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neuroimagen:bioface_sbm

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Analisis SBM con Freesurfer (a.k.a. FSGA) para BIOFACE

Haciendo los archivos

Para construir los archivos .fsgd a partir de la base de datos han de hacerse una serie de pasos. Voy a poner el ejemplo con la variable RLPP. Para cualquier otra variable simplemente se cambia.

[osotolongo@brick03 fsga2]$ sed 's/;/,/g' ../bioface_mri.csv > bioface_join.csv
[osotolongo@brick03 fsga2]$ awk -F"," '{print $3","$5","$6","$7","$21}' ../bioface_np.csv > bioface_rlpp.csv
[osotolongo@brick03 fsga2]$ sed -i 's/Subjecte/PSubject/g' bioface_rlpp.csv
[osotolongo@brick03 fsga2]$ join -t"," -1 2 -2 1 bioface_join.csv bioface_rlpp.csv > bioface_data_rlpp.csv
[osotolongo@brick03 fsga2]$ awk -F"," '{print "bioface_"$2","$3","$4","$5","$6}' bioface_data_rlpp.csv | sed 's/bioface_Subject/Variables/;s/Edad/Age/;s/Sexo/Gender/;s/Años_Estudios/Education/' | sed 's/bioface_\([^,]*\),/Input bioface_\1 Main /; s/,/ /g' > rlpp_body.csv
[osotolongo@brick03 fsga2]$ head rlpp_body.csv
Variables Age Gender Education RLPP
Input bioface_0001 Main 64 1 11 2
Input bioface_0002 Main 53 2 9 10
Input bioface_0003 Main 60 2 8 3
Input bioface_0004 Main 64 2 7 3
Input bioface_0005 Main 62 2 16 7
Input bioface_0006 Main 64 2 5 7
Input bioface_0007 Main 62 2 11 5
Input bioface_0008 Main 58 2 11 8
Input bioface_0009 Main 51 2 11 7
[osotolongo@brick03 fsga2]$ cat headers_rlpp.txt
GroupDescriptorFile 1
Title BIOFACE_rlpp
Class Main
[osotolongo@brick03 fsga2]$ cat headers_rlpp.txt rlpp_body.csv > rlpp.fsgd

y por otra parte el modelo,

[osotolongo@brick03 fsga2]$ cat rlpp.mtx
0 0 0 0 1

Preparando la ejecucion

Ahora voy a hacer un script que corra todo el proceso, para no saltarme ningun paso

[osotolongo@brick03 fsga2]$ cat fsga_rlpp.sh 
#!/bin/bash
mris_preproc --fsgd rlpp.fsgd --cache-in thickness.fwhm10.fsaverage --target fsaverage --hemi lh --out lh.rlpp.thickness.10.mgh
mris_preproc --fsgd rlpp.fsgd --cache-in thickness.fwhm10.fsaverage --target fsaverage --hemi rh --out rh.rlpp.thickness.10.mgh
mri_glmfit --y lh.rlpp.thickness.10.mgh --fsgd rlpp.fsgd --C rlpp.mtx --surf fsaverage lh --glmdir lh.rlpp.glmdir
mri_glmfit --y rh.rlpp.thickness.10.mgh --fsgd rlpp.fsgd --C rlpp.mtx --surf fsaverage rh --glmdir rh.rlpp.glmdir
mri_glmfit-sim --glmdir lh.rlpp.glmdir --cache 2 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir lh.rlpp.glmdir --cache 2 pos --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.rlpp.glmdir --cache 2 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.rlpp.glmdir --cache 2 pos --cwp 0.05 --2spaces

y se lanza todo con,

[osotolongo@brick03 fsga2]$ ./fsga_rlpp.sh

Buscando resultados

Para encontrar los clusters hacemos,

[osotolongo@brick03 fsga2]$ for x in `find ./ -name "*.summary"`; do echo ${x}; grep -v "^#" ${x}; done
./lh.rlpn.glmdir/rlpn/cache.th20.neg.sig.cluster.summary
./lh.rlpn.glmdir/rlpn/cache.th20.pos.sig.cluster.summary
./lh.rlpp.glmdir/rlpp/cache.th20.neg.sig.cluster.summary
./lh.rlpp.glmdir/rlpp/cache.th20.pos.sig.cluster.summary
   1        3.519  112413   3477.26    -18.5  -75.6   28.2  0.00020  0.00000  0.00040   6595    15036.07  precuneus
   2        3.947  157071   2138.53    -58.6  -51.1    1.7  0.00020  0.00000  0.00040   4377    10588.38  bankssts
   3        4.314   99360   1135.62    -44.3    7.6    5.7  0.00878  0.00719  0.01057   2749     7526.91  parsopercularis
./rh.rlpn.glmdir/rlpn/cache.th20.neg.sig.cluster.summary
./rh.rlpn.glmdir/rlpn/cache.th20.pos.sig.cluster.summary
./rh.rlpp.glmdir/rlpp/cache.th20.neg.sig.cluster.summary
./rh.rlpp.glmdir/rlpp/cache.th20.pos.sig.cluster.summary
   1        4.982  104537   7071.55     46.5  -57.5   23.1  0.00020  0.00000  0.00040  14015    35733.48  inferiorparietal
   2        3.430   29029   2647.45     27.8   19.3   42.7  0.00020  0.00000  0.00040   5370    11735.45  caudalmiddlefrontal
   3        4.229   45003   1848.86     12.5  -52.8   30.8  0.00020  0.00000  0.00040   4269    10182.11  precuneus
   4        2.481  143807   1116.95     30.5  -49.7  -13.8  0.01276  0.01077  0.01475   2172     4473.95  fusiform

Ahora, en el hemisferio/modelo que hemos encontrado resultados podemos identificar estos clusters con,

$ freeview -f $SUBJECTS_DIR/fsaverage/surf/rh.inflated:overlay=rh.rlpp.glmdir/rlpp/cache.th20.pos.sig.cluster.mgh:overlay_threshold=0.1,5:annot=rh.rlpp.glmdir/rlpp/cache.th20.pos.sig.ocn.annot -viewport 3d &

Para superponer el atlas DK,

$ freeview -f $SUBJECTS_DIR/fsaverage/surf/rh.inflated:overlay=rh.rlpp.glmdir/rlpp/cache.th20.pos.sig.cluster.mgh:overlay_threshold=0.1,5:annot=aparc.annot:annot_outline=1 -viewport 3d &

y para ver las areas de Brodman afectadas,

$ freeview -f $SUBJECTS_DIR/fsaverage/surf/rh.inflated:overlay=rh.rlpp.glmdir/rlpp/cache.th20.pos.sig.cluster.mgh:overlay_threshold=0.1,5:annot=PALS_B12_Brodmann.annot:annot_outline=1 -viewport 3d &

Cambiando a Volumen

Para area y/o otra variable es los mismo, con un solo ejemplo ya la idea la tenemos. Hay que hacer las 9 combinaciones pero no deberia tomar demasiado.

Extraer ICV

[osotolongo@brick03 fsga]$ awk -F"," '{print $1","$67}' ../fsrecon/aseg_stats.csv > bioface_icv.csv
[osotolongo@brick03 fsga]$ head bioface_icv.csv
Subject,EstimatedTotalIntraCranialVol
0001,1486505.98285
0002,1249118.11926
0003,1333969.51364
0004,1344500.90389
0005,1408317.34379
0006,1321218.04232
0007,1501903.59123
0008,1480617.98715
0009,1368664.47062
[osotolongo@brick03 fsga]$ cat convert_icv.r
setwd("/nas/data/bioface/fsga")
read.csv("bioface_icv.csv") -> x
x$zICV <- (x$EstimatedTotalIntraCranialVol - mean(x$EstimatedTotalIntraCranialVol))/sd(x$EstimatedTotalIntraCranialVol)
write.csv(x, file="bioface_zic_tempv.csv", row.names=FALSE)
[osotolongo@brick03 fsga]$ Rscript convert_icv.r 
[osotolongo@brick03 fsga]$ head bioface_zic_tempv.csv
"Subject","EstimatedTotalIntraCranialVol","zICV"
1,1486505.98285,0.41844790218558
2,1249118.11926,-1.20476544891787
3,1333969.51364,-0.624567648851558
4,1344500.90389,-0.552555992480593
5,1408317.34379,-0.116191236089841
6,1321218.04232,-0.711759800041669
7,1501903.59123,0.523733838540968
8,1480617.98715,0.378186900266852
9,1368664.47062,-0.387330089465778
[osotolongo@brick03 fsga]$ awk -F"," '{$1 = sprintf("%04d",$1); print $1","$3}' bioface_zic_tempv.csv | sed 's/"//g;s/0000/Subject/' > bioface_zicv.csv
[osotolongo@brick03 fsga]$ head bioface_zicv.csv
Subject,zICV
0001,0.41844790218558
0002,-1.20476544891787
0003,-0.624567648851558
0004,-0.552555992480593
0005,-0.116191236089841
0006,-0.711759800041669
0007,0.523733838540968
0008,0.378186900266852
0009,-0.387330089465778

Integrando con los datos

[osotolongo@brick03 fsga]$ join -t"," -1 2 -2 1 bioface_data.csv bioface_zicv.csv > bioface_vdata.csv
[osotolongo@brick03 fsga]$ head bioface_vdata.csv
Subject,PSubject,Edad,Sexo,Estudios,FACEmem,zICV
0001,B001,64,1,11,13,0.41844790218558
0002,B002,53,2,9,48,-1.20476544891787
0003,B003,60,2,8,10,-0.624567648851558
0004,B004,64,2,7,21,-0.552555992480593
0005,B005,62,2,16,30,-0.116191236089841
0006,B006,64,2,5,25,-0.711759800041669
0007,B007,62,2,11,39,0.523733838540968
0008,B008,58,2,11,45,0.378186900266852
0009,B009,51,2,11,28,-0.387330089465778
[osotolongo@brick03 fsga]$ awk -F"," '{print "bioface_"$1","$3","$4","$5","$6","$7}' bioface_vdata.csv | sed 's/bioface_Subject/Variables/;s/Edad/Age/;s/Sexo/Gender/;s/Estudios/Education/' | sed 's/bioface_\([^,]*\),/Input bioface_\1 Main /; s/,/ /g' > memv_body.csv
[osotolongo@brick03 fsga]$ head memv_body.csv
Variables Age Gender Education FACEmem zICV
Input bioface_0001 Main 64 1 11 13 0.41844790218558
Input bioface_0002 Main 53 2 9 48 -1.20476544891787
Input bioface_0003 Main 60 2 8 10 -0.624567648851558
Input bioface_0004 Main 64 2 7 21 -0.552555992480593
Input bioface_0005 Main 62 2 16 30 -0.116191236089841
Input bioface_0006 Main 64 2 5 25 -0.711759800041669
Input bioface_0007 Main 62 2 11 39 0.523733838540968
Input bioface_0008 Main 58 2 11 45 0.378186900266852
Input bioface_0009 Main 51 2 11 28 -0.387330089465778
[osotolongo@brick03 fsga]$ cat headers.txt
GroupDescriptorFile 1
Title BIOFACE_mem
Class Main
[osotolongo@brick03 fsga]$ cat headers.txt memv_body.csv > memv.fsgd
[osotolongo@brick03 fsga]$ head memv.fsgd
GroupDescriptorFile 1
Title BIOFACE_mem
Class Main
Variables Age Gender Education FACEmem zICV
Input bioface_0001 Main 64 1 11 13 0.41844790218558
Input bioface_0002 Main 53 2 9 48 -1.20476544891787
Input bioface_0003 Main 60 2 8 10 -0.624567648851558
Input bioface_0004 Main 64 2 7 21 -0.552555992480593
Input bioface_0005 Main 62 2 16 30 -0.116191236089841
Input bioface_0006 Main 64 2 5 25 -0.711759800041669

Preparando el run

[osotolongo@brick03 fsga]$ cat memv.mtx
0 0 0 0 1 0
fsgav.sh
#!/bin/bash
mris_preproc --fsgd memv.fsgd --cache-in volume.fwhm10.fsaverage --target fsaverage --hemi lh --out lh.bioface.volume.10.mgh
mris_preproc --fsgd memv.fsgd --cache-in volume.fwhm10.fsaverage --target fsaverage --hemi rh --out rh.bioface.volume.10.mgh
mri_glmfit --y lh.bioface.volume.10.mgh --fsgd memv.fsgd --C memv.mtx --surf fsaverage lh --glmdir lh.bioface.vglmdir
mri_glmfit --y rh.bioface.volume.10.mgh --fsgd memv.fsgd --C memv.mtx --surf fsaverage rh --glmdir rh.bioface.vglmdir
mri_glmfit-sim --glmdir lh.bioface.vglmdir --cache 2 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir lh.bioface.vglmdir --cache 2 pos --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.bioface.vglmdir --cache 2 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.bioface.vglmdir --cache 2 pos --cwp 0.05 --2spaces

Sacar resultados

Ahora,

$ ./fsgav.sh

y buscamos los resultados,

[osotolongo@brick03 fsga]$ for x in `find *vglmdir/memv/ -name "*.th20.*.summary"`; do echo ${x}; grep -v "^#" ${x}; done
lh.bioface.vglmdir/memv/cache.th20.neg.sig.cluster.summary
lh.bioface.vglmdir/memv/cache.th20.pos.sig.cluster.summary
   1        3.959   79787    828.17    -32.6  -76.7  -11.8  0.00180  0.00100  0.00260   1178     3049.31  fusiform
   2        2.513   56552    625.22    -47.9  -26.0  -10.1  0.01574  0.01355  0.01792   1292     2610.65  middletemporal
rh.bioface.vglmdir/memv/cache.th20.neg.sig.cluster.summary
rh.bioface.vglmdir/memv/cache.th20.pos.sig.cluster.summary
   1        5.991   47819    954.75     45.3  -50.6   24.1  0.00060  0.00020  0.00100   2214     6368.82  inferiorparietal
   2        3.719    5713    674.59     54.4    3.4  -15.3  0.01157  0.00958  0.01355   1372     3261.33  superiortemporal

;-)

SBM vs NBACE

[osotolongo@brick03 fsga_nbace]$ head -n 1 bioface_full_db.csv | sed 's/,/\n/g' | cat -n
     1	SUJETO
     2	Interno
     3	INV_Edinburgh
     4	ISIS
     5	Suenyo_Pitsb
     6	P_Orofonatorias
     7	R_Pseudopalabras
     8	R_Palabras
     9	R_Frases
    10	C_Palabras
    11	C_Ordenes
    12	Proces_Sintactico
    13	L_Numeros
    14	L_Logotomos
    15	L_Textos
    16	Escritura
    17	TAP
    18	Secuencias_Posturas_D
    19	Secuencias_Posturas_I
    20	Grafestesia_D
    21	Grafestesia_I
    22	Recon_Digital_D
    23	Recon_Digital_I
    24	Orientacion_D_I
    25	Probl_aritmeticos
    26	Num_LCR
    27	PLASMA_Exosomes
    28	PrimarioÚltimo
    29	Primera_Prueba
    30	Prueba
    31	EIN1
    32	EIP1
    33	EIN2
    34	EIP2
    35	RCPN
    36	RCPP
    37	RF
    38	RLPN
    39	RLPP
    40	NEM
    41	PEM
    42	Resultado
    43	RLPTOTAL
    44	TABLETAPTOTAL
    45	Fecha_NPS_adicional
    46	FCSRT_Total_Free_Recall
    47	FCSRT_Total_Recall
    48	FCSRT_Delayed_Free_Recall
    49	FCSRT_Delayed_Total_Recall
    50	ROCFT_Delayed
    51	DMS_48_SET1
    52	DMS_48_SET2
    53	TMT_A_Time
    54	TMT_A_errors
    55	TMT_B_Time
    56	TMT_B_errors
    57	Fluency_M
    58	Fluency_R
    59	Fluency_P
    60	Stroop_P
    61	Stroop_C
    62	Stroop_PC
    63	Stroop_Formula
    64	Stroop_Interferencia
    65	ROCFT_Copy
    66	ROCFT_Time
    67	VOSP_Incomp_Letters
    68	VOSP_Numb_Locat
    69	JLO
    70	Boston_free
    71	Boston_free_CS
    72	Boston_free_CS_CF
    73	Piram_Palm_Imagenes
    74	Piram_Palm_palabras
    75	ATN_grupo
    76	PrimaryLast
    77	Fecha_Nacimiento
    78	Sexo
    79	Diagnostico_Sindromico
    80	Diagnost_secund_agrupado
    81	Diagnostico_secundario
    82	Diagnostico_NPS
    83	Anyos_escolaridad
    84	Nivel_escolaridad
    85	Situacion_laboral
    86	Convivencia
    87	Estado_civil
    88	Lengua_materna
    89	Bilingue
    90	Lateralidad
    91	Fecha_NRL
    92	Fecha_Inicio_sintomas
    93	Edad_inicio
    94	Edad_visita
    95	Sintoma_Inicio1
    96	Sintoma_Inicio2
    97	Sintoma_Inicio3
    98	Sintoma_Inicio4
    99	Sintoma_Inicio5
   100	Sintoma_Inicio6
   101	Sintoma_Inicio7
   102	Progresion
   103	Fluctuacion
   104	HTA
   105	DL
   106	DM
   107	EAP
   108	Ictus_isq
   109	Ictus_hem
   110	AIT
   111	Cardiopatia_isq
   112	Tabaquismo
   113	SAHS
   114	EPOC
   115	ASMA
   116	TAnsiedad_generalizada
   117	TDepresivo_persistente
   118	EnfPsiOTRAS
   119	Sind_AnsioDepre
   120	Fibromialgia
   121	Fatiga_cronica
   122	SQM
   123	Enf_autoinmunes
   124	RBD
   125	APNEASSUEnyO
   126	Roncopatia
   127	Bruxismo
   128	MovVigorSuenyos
   129	Sominiloquios
   130	ParaSuenyViole
   131	Insomnio
   132	SomnolecenciaDiurna
   133	Parasomnias
   134	SPI
   135	Migranya_aura
   136	Migranya_sin_aura
   137	Migranya_cronica
   138	Migranya_Episodica
   139	IRC
   140	hipotiroidismo
   141	Hipo_B12
   142	Consumo_riesgo_OH
   143	Consumo_otras_sustancias
   144	m1.Antiagregantes
   145	m2.Antihipertensivos
   146	m3.Antidiabeticos1
   147	m4.Hipolipemientes2
   148	m5.Antidepresivos
   149	m6.Ansioliticos
   150	m7.Antiepilecticos
   151	m8.Antiparkinsonianos
   152	m9.Neurolecticos
   153	m10.Antiinflamatorios
   154	m11.Analgesicos
   155	m12.Opiaceos
   156	m13.Souvenaid
   157	m14.GinkgoBiloba
   158	m.15HormonasTiroideas
   159	m16.Hinopticos
   160	m17.B12
   161	M18.Antihistaminicos
   162	Antecedentes_familiares_1G
   163	Antecedentes_familiares_presenil
   164	IPAQ
   165	UPDRSS_III
   166	MMSE
   167	CDR
   168	Talla
   169	Peso
   170	IMC
   171	Perimetro_abdominal
   172	TA_S
   173	TA_D
   174	Obesidad_abdominal
   175	Obesidad
   176	Atrofia_global
   177	Atrofia_parietal_D
   178	Atrofia_parietal_I
   179	Atrofia_temporal_D
   180	Atrofia_temporal_I
   181	Fazekas_profundo_D
   182	Fazekas_profundo_I
   183	Fazekas_periventricular
   184	ARWMC_Frontal_D
   185	ARWMC_Frontal_I
   186	ARWMC_Parieto_occipital_D
   187	ARWMC_Parieto_occipital_I
   188	ARWMC_Temporal_D
   189	ARWMC_Temporal_I
   190	ARWMC_Basal_ganglia
   191	ARWMC_infratentorial
   192	ARWMC_total
   193	EspaciosPV_centrosemioval
   194	EspaciosPV_gangliosbasales
   195	Infartos_lacunares
   196	Microhemorragias_menor5_profundas
   197	Microhemorragias_menor5_corticales
   198	Microhemorragias_5_10_profundas
   199	Microhemorragias_5_10_corticales
   200	Siderosis_superficial
   201	APOE
   202	E4_positivo
   203	PL
   204	Fecha_PL
   205	ABETA42_LUMI
   206	AB42resAB40
   207	LumipTau
   208	LumihTau
   209	Lumi_Abeta_ratio_dicot
   210	Lumi_T_tau_dicot
   211	Lumi_P_tau_dicot
   212	Lumi_ATNr
   213	FechaNeuropsicologia
   214	O_Total_NP
   215	O_T_NP
   216	O_E_NP
   217	O_P_NP
   218	M_numparaules_NP
   219	M_WMS_total_NP
   220	M_ret_NP
   221	M_recon_NP
   222	M_f_rec_NP
   223	M_digtspan_direct_NP
   224	M_digtspan_invers_NP
   225	M_digttotal_direct_NP
   226	M_digttotal_invers_NP
   227	LL_Namingtotal_NP
   228	LL_comprensio_NP
   229	LL_R_total_NP
   230	LL_escriptura_NP
   231	G_pop_total_NP
   232	G_15obj_NP
   233	G_15obj_nf_NP
   234	G_Luria_NP
   235	P_Ecopraxiatotal_NP
   236	P_ideo_total_NP
   237	P_constr_total_NP
   238	FE_SKTtemps_NP
   239	FE_SKTerrors_NP
   240	FE_Pflu_NP
   241	FE_anflu_NP
   242	Fluidesa_Verbal_Accio_NP
   243	FE_R_abstracte_NP
   244	FE_T_rellotge_NP
   245	HAD_ansiedad_NP
   246	HAD_depresion_NP
[osotolongo@brick03 fsga_nbace]$ sed 's/;/,/g' ../bioface_mri.csv | sort -t, -k 2 > bioface_join.csv
[osotolongo@brick03 fsga_nbace]$ sed -i '1iSubject,PSubject' bioface_join.csv
[osotolongo@brick03 fsga_nbace]$ head bioface_join.csv
Subject,PSubject
0001,B001
0002,B002
0003,B003
0004,B004
0005,B005
0006,B006
0007,B007
0008,B008
0009,B009
[osotolongo@brick03 fsga_nbace]$ awk -F"," '{print $1","$94","$78","$83","$220}' bioface_full_db.csv > fsga_220/bioface_220.csv
[osotolongo@brick03 fsga_nbace]$ sed -i 's/SUJETO/PSubject/g' fsga_220/bioface_220.csv
[osotolongo@brick03 fsga_nbace]$ join -t"," -1 2 -2 1 bioface_join.csv fsga_220/bioface_220.csv > fsga_220/bioface_data_220.csv
[osotolongo@brick03 fsga_220]$ (head -n 1 bioface_data_220.csv; tail -n +2 bioface_data_220.csv | sort -t, -k 2) > bioface_data_sorted_220.csv
[osotolongo@brick03 fsga_220]$ awk -F"," '{print "bioface_"$2","$3","$4","$5","$6}' bioface_data_220.csv | sed 's/bioface_Subject/Variables/;s/Edad_visita/Age/;s/Sexo/Gender/;s/Anyos_escolaridad/Education/' | sed 's/bioface_\([^,]*\),/Input bioface_\1 Main /; s/,/ /g' > 220_body.csv
[osotolongo@brick03 fsga_220]$ head 220_body.csv
Variables Age Gender Education M_ret_NP
Input bioface_0001 Main 64 0 13 6
Input bioface_0002 Main 53 1 10 5
Input bioface_0003 Main 60 1 8 5
Input bioface_0004 Main 64 1 7 5
Input bioface_0005 Main 62 1 16 4
Input bioface_0006 Main 64 1 6 7
Input bioface_0007 Main 62 1 9 5
Input bioface_0008 Main 58 1 15 5
Input bioface_0009 Main 51 1 9 6
[osotolongo@brick03 fsga_220]$ cat headers_220.txt
GroupDescriptorFile 1
Title BIOFACE_220
Class Main
[osotolongo@brick03 fsga_220]$ cat headers_220.txt 220_body.csv > 220.fsgd
fsga_220.sh
#!/bin/bash
mris_preproc --fsgd 220.fsgd --cache-in thickness.fwhm10.fsaverage --target fsaverage --hemi lh --out lh.220.thickness.10.mgh
mris_preproc --fsgd 220.fsgd --cache-in thickness.fwhm10.fsaverage --target fsaverage --hemi rh --out rh.220.thickness.10.mgh
mri_glmfit --y lh.220.thickness.10.mgh --fsgd 220.fsgd --C 220.mtx --surf fsaverage lh --glmdir lh.220.glmdir
mri_glmfit --y rh.220.thickness.10.mgh --fsgd 220.fsgd --C 220.mtx --surf fsaverage rh --glmdir rh.220.glmdir
mri_glmfit-sim --glmdir lh.220.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir lh.220.glmdir --cache 3 pos --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220.glmdir --cache 3 pos --cwp 0.05 --2spaces
awk -F"," '{print $1","$65}' ../../fsrecon/aseg_stats.csv > bioface_icv.csv
Rscript convert_icv.r
awk -F"," '{$1 = sprintf("%04d",$1); print $1","$3}' bioface_zic_tempv.csv | sed 's/"//g;s/0000/Subject/' > bioface_zicv.csv
join -t"," -1 2 -2 1 bioface_data_sorted_220.csv bioface_zicv.csv > bioface_vdata.csv
awk -F"," '{print "bioface_"$1","$3","$4","$5","$6","$7}' bioface_vdata.csv | sed 's/bioface_Subject/Variables/;s/Edad_visita/Age/;s/Sexo/Gender/;s/Anyos_escolaridad/Education/' | sed 's/bioface_\([^,]*\),/Input bioface_\1 Main /; s/,/ /g' > 220v_body.csv
cat headers_220.txt 220v_body.csv > 220v.fsgd
[osotolongo@brick03 fsga_220]$ cat 220a.mtx
0 0 0 0 1 0
 
[osotolongo@brick03 fsga_220]$ cat fsga_220a.sh
#!/bin/bash
mris_preproc --fsgd 220v.fsgd --cache-in area.fwhm10.fsaverage --target fsaverage --hemi lh --out lh.220.area.10.mgh
mris_preproc --fsgd 220v.fsgd --cache-in area.fwhm10.fsaverage --target fsaverage --hemi rh --out rh.220.area.10.mgh
mri_glmfit --y lh.220.area.10.mgh --fsgd 220v.fsgd --C 220a.mtx --surf fsaverage lh --glmdir lh.220a.glmdir
mri_glmfit --y rh.220.area.10.mgh --fsgd 220v.fsgd --C 220a.mtx --surf fsaverage rh --glmdir rh.220a.glmdir
mri_glmfit-sim --glmdir lh.220a.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir lh.220a.glmdir --cache 3 pos --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220a.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220a.glmdir --cache 3 pos --cwp 0.05 --2spaces
 
[osotolongo@brick03 fsga_220]$ cat fsga_220v.sh
#!/bin/bash
mris_preproc --fsgd 220v.fsgd --cache-in volume.fwhm10.fsaverage --target fsaverage --hemi lh --out lh.220.volume.10.mgh
mris_preproc --fsgd 220v.fsgd --cache-in volume.fwhm10.fsaverage --target fsaverage --hemi rh --out rh.220.volume.10.mgh
mri_glmfit --y lh.220.volume.10.mgh --fsgd 220v.fsgd --C 220a.mtx --surf fsaverage lh --glmdir lh.220v.glmdir
mri_glmfit --y rh.220.volume.10.mgh --fsgd 220v.fsgd --C 220a.mtx --surf fsaverage rh --glmdir rh.220v.glmdir
mri_glmfit-sim --glmdir lh.220v.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir lh.220v.glmdir --cache 3 pos --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220v.glmdir --cache 3 neg --cwp 0.05 --2spaces
mri_glmfit-sim --glmdir rh.220v.glmdir --cache 3 pos --cwp 0.05 --2spaces
neuroimagen/bioface_sbm.1639232004.txt.gz · Last modified: 2021/12/11 14:13 by osotolongo