, Narun Pat¹

ORCID iDs: Irina Buianova, Mateus Silvestrin, Jeremiah D Deng, Narun Pat

Department of Psychology, University of Otago Dunedin New Zealand
Federal University of the São Francisco Valley Petrolina Brazil
National Institute of Social and Affective Neuroscience Petrolina Brazil
School of Computing, University of Otago Dunedin New Zealand

Institutions: University of Otago (New Zealand); Universidade Federal do Vale do São Francisco (Brazil)

Journal: eLife, volume 14, article RP108109

Dates: published online 20 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.108109 · PMID 42160203 · PMCID PMC13189626 · OpenAlex W4414263040

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)

Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing, Evoked potentials, fMRI & imaging, Physiology & signal measures

Keywords: cognition, mental health, neuroimaging, MRI, machine learning, Human

MeSH: Cognition*, Magnetic Resonance Imaging*, Mental Health*, Biological Specimen Banks, Brain, Female, Humans, Machine Learning, Male, Neuroimaging, UK Biobank, United Kingdom (* major topic)

Journal subjects: Neuroscience

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Health Research Council of New Zealand (21/618, 24/838); Neurological Foundation of New Zealand (2350 PRG); Ministry of Business, Innovation and Employment (UOA2421, RTVU2403); University of Otago (The University of Otago Doctoral Scholarship)

Citations: cited by 2 papers (Europe PMC); 236 references in the paper

Abstract

Cognitive dysfunction often co-occurs with psychopathology. Advances in neuroimaging and machine learning have led to neural indicators that predict individual differences in cognition with reasonable performance. We examined whether these indicators explain the relationship between cognition and mental health in the UK Biobank (n>14,000). Using machine learning, we quantified the covariation between cognition and 133 mental health indices and derived neural indicators of cognition from 72 neuroimaging phenotypes across diffusion-weighted MRI (dwMRI), resting-state functional MRI (rsMRI), and structural MRI (sMRI). With commonality analyses, we investigated how much of the cognition–mental health covariation is captured by each indicator and neural indicators combined within and across MRI modalities. The predictive association between mental health and cognition was at r=0.3. Neuroimaging captured 2.1 to 25.8% of the cognition-mental health covariation. Combining phenotypes within modalities improved the explanation to 25.5% for dwMRI, 29.8% for rsMRI, and 31.6% for sMRI, and combining them across modalities enhanced the explanation to 48%. We present an integrated approach to derive multimodal MRI markers of cognition that can be transdiagnostically linked to psychopathology, demonstrating that the predictive ability of neural indicators extends beyond the prediction of cognition itself, enabling us to capture cognition-mental health covariation.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 18 matches between paragraphs and lines of code.

HAM-lab-Otago-University/UKBiobank

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 633419e8593d3d768c719f0e81d58dd6e345ce7c, 20 May 2026

Languages: Jupyter (27), R (3)

Size: 33 files, 30 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file, 30 notebooks

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

Tools: pandas (11 files), NumPy (10 files), Matplotlib (6 files), SciPy (6 files), scikit-learn (5 files), seaborn (5 files), data.table (2 files), ggplot2 (2 files), lavaan (2 files), psych (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

14 files

1_Cognitive_preprocessing/01_GetCogData.ipynb, Jupyter, 207 lines
1_Cognitive_preprocessing/02_CogData_Descriptive_5Folds.ipynb, Jupyter, 272 lines, 1 match
1_Cognitive_preprocessing/03_GetGFactor_5Folds.Rmd, R, 336 lines, 1 match
1_Cognitive_preprocessing/04_GetGFactor_SingleSplit.Rmd, R, 135 lines, 2 matches
2_Mental_Health/01_GetMHData.ipynb, Jupyter, 1,989 lines, 4 matches
2_Mental_Health/02_MHData_Descriptives.Rmd, R, 50 lines
3_MRI_preprocessing/01_GetMRIData_dwMRI_IDP.ipynb, Jupyter, 1,627 lines, 3 matches
3_MRI_preprocessing/02_GetMRIData_dwMRI_Parcellations.ipynb, Jupyter, 2,683 lines
3_MRI_preprocessing/03_GetMRIData_sMRI.ipynb, Jupyter, 2,942 lines, 1 match
3_MRI_preprocessing/04_GetMRIData_rsMRI_IDP_FullPartCorr_Confounds.ipynb, Jupyter, 1,539 lines, 2 matches
3_MRI_preprocessing/05_GetMRIData_rsMRI_Parcellations.ipynb, Jupyter, 1,228 lines
3_MRI_preprocessing/06_GetMRI_rsMRI_Parcellations_Get2DMatrix.ipynb, Jupyter, 178 lines
4_PLS/01_PLS_MH_5Folds.ipynb, Jupyter, 329 lines, 2 matches
4_PLS/02_1_PLS_MH_SingleSplit.ipynb, Jupyter, 984 lines, 2 matches
repository limit reached (2,000 files or 30 MB): the rest is at the source (18 files)

At the source: github.com/HAM-lab-Otago-University/UKBiobank/

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
14 scripts, each with its path and the digest of its content;
18 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

ukbiobank.ac.uk/enable-your-research/apply-for-access, at UK Biobank; found in “Data availability”

Data availability

This study used data from the UK Biobank resource (Application No. 70132). These data cannot be publicly shared by the authors due to legal and ethical restrictions imposed by UK Biobank. Access to individual-level data is governed by UK Biobank's data access policies, which are designed to protect participant confidentiality. Researchers can access the data by submitting an application directly to UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). Applications must include a research proposal outlining the scientific rationale and intended use of the data, and are reviewed as part of UK Biobank's access procedures. Access may be granted to approved researchers from academic, charity, government, and commercial organisations, subject to UK Biobank's terms and conditions. Due to these restrictions, we are not permitted to share raw or deidentified individual-level data. Redistribution of such data is contractually prohibited, and deidentification does not eliminate the risk of participant re-identification. This manuscript is a computational study and did not generate new primary data. Numerical data underlying the figures are provided as figure source data files. All analyses can be reproduced using the code provided by the authors. The modelling and analysis code is openly available on GitHub: https://github.com/HAM-lab-Otago-University/UKBiobank/ (copy archived at Buianova, 2026).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 12 MeSH terms, 4 funders, 228 references.

Cite

This paper

Buianova, I., Silvestrin, M., Deng, J. D., & Pat, N. (2026). Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank. eLife, 14, RP108109. https://doi.org/10.7554/elife.108109

BibTeX

@article{buianova2026multimodal, author = {Buianova, Irina and Silvestrin, Mateus and Deng, Jeremiah D and Pat, Narun}, title = {{Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank}}, journal = {eLife}, year = {2026}, month = may, volume = {14}, pages = {RP108109}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.108109}, url = {https://doi.org/10.7554/elife.108109}, pmid = {42160203}, pmcid = {PMC13189626} }

RIS

TY - JOUR AU - Buianova, Irina AU - Silvestrin, Mateus AU - Deng, Jeremiah D AU - Pat, Narun TI - Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank T2 - eLife J2 - Elife PY - 2026 DA - 2026/05/20 VL - 14 SP - RP108109 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.108109 UR - https://doi.org/10.7554/elife.108109 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.108109", "type": "article-journal", "title": "Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank", "container-title": "eLife", "author": [ { "family": "Buianova", "given": "Irina" }, { "family": "Silvestrin", "given": "Mateus" }, { "family": "Deng", "given": "Jeremiah D" }, { "family": "Pat", "given": "Narun" } ], "container-title-short": "Elife", "volume": "14", "page": "RP108109", "DOI": "10.7554/elife.108109", "PMID": "42160203", "PMCID": "PMC13189626", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.108109", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 20 ] ] } }

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