OSCR

World Trade Center neurotoxicant exposure severity is associated with cortical thinning and regional brain atrophy: a neuroimaging study of general responders at midlife.

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Paper

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The authors' code

Stata · 252 lines · 12 KB · no license

  1. ** Code for WTC-NESI Exposure Score
  3. use EAQV1.dta , clear
  6. ** cleaning and making constructs that could be useful for exposure assessment **
  7. ren eaq_16_01 respi
  9. foreach x in eaq_09_03 eaq_09_04 eaq_09_05 eaq_09_06 eaq_09_07 eaq_09_08 eaq_09_09 eaq_09_10 eaq_09_11 eaq_09_12 eaq_09_13 eaq_09_14 eaq_09_15 eaq_09_16 eaq_09_17 eaq_09_18 eaq_09_19 eaq_09_20 eaq_09_21 eaq_09_22 eaq_09_23 eaq_09_24 eaq_10_04 eaq_10_10 eaq_10_16 {
  10. quietly replace `x' = 0 if `x' > 1000000
  11. }
  13. gen septdays = 0
  14. foreach x in eaq_09_03 eaq_09_04 eaq_09_05 eaq_09_06 eaq_09_07 eaq_09_08 eaq_09_09 eaq_09_10 eaq_09_11 eaq_09_12 eaq_09_13 eaq_09_14 eaq_09_15 eaq_09_16 eaq_09_17 eaq_09_18 eaq_09_19 eaq_09_20 eaq_09_21 eaq_09_22 {
  15. quietly replace septdays = septdays + sign(`x')
  16. }
  18. ren eaq_10_02 octdays
  19. ren eaq_10_08 novdays
  20. ren eaq_10_14 jandays
  22. foreach x in sept oct nov jan {
  23. quietly replace `x'days = . if `x'days > 1000 | `x'days == 0
  24. quietly bysort mrn (`x'days): replace `x'days = `x'days[_n-1] if `x'days == .
  25. quietly replace `x'days = 0 if `x'days == .
  26. }
  28. replace respi = 0 if respi < 0
  29. replace respi = . if respi > 10
  31. gen alresp = int(respi/3)
  32. gen eresp = sign(respi)
  33. replace alresp = 0 if alresp == .
  34. replace eresp = 0 if eresp == .
  36. foreach x in eaq_11_04 eaq_12_02 eaq_12_13 eaq_12_24 eaq_12_35 eaq_12_04 eaq_12_15 eaq_12_26 eaq_12_37 eaq_12_08 eaq_12_19 eaq_12_30 eaq_12_41 eaq_11_04 eaq_11_05 eaq_11_06 eaq_11_15 eaq_11_16 eaq_11_17 eaq_11_26 eaq_11_27 eaq_11_28 eaq_11_37 eaq_11_38 eaq_11_39 {
  37. quietly replace `x' = . if `x' > 1
  38. quietly bysort mrn (`x') : replace `x' = `x'[_n-1] if `x' == .
  39. }
  40. foreach x in eaq_12_03 eaq_12_14 eaq_12_25 eaq_12_36 {
  41. quietly replace `x' = . if `x' > 2
  42. quietly replace `x' = sign(`x')
  43. quietly bysort mrn (`x') : replace `x' = `x'[_n-1] if `x' == .
  44. }
  46. alpha eaq_11_04 eaq_11_05 eaq_11_06 eaq_11_15 eaq_11_16 eaq_11_17 eaq_11_26 eaq_11_27 eaq_11_28 eaq_11_37 eaq_11_38 eaq_11_39 , gen(supervis)
  48. foreach x in eaq_13_01 eaq_13_02 eaq_13_03 eaq_13_04 eaq_13_05 eaq_13_06 eaq_13_07 eaq_13_08 eaq_13_09 eaq_13_10 eaq_13_11 eaq_13_12 eaq_13_13 eaq_13_14 eaq_13_15 eaq_13_16 eaq_13_17 eaq_13_18 eaq_13_19 eaq_13_20 eaq_13_21 eaq_13_22 eaq_13_23 eaq_13_24 eaq_13_25 eaq_13_26 eaq_13_27 eaq_13_28 eaq_13_29 eaq_13_30 eaq_13_31 eaq_13_32 eaq_13_33 eaq_13_34 eaq_13_35 eaq_13_36 eaq_13_37 eaq_13_38 eaq_13_39 eaq_13_40 eaq_13_41 eaq_13_42 {
  49. quietly replace `x' = 0 if `x' == 2
  50. quietly replace `x' = . if `x' > 10
  51. }
  53. rename eaq_13_0* eaq_13_*
  54. forv i = 1/42 {
  55. if mod(`i',3) == 1 quietly gen ir13_`i' = eaq_13_`i'*irisk
  56. if mod(`i',3) == 2 quietly gen nr13_`i' = eaq_13_`i'*nrisk
  57. if mod(`i',3) == 0 quietly gen jr13_`i' = eaq_13_`i'*jrisk
  58. }
  60. rename eaq_12_0* eaq_12_*
  61. local j = 1
  62. foreach x of numlist 2 13 24 35 3 14 25 36 4 15 26 37 8 19 30 41 {
  63. if mod(`j',4) == 1 quietly gen raq_12_`j' = eaq_12_`x'*srisk
  64. if mod(`j',4) == 2 quietly gen raq_12_`j' = eaq_12_`x'*orisk
  65. if mod(`j',4) == 3 quietly gen raq_12_`j' = eaq_12_`x'*nrisk
  66. if mod(`j',4) == 0 quietly gen raq_12_`j' = eaq_12_`x'*jrisk
  67. local j = `j' + 1
  68. }
  70. forv i = 1/54 {
  71. capture: quietly gen sac`i' = srisk if eaq_11_01 == `i'
  72. capture: quietly gen oac`i' = orisk if eaq_11_12 == `i'
  73. capture: quietly gen nac`i' = nrisk if eaq_11_23 == `i'
  74. capture: quietly gen jac`i' = jrisk if eaq_11_34 == `i'
  76. capture: quietly replace sac`i' = srisk*.3 if eaq_11_02 == `i' & sac`i' == .
  77. capture: quietly replace oac`i' = orisk*.3 if eaq_11_13 == `i' & oac`i' == .
  78. capture: quietly replace nac`i' = nrisk*.3 if eaq_11_24 == `i' & nac`i' == .
  79. capture: quietly replace jac`i' = jrisk*.3 if eaq_11_35 == `i' & jac`i' == .
  81. capture: quietly replace sac`i' = srisk*.1 if eaq_11_03 == `i' & sac`i' == .
  82. capture: quietly replace oac`i' = orisk*.1 if eaq_11_14 == `i' & oac`i' == .
  83. capture: quietly replace nac`i' = nrisk*.1 if eaq_11_25 == `i' & nac`i' == .
  84. capture: quietly replace jac`i' = jrisk*.1 if eaq_11_36 == `i' & jac`i' == .
  86. capture: quietly egen acr`i' = rowtotal(sac`i' oac`i' nac`i' jac`i' ) , m
  87. capture: quietly replace acr`i' = 0 if acr`i' == .
  88. }
  90. foreach x in ir13_1 nr13_2 jr13_3 ir13_4 nr13_5 jr13_6 ir13_7 nr13_8 jr13_9 ir13_10 nr13_11 jr13_12 ir13_13 nr13_14 jr13_15 ir13_16 nr13_17 jr13_18 ir13_19 nr13_20 jr13_21 ir13_22 nr13_23 jr13_24 ir13_25 nr13_26 jr13_27 ir13_28 nr13_29 jr13_30 ir13_31 nr13_32 jr13_33 ir13_34 nr13_35 jr13_36 ir13_37 nr13_38 jr13_39 ir13_40 nr13_41 jr13_42 raq_12_1 raq_12_2 raq_12_3 raq_12_4 raq_12_9 raq_12_10 raq_12_11 raq_12_12 raq_12_13 raq_12_14 raq_12_15 raq_12_16 sac1 oac1 nac1 jac1 sac2 oac2 nac2 jac2 sac3 oac3 nac3 jac3 sac4 oac4 nac4 jac4 sac5 oac5 nac5 jac5 sac6 oac6 nac6 jac6 sac7 oac7 nac7 jac7 sac8 oac8 nac8 jac8 sac9 oac9 nac9 jac9 sac10 oac10 nac10 jac10 sac11 oac11 nac11 jac11 sac12 oac12 nac12 jac12 sac13 oac13 nac13 jac13 sac14 oac14 nac14 jac14 sac15 oac15 nac15 jac15 sac16 oac16 nac16 jac16 sac17 oac17 nac17 jac17 sac18 oac18 nac18 jac18 sac19 oac19 nac19 jac19 sac20 oac20 nac20 jac20 sac21 oac21 nac21 jac21 sac22 oac22 nac22 jac22 sac23 oac23 nac23 jac23 sac24 oac24 nac24 jac24 sac25 oac25 nac25 jac25 sac26 oac26 nac26 jac26 sac27 oac27 nac27 jac27 sac28 oac28 nac28 jac28 sac29 oac29 nac29 jac29 sac30 oac30 nac30 jac30 sac31 oac31 nac31 jac31 sac32 oac32 nac32 jac32 sac33 oac33 nac33 jac33 sac34 oac34 nac34 jac34 sac35 oac35 nac35 jac35 sac36 oac36 nac36 jac36 sac37 oac37 nac37 jac37 sac38 oac38 nac38 jac38 sac39 oac39 nac39 jac39 sac40 oac40 nac40 jac40 sac41 oac41 nac41 jac41 sac42 oac42 nac42 jac42 sac43 oac43 nac43 jac43 sac44 oac44 nac44 jac44 sac45 oac45 nac45 jac45 sac46 oac46 nac46 jac46 sac47 oac47 nac47 jac47 sac48 oac48 nac48 jac48 sac49 oac49 nac49 jac49 sac50 oac50 nac50 jac50 sac51 oac51 nac51 jac51 sac52 oac52 nac52 jac52 sac53 oac53 nac53 jac53 sac54 oac54 nac54 jac54 {
  91. quietly bysort mrn (`x'): replace `x' = `x'[_n-1] if `x' == .
  92. quietly replace `x' = 0 if `x' == .
  93. }
  95. forv i = 1/54 {
  96. quietly gen pex`i' = sac`i' + 4*oac`i' + .5*nac`i' + .1*jac`i'
  97. quietly gen dex`i' = sign(int(pex`i'/8))
  98. }
  100. egen encd = rowtotal(raq_12_1 raq_12_2 raq_12_3 raq_12_4) , m
  101. egen cutd = rowtotal( raq_12_9 raq_12_10 raq_12_11 raq_12_12) , m
  102. egen cond = rowtotal( raq_12_13 raq_12_14 raq_12_15 raq_12_16) , m
  103. egen humd = rowtotal( ir13_1 nr13_2 jr13_3 ) , m
  104. egen blod = rowtotal( ir13_4 nr13_5 jr13_6 ) , m
  105. egen dstd = rowtotal( ir13_7 nr13_8 jr13_9 ) , m
  106. egen mold = rowtotal( ir13_10 nr13_11 jr13_12) , m
  107. egen chmd = rowtotal( ir13_13 nr13_14 jr13_15 ) , m
  108. egen fumd = rowtotal( ir13_16 nr13_17 jr13_18 ) , m
  109. egen swgd = rowtotal( ir13_19 nr13_20 jr13_21 ) , m
  110. egen hsud = rowtotal( ir13_22 nr13_23 jr13_24 ) , m
  111. egen smod = rowtotal( ir13_25 nr13_26 jr13_27 ) , m
  112. egen dsld = rowtotal( ir13_28 nr13_29 jr13_30 ) , m
  113. egen ndxd = rowtotal( ir13_31 nr13_32 jr13_33 ) , m
  114. egen ergd = rowtotal( ir13_34 nr13_35 jr13_36 ) , m
  115. egen temd = rowtotal( ir13_37 nr13_38 jr13_39 ) , m
  116. egen noid = rowtotal( ir13_40 nr13_41 jr13_42 ) , m
  118. qui gen human = .
  119. forv i = 1/3 {
  120. quietly replace human = eaq_13_`i' if (human == . | human == 0) & eaq_13_`i' < .
  121. }
  123. quietly bysort mrn (human): replace human = human[_n-1] if human== .
  125. gen blood_exposure = .
  126. foreach x in eaq_13_4 eaq_13_5 eaq_13_6 {
  127. quietly replace blood_exposure = 1 if `x' ==1
  128. quietly replace blood_exposure = 0 if `x' ==0 & blood_exposure == .
  129. }
  130. quietly bysort mrn (blood_exposure): replace blood_exposure = blood_exposure[_n-1] if blood_exposure== .
  132. lab def resp 0 "rarely/never" 1 "sometimes" 2 "most of the time" 3 "always"
  133. replace respi = respi - 1
  134. lab val respi resp
  135. replace respi = 0 if respi == 4 | respi == 5 | respi == 7
  136. replace respi = . if respi == 6
  137. gen pilesept = sign(int(eaq_12_1/2))
  138. gen pileoct = sign(int(eaq_12_12/2))
  139. gen human_remains = mod(eaq_13_1,2)
  140. replace human_remains = mod(eaq_13_2,2) if human_remains == . | human_remains == 0
  141. replace human_remains = mod(eaq_13_3,2) if human_remains == . | human_remains == 0
  142. gen fires = mod(eaq_13_25,2)
  144. quietly compress
  145. forv i = 1/1200 {
  146. capture: quietly labelrename labels`i' eaq_labels`i'
  147. if _rc == 111 continue, break
  148. }
  150. gen tdays = septdays + octdays + novdays + jandays
  151. gen wks11 = tdays/7
  153. gen fdworker = strmatch(eaq_01_02, "*fire*")
  154. gen ar911 = abs(sign(arrival-1)-1)
  155. gen dustc = abs(sign(arrivaldust-1)-1)
  156. replace everpile = 0 if everpile > 1
  157. gen temp_lrisk = sign(dex45+dex35+dex37+dex38+dex39+dex40)
  158. recode eaq_17_02 (1=1)(else=0), gen(tyvek)
  159. recode respi (2 3 =1)(else=0) , gen(resp)
  160. recode eaq_17_02 (1 2 4 5 6 =1)(else=0), gen(hoodsuit)
  161. gen ppe = int((hoodsuit + resp)/2)
  163. foreach x in acr2 acr3 acr4 acr5 acr6 acr7 acr8 acr9 acr10 acr11 acr12 acr13 acr14 acr15 acr16 acr17 acr18 acr20 acr23 acr25 acr26 acr28 acr29 acr30 acr31 acr32 acr33 acr34 acr35 acr36 acr37 acr38 acr39 acr40 acr41 acr42 acr43 acr44 acr45 acr46 acr47 acr48 acr49 acr50 acr51 acr52 acr53 acr54 {
  164. quietly gen d`x' = sign(int(`x'/16))
  165. }
  167. foreach x in encd cutd cond humd blod dstd mold chmd fumd swgd hsud smod dsld ndxd ergd temd noid {
  168. quietly gen d`x' = sign(int(`x'/10))
  169. }
  171. gen macri = sign(dacr48+dacr53+dacr50+dacr26+dacr17+dacr15+dacr11+dacr10+dacr9+dacr8+dacr6+dacr5+dacr4+dacr2)
  172. gen mspec = sign(dencd+dcond+dhumd+ddstd+dchmd+dfumd+dswgd+dhsud+dsmod+dergd+dtemd+dnoid)
  174. gen decon = 1 if (eaq_12_9 == 1 | eaq_12_9 == 4 | eaq_12_9 == 8) | (eaq_12_20 == 1 | eaq_12_20 == 4 | eaq_12_20 == 8) | (eaq_12_31 == 1 | eaq_12_31 == 4 | eaq_12_31 == 8) | (eaq_12_42 == 1 | eaq_12_42 == 4 | eaq_12_42 == 8)
  175. replace decon = 0 if decon == .
  177. gen nodustin = 0
  178. replace nodust = nodust+ abs(eaq_12_3-1)
  179. replace nodust = nodust+ abs(eaq_12_14-1)
  180. replace nodust = nodust+ abs(eaq_12_25-1)
  181. replace nodust = nodust+ abs(eaq_12_36-1)
  182. replace nodust = sign(nodust)
  183. gen ntrad = int(lawenfor/7)
  185. alpha eaq_12_3 eaq_12_14 eaq_12_25 eaq_12_36, gen(dusty)
  186. replace dusty = int(dusty)
  187. replace dusty = 0 if dusty == .
  188. gen dsearch = dusty*dex49
  190. gen temp_plax = temp_lrisk + ppe*10 + macri*100 + mspec*1000
  192. merge 1:1 mrn using MHSQ_2024.dta , gen(mer_mhsq_1) keepus(mhsq_13_12 mhsq_13_15 mhsq_13_17 lose_any jumper)
  194. alpha mhsq_13_12 mhsq_13_15 mhsq_13_17 lose_any jumper , gen(psych_exp) min(3)
  195. replace psych_exp = round(psych_exp*5,1)
  196. bysort mrn (psych_exp): replace psych_exp = psych_exp[_n-1] if psych_exp == .
  197. replace psych_exp = 4 if psych_exp == 5
  199. gen temp_styx = sign(int(psych/2))
  200. replace temp_styx = 0 if temp_styx == .
  202. ** creating the exposure assessment "WTC-NESI" **
  204. gen wtc_nesi = 0 if temp_lrisk == 1
  205. replace wtc_nesi = 1 if wtc_nesi == . & ppe == 1
  206. replace wtc_nesi = 2 if temp_plax == 110 | temp_plax == 1010
  207. replace wtc_nesi = 3 if temp_plax == 1000
  208. replace wtc_nesi = 4 if temp_plax == 0
  209. replace wtc_nesi = 5 if temp_plax == 100 | temp_plax == 1100 | temp_plax == 1110 | temp_plax == 1111
  210. replace wtc_nesi = 6 if zax == 2
  212. replace wtc_nesi = (wtc_nesi+.5*sign(int(wks11/4.54))*2
  213. replace wtc_nesi = 0 if wtc_nesi < 5
  214. replace wtc_nesi = 2*(wtc_nesi+.5*temp_styx)
  216. replace wtc_nesi = 1 if wtc_nesi< 15 & wtc_nesi > 0
  217. replace wtc_nesi = 2 if wtc_nesi< 20 & wtc_nesi > 14
  218. replace wtc_nesi = 3 if wtc_nesi>= 20 & wtc_nesi < .
  219. replace wtc_nesi = 0 if temp_lrisk == 1
  220. replace wtc_nesi = 0 if ppe == 1 | dacr13==1
  221. replace wtc_nesi = 1 if temp_styx == 1 & wtc_nesi == 0
  222. replace wtc_nesi = 4 if wtc_nesi == 3 & temp_styx == 1
  223. replace wtc_nesi = -1 if wtc_nesi == 0 & super == 1
  224. replace wtc_nesi = wtc_nesi+1
  225. replace wtc_nesi = 1 if wtc_nesi == 2 & super >= .6 & super < .
  226. replace wtc_nesi = wtc_nesi + ntrad if wtc_nesi > 0
  227. replace wtc_nesi = wtc_nesi+decon if wtc_nesi > 0
  228. replace wtc_nesi = wtc_nesi+dex43 if wtc_nesi > 0
  231. replace wtc_nesi = 1 if wtc_nesi > 1 & temp_lrisk == 1
  232. replace wtc_nesi = wtc_nesi - hoodsuit if wtc_nesi > 0
  233. replace wtc_nesi = dusty+wtc_nesi if wtc_nesi > 0
  234. replace wtc_nesi = wtc_nesi+ddstd if wtc_nesi > 0
  235. replace wtc_nesi = wtc_nesi + dacr18 if wtc_nesi > 0
  236. replace wtc_nesi = 0 if wtc_nesi < 0
  237. recode wtc_nesi (0 =0)(1 2 = 1)(3 = 2)(4 5=3)(6=4)(7 8 9 = 5)
  238. replace wtc_nesi = wtc_nesi + int(wks11/23) if wtc_nesi > 0 & wtc_nesi < 5
  239. replace wtc_nesi = 2 if wtc_nesi == 1
  241. drop sac* nac* jac* oac* dacr* temp_*
  243. ** output: score used in WTC Lung Cancer study **
  245. gen wtc_lc_exposure = wtc_nesi -2 if wtc_nesi > 0
  246. replace wtc_lc_exposure = 2 if qual_exp_ci == 3
  248. ** output: score used in WTC Lung Cancer study **
  250. keep mrn wtc_lc_exposure
  252. save WTC_Exposure.dta , replace

OSF_Code_EAQ_Exposure.do, no license · at the source

Overview

Authors: Sean A P Clouston1,2, Chuan Huang3, Temurbek Askarov4, Yicun Wang5, Zan Chaudhry6, Melissa A Carr6, Yuan Yang1, Steven West5, Frank D Mann1,7, Ruth Marquez8, Paul Vaska5,9, Jaymie Meliker1,2, Sam Gandy10,11, Roberto G Lucchini8, Benjamin J Luft6,7, for the Alzheimer's Disease Neuroimaging Initiative
ORCID iDs: Sean A P Clouston
  1. Program in Public Health, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
  2. Department of Family, Population, and Preventive Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
  3. Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
  4. Department of Chemistry, Stony Brook University, Stony Brook, New York, USA
  5. Department of Radiology, Division of Neuroradiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
  6. World Trade Center Health and Wellness Program, Commack, New York, USA
  7. Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
  8. Department of Environmental Health Sciences, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, USA
  9. Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
  10. Departments of Neurology and Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
  11. Division of Neurology, James J Peters VA Medical Center, Bronx, New York, USA
Institutions: Stony Brook University (United States); Emory University (United States); Florida International University (United States); James J. Peters VA Medical Center (United States); Icahn School of Medicine at Mount Sinai (United States)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 10, article e71878
Dates: received 25 March 2026; accepted 6 September 2026; published online 28 September 2026; in print October 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/alz.71878 · PMID 42805945 · PMCID PMC13619733 · OpenAlex W7214626666
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population)
Methods: Connectivity, Statistics, fMRI & imaging, Preprocessing
Keywords: Alzheimer's disease and related dementias, cortical atrophy, pollutant exposures, World Trade Center responders
MeSH: Brain*, Cerebral Cortex*, Cerebral Cortical Thinning*, Emergency Responders*, September 11 Terrorist Attacks*, Adult, Aged, Atrophy, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging (* major topic)
Topic: Air Quality and Health Impacts (Health, Toxicology and Mutagenesis, Environmental Science), according to OpenAlex
Funding: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics; CIHR; NIOSH (R01AG067590, P30 AG066514, R01AG049953); Foundation for the National Institutes of Health; Cure Alzheimer’s Fund (R01AG084699, R01AG093281, R01AG093879); ADNI National Institutes of Health (U01 AG024904); NIOSH CDC HHS (U01OH011314); DOD ADNI (W81XWH-12-2-0012); I.B. Ertegun Project for Cognitive Health Extension at ISMMS
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF hxye6

License: none: the authors keep all their rights
State: the link answers, verified on 31 October 2026
Evidence: files inventoried
Languages: Stata (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: the text, “WTC Neurotoxicant Exposure Severity Index (WTC‐N”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 2 checks, the latest on 31 October 2026: the link answers (HTTP 200)
  • 31 October 2026: the link answers (HTTP 200)
  • 1 October 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/hxye6/overview

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;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

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Versions

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Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 4 keywords, 14 MeSH terms, 9 funders, 37 references.

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This paper

Clouston, S. A. P., Huang, C., Askarov, T., Wang, Y., Chaudhry, Z., Carr, M. A., Yang, Y., West, S., Mann, F. D., Marquez, R., Vaska, P., Meliker, J., Gandy, S., Lucchini, R. G., Luft, B. J., & for the Alzheimer's Disease Neuroimaging Initiative. (2026). World Trade Center neurotoxicant exposure severity is associated with cortical thinning and regional brain atrophy: a neuroimaging study of general responders at midlife. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(10), e71878. https://doi.org/10.1002/alz.71878

BibTeX

@article{clouston2026world,
author = {Clouston, Sean A P and Huang, Chuan and Askarov, Temurbek and Wang, Yicun and Chaudhry, Zan and Carr, Melissa A and Yang, Yuan and West, Steven and Mann, Frank D and Marquez, Ruth and Vaska, Paul and Meliker, Jaymie and Gandy, Sam and Lucchini, Roberto G and Luft, Benjamin J and {for the Alzheimer's Disease Neuroimaging Initiative}},
title = {{World Trade Center neurotoxicant exposure severity is associated with cortical thinning and regional brain atrophy: a neuroimaging study of general responders at midlife}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = oct,
volume = {22},
number = {10},
pages = {e71878},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71878},
url = {https://doi.org/10.1002/alz.71878},
pmid = {42805945},
pmcid = {PMC13619733}
}

RIS

TY - JOUR
AU - Clouston, Sean A P
AU - Huang, Chuan
AU - Askarov, Temurbek
AU - Wang, Yicun
AU - Chaudhry, Zan
AU - Carr, Melissa A
AU - Yang, Yuan
AU - West, Steven
AU - Mann, Frank D
AU - Marquez, Ruth
AU - Vaska, Paul
AU - Meliker, Jaymie
AU - Gandy, Sam
AU - Lucchini, Roberto G
AU - Luft, Benjamin J
AU - for the Alzheimer's Disease Neuroimaging Initiative
TI - World Trade Center neurotoxicant exposure severity is associated with cortical thinning and regional brain atrophy: a neuroimaging study of general responders at midlife
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/10/01
VL - 22
IS - 10
SP - e71878
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71878
UR - https://doi.org/10.1002/alz.71878
LA - en
ER -

CSL-JSON

{
"id": "10.1002/alz.71878",
"type": "article-journal",
"title": "World Trade Center neurotoxicant exposure severity is associated with cortical thinning and regional brain atrophy: a neuroimaging study of general responders at midlife",
"container-title": "Alzheimer's & dementia : the journal of the Alzheimer's Association",
"author": [
{
"family": "Clouston",
"given": "Sean A P"
},
{
"family": "Huang",
"given": "Chuan"
},
{
"family": "Askarov",
"given": "Temurbek"
},
{
"family": "Wang",
"given": "Yicun"
},
{
"family": "Chaudhry",
"given": "Zan"
},
{
"family": "Carr",
"given": "Melissa A"
},
{
"family": "Yang",
"given": "Yuan"
},
{
"family": "West",
"given": "Steven"
},
{
"family": "Mann",
"given": "Frank D"
},
{
"family": "Marquez",
"given": "Ruth"
},
{
"family": "Vaska",
"given": "Paul"
},
{
"family": "Meliker",
"given": "Jaymie"
},
{
"family": "Gandy",
"given": "Sam"
},
{
"family": "Lucchini",
"given": "Roberto G"
},
{
"family": "Luft",
"given": "Benjamin J"
},
{
"literal": "for the Alzheimer's Disease Neuroimaging Initiative"
}
],
"container-title-short": "Alzheimers Dement",
"volume": "22",
"issue": "10",
"page": "e71878",
"DOI": "10.1002/alz.71878",
"PMID": "42805945",
"PMCID": "PMC13619733",
"ISSN": "1552-5260",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/alz.71878",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
1
]
]
}
}

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