Human halamoco ical connec ions and
hei in ol emen in language sys ems
Doc o al Thesis by:
Mengxing Liu
Supe ised by:
D . Ped o M. Paz-Alonso
D . Ga ikoi z Le ma-Usabiaga
2022
(cc)2022 MENGXING LIU (cc by-nc-sa 4.0)
Mengxing Liu
All igh s ese ed.
BCBL Basque Cen e on Cogni ion, B ain and Language
Paseo Mikele egi, 69,
Donos ia-San Sebas ian, Spain
1
This wo k was suppo ed by g an s om he Eu opean Union’s Ho izon 2020 esea ch and
inno a ion p og amme unde he Ma ie Sklodowska-Cu ie (g an ag eemen No. 713673),
and om “la Caixa” Founda ion (No. 11660016).
2
Human halamoco ical connec ions and
hei in ol emen in language sys ems
Doc o al Thesis by:
Mengxing Liu
Supe ised by:
D . Ped o M. Paz-Alonso
D . Ga ikoi z Le ma-Usabiaga
2022
3
Acknowledgemen
Thanks o Kepa, o ec ui ing me a i s , and con inuously eaching and aining me.
In he pas i e yea s, you keep eplying ha i is you job e e y ime I say hank you o you.
Bu I know you ha e done way mo e han you job asks you o do. I one day I managed o
become a quali ied esea che and s a men o ing s uden s, I hope I could be a men o like
you.
Thanks o Ga i, o hos ing me in a o eign coun y, and o all he aining om you. I
lea ned a lo om you.
Thanks o la Caixa Founda ion, o he gene ous ellowship in exchange o only
men ioning hei names in my publica ions.
Thanks o he admin and lab g oups in he BCBL, o all he help hey ha e p o ided
o make my li e and esea ch he e easie .
4
Con en
Acknowledgemen 4
Con en 5
Lis o abb e ia ions 6
1 Resumen en cas ellano 9
2 Abs ac 14
3 Backg ound and mo i a ion 18
4 Thalamus 22
4.1 Gene al in oduc ion o he s uc u e 22
4.2 Thalamic a lases and nuclea di isions 23
4.3 Thalamic nuclei segmen a ion me hods 26
4.4 Fi s -o de elay halamic nuclei 29
4.4.1 La e al genicula e nucleus 30
4.4.2 Medial genicula e nucleus 31
4.4.3 Ven al la e al nucleus 33
4.5 Highe -o de elay halamic nuclei 35
4.5.1 An e io nuclea complex 35
4.5.2 Mediodo sal nucleus 37
4.5.3 Pul ina 38
5 Neu obiology o language 41
5.1 His o y 41
5.2 Mode n neu oana omical models 45
5.2.1 Modali y-gene al models 45
5.2.1.1 P ice’s model 45
5.2.1.2 MUC model 47
5.2.1.3 Lau’s model 48
5.2.2 Reading models 49
5.2.3 Speech comp ehension models 50
5.2.4 P oduc ion models 52
6 S udy 1: S uc u al connec ion o i s -o de halamic nuclei 55
6.1 Me hods 56
6.1.1 Subjec s 56
6.1.2 Da a acquisi ion 57
6.1.3 T ac og aphy pipeline 58
6.1.3.1 ROI de ini ion 58
6.1.3.2 DWI da a p ep ocessing 60
6.1.3.3 T ac iden i ica ion and ac ome y 60
5
6.1.4 Rep oducibili y measu emen 62
6.2 Resul s 64
6.2.1 Compu a ional ep oducibili y 66
6.2.2 Tes - e es ep oducibili y 68
6.3 Discussion 69
7 S udy 2: S uc u al connec i i y o highe -o de halamic nuclei: An e io halamic
complex and mediodo sal nucleus 73
7.1 Me hods 75
7.1.1 Subjec s and da a acquisi ion 75
7.1.2 T ac og aphy pipeline 75
7.1.2.1 ROI de ini ion 76
7.1.2.2 DWI da a p ep ocessing 77
7.1.2.3 T ac iden i ica ion and ac ome y 77
7.1.3 Rep oducibili y measu emen 78
7.1.4 Pos -hoc analysis 78
7.2 Resul s 79
7.2.1 Compu a ional ep oducibili y 84
7.2.2 Tes - e es ep oducibili y 85
7.2.3 Pos -hoc analysis esul s 86
7.3 Discussion 87
8 S udy 3: ask-based MRI s udy o halamic in ol emen in human language sys ems
93
8.1 Me hods 93
8.1.1 Pa icipan s 93
8.1.2 Ma e ials and Expe imen al P ocedu e 94
8.1.3 MRI da a acquisi ion 96
8.1.4 MRI da a analysis 96
8.2 Resul s 99
8.2.1 Whole-b ain con as s 99
8.2.2 ROI esul s 100
8.2.3 Func ional connec i i y esul s 102
8.2.4 S uc u al connec i i y 105
8.3 Discussion 105
9 Gene al Discussion 111
10 Bibliog aphy 116
6
Lis o abb e ia ions
A1
p ima y audi o y co ex
ACC
an e io cingula e co ex
AD
axial di usi i y
AN
an e io nuclea complex
ANOVA
analysis o a iance
AR
acous ic adia ion
BCBL
Basque Cen e on Cogni ion, B ain and Language
BOLD
blood-oxygen-le el-dependen
CSD
cons ained sphe ical decon olu ion
dlPFC
do sola e al p e on al co ex
DT
den a o halamic ac
DTI
di usion enso images/imaging
DWI
di usion-weigh ed images/imaging
MRI
unc ional magne ic esonance imaging
FODs
ibe o ien a ion dis ibu ions
FoV
ield o iew
FWE
amily wise e o
FWHM
ull-wid h hal -maximum
GLM
gene al linea model
HCP
human connec ome p ojec
HRF
hemodynamic esponse unc ion
IFG
in e io on al gy us
LGN
la e al genicula e nucleus
M1
p ima y mo o co ex
MD
mediodo sal nucleus
MGN
medial genicula e nucleus
mPFC
medial p e on al co ex
MR
mo o adia ion
MRI
magne ic esonance imaging
MUC
Memo y-Uni ica ion-Con ol
OR
op ic adia ion
PFC
p e on al co ex
pSTG
pos e io supe io empo al gy us
ROI
egion-o -in e es
7
RTP2
ep oducible- ac -p o iles
SMA
supplemen a y mo o a ea
SMG
sup ama ginal gy us
T1
T1-weigh ed s uc u al image
TE
ime- o-echo
TR
ime- o- epe i ion
V1
p ima y isual co ex
V2
seconda y isual co ex
VLa
an e io en al la e al nucleus
VLN
en al la e al nucleus
VLp
pos e io en al la e al nucleus
8
whi e-ma e ac s and ac ome y. We hen used compu a ion and es - e es me hods o check
whe he ou p o ocol could eliably econs uc hese ac s o in e es and hei p o iles. Ou esul s
demons a ed ha he p o ocol had nea ly pe ec compu a ional ep oducibili y and good- o-excellen
es - e es ep oducibili y. This new p o ocol may be o in e es o bo h neu oimaging and clinical
esea ch, and i has been made publicly a ailable o he scien i ic communi y.
In con as o he i s -o de elay nuclei, he “second-o de ” o “highe -o de ” elay nuclei o
he halamus a e ecip ocally in e connec ed wi h he ce eb al co ex ia co ico- halamo-co ical
pa hways. Fo example, he mediodo sal halamus is in e connec ed wi h p ac ically he en i e
p e on al co ex (PFC). In addi ion, i also ecei es a e en s om he an e io empo al lobe and
amygdala. The an e io nuclei ha e in e connec ions wi h he cingula e co ex and he e osplenial
co ex. Due o he complex s uc u al connec ions, he e a e li le ac og aphy s udies being able o
econs uc hose highe -o de halamic whi e-ma e ac s. In he second s udy, we ocused on he
whi e-ma e ac s o he an e io and mediodo sal nuclei o he halamus and de eloped a
econs uc ion p o ocol o ob ain he ac s o igina ing om o e mina ing a hese nuclei. We also
es ed he eliabili y o he econs uc ion p o ocol on a ela i ely la ge da ase . This p o ocol has
p o ed o be able o econs uc hose whi e-ma e ac s eliably, wi h only ac s wi h speci ic
imaging o ana omical cha ac e is ics showing ela i ely lowe ep oducibili y. A pos -hoc analysis
was conduc ed o explo e he associa ion o speci ic imaging and ana omical cha ac e is ics wi h
ep oducibili y. The esul s e ealed a s ong nega i e co ela ion be ween bo h di usion imaging da a
noise and ac leng h wi h ep oducibili y, and a posi i e co ela ion be ween s eamline coun and
ep oducibili y. This p o ocol is publicly a ailable o bo h esea ch and clinical use. The
ep oducibili y esul s also opened new a enues o u u e s udies; o example, o sys ema ically
examining he possible ac o s ha could ha e a s onge impac on ac ep oducibili y.
The i s -o de halamic nuclei a e adi ionally belie ed o elay senso imo o in o ma ion
om he pe iphe y and ce ebellum o co ical egions. Recen wo k has shown ha he engagemen o
he i s -o de halamic nuclei can be modula ed by he co ical egions (Andolina e al., 2007;
Cudei o & Silli o, 2006; on K iegs ein e al., 2008). Neu obiology o language esea ch has been
15
ocused on he co ical egions (aside om a ew excep ions) and has igno ed he con ibu ions o
subco ical s uc u es such as he i s -o de halamic nuclei. In he hi d s udy, we in es iga ed he
in ol emen o he i s -o de halamic nuclei in language p ocesses in a ask-based unc ional MRI
expe imen . In his expe imen , he pa icipan s pe o med language asks ha ely on di e en
senso imo o sys ems: eading ( isual pa hway), speech comp ehension (audi o y pa hway) and
speech p oduc ion (mo o pa hway). These h ee linguis ic asks ely on di e en senso imo o
sys ems ec ui ing i s -o de halamic nuclei du ing pe cep ual and mo o in o ma ion p ocessing. In
addi ion, we also included h ee non-linguis ic asks ha we e pa allel o he linguis asks: seeing
sc ambled images ( isual pa hway); lis ening o noise audios (audi o y pa hway) and p oducing
unin ellec ual sounds (mo o pa hway). We ound modali y-speci ic engagemen o he i s -o de
halamic nuclei in bo h linguis ic and non-linguis ic asks. Mo e impo an ly, he esul s e ealed a
modula ion in he engagemen o bo h he isual and audi o y halamic nuclei as a unc ion o he
linguis ic e sus non-linguis ic na u e o he s imuli. Fo example, he le la e al genicula e nucleus
(LGN) showed s onge ac i a ion o eading eal wo ds han o seeing sc ambled images. This
modula ion was no obse ed in he igh halamus. We also ound s ong unc ional coupling be ween
he i s -o de halamic nuclei and hei p ima y co ical egions o asks associa ed wi h hei
co esponding modali ies. These esul s sugges a seg ega ion in he implica ion o di e en human
halamic senso imo o nuclei in he main human language sys ems, and ha hese nuclei exhibi a
unc ional p e e ence o linguis ic e sus non-linguis ic s imuli. This wo k aised he possibili y ha
he i s -o de halamic nuclei eac adap i ely o he high le el cogni i e in o ma ion o he senso y
inpu . So a e y li le is known abou he ole o subco ical s uc u es in high-le el cogni i e
unc ions. The cu en s udy began o ex end he adi ional iew o human language mechanisms
om ce eb al co ex o a b oade scope and s a ed o acknowledge he possible con ibu ions o he
halamus in language p ocessing.
Al oge he , he cu en hesis in es iga ed he s uc u al connec ions be ween he halamus
and he ce eb al co ex, and p oposed wo econs uc ion p o ocols ha a e a ailable o he scien i ic
communi y o ob ain eliable i s -o de and highe -o de halamic whi e-ma e ac s. We also
16
examined some possible ac o s ha a e linked wi h ac og aphy econs uc ion ep oducibili y and
p o ided e idence ha some ac s can be less ep oducible when ha ing speci ic imaging o
ana omical cha ac e is ics. Finally, we showed he in ol emen o he i s -o de halamic nuclei in
language p ocessing in a ask-based unc ional MRI expe imen , and a gued he necessi y o
conside ing he ole o subco ical s uc u es, like he halamus, when in es iga ing he neu al basis o
human language unc ion.
17
3 Backg ound and mo i a ion
Cu en iews o co ical unc ion and expe imen al app oaches o unde s anding he
mechanisms o high-le el cogni i e unc ions, such as human language, a e hea ily domina ed by
wha can be desc ibed as a co icocen ic iew. In his iew, he ce eb al co ex is belie ed o ha e he
mos impo an ole in high-le el cogni ion and beha io , whe eas he subco ical s uc u es a e seen
o ha e a subse ien ole, o no ole in hese unc ions (Pa izi, 2009). This no ion is p e alen
enough o aise a conce n. In a su ey in 2008 abou co icocen ic ends in he neu oscience ield, i
was ound ha 72% o s udies in cu en neu oscience esea ch in which subco ical s uc u es could
ha e been he ocus o he s udy, ended up being igno ed. Also, 50% o s udies in which subco ical
indings we e epo ed chose no o discuss he indings abou subco ical s uc u es (Pa izi, 2009).
Lacking knowledge abou he na u e o he connec i i y be ween co ical and subco ical s uc u es
migh be he main cause o he co icocen ic iew.
As an example, he halamus has long been e e ed o as a passi e elay, a necessa y link in
he low o in o ma ion om he pe iphe y o he ce eb al co ex. The main esponsibili y o he
halamus is o ansmi pe cep ual in o ma ion ( isual, audi o y, soma osenso y) and in o ma ion in
o he o ms (mo o ins uc ions om he ce ebellum). Bu his only accoun s o a small pa o he
halamus (She man, 2007). The la ges nucleus o he halamus, he pul ina , has ex ensi e
connec ions wi h he isual co ex and also wi h he ex as ia e co ex. I was p o ed o ecei e
a e en in o ma ion om he laye s V and VI o hese isual a eas opog aphically and p ojec back
o he supe icial laye s o hese a eas (Guille y & She man, 2002). These co ico- halamo-co ical
pa hways a e no only used o elay pe iphe al in o ma ion o he ce eb al co ex, bu a he play a key
componen in co ico-co ical communica ions. The pul ina is no he only s uc u e in he halamus
ha ecei es a e en in o ma ion om he ce eb al co ex. The LGN o he halamus is he elay o
isual in o ma ion om he e ina o he isual co ex, bu also ecei es modula o y axons om laye
VI o he p ima y isual co ex (She man & Guille y, 2009). The exac unc ion o he modula o y
axons om laye VI o he p ima y isual co ex o he LGN is s ill no clea , bu i is a use ul
example o why he halamus should no be ea ed as a me e elay.
18
The in e connec ions be ween he halamus and he ce eb al co ex a e key o unde s anding
halamic unc ion. An ea ly me hod o ob ain halamoco ical connec ions was o s udy he e og ade
degene a ion o halamic cells when co ical s uc u es we e damaged by local lesions ( o a b ie
in oduc ion, see She man & Guille y, 2009). Fo example, i a e og ade degene a ion o he LGN o
he halamus was obse ed along wi h a limi ed lesion in he isual co ex, ea lie in es iga o s could
specula e ha his s uc u e is in e connec ed wi h he isual co ex. This me hod has now been
supe seded by mo e mode n me hods ha ely on axonally anspo ed ace s. Res ic ed co ical
injec ions o anspo ed ace s in speci ic laye s could show which halamic cells send axons o a
gi en a ea (e.g., Pa en e al., 1999). Ne e heless, i has limi ed applica ion on human beings as i is
an in asi e me hod.
Recen ad ances in non-in asi e s uc u al imaging ha e opened new app oaches o
in es iga ing in i o whi e ma e s uc u es in human beings. Among hem, DWI allows o indi ec
es ima ion o he axon g oup o ien a ions by measu ing he mo ion o wa e p o ons (Bamme , 2003;
Mukhe jee e al., 2008). This p ocedu e o econs uc ing whi e ma e ac s om DWI da a is
con en ionally called ac og aphy. T ac og aphy has p o en success ul in quan i a i ely measu ing
he s uc u al connec i i y be ween di e en b ain s uc u es (Aydogan e al., 2018). I has been used
o in es iga e halamoco ical whi e ma e ibe s (Johansen-Be g e al., 2005; Klein e al., 2010;
T ayno e al., 2010). Mo e de ails in his ega d will be e iewed in chap e 4. In he i s wo s udies
o his hesis, we will use ac og aphy o econs uc speci ic halamoco ical whi e-ma e ac s, and
p opose p ecise and ep oducible p o ocols ha can be used by he scien i ic communi y o u u e
s udies conce ning halamoco ical pa hways. The i s s udy will ocus on he i s -o de halamic
ac s ha se e o elay pe iphe al and ce ebella inpu o he ce eb al co ex. The second s udy will
ocus on he high-o de halamic ac s ha connec he an e io and mediodo sal halamus wi h
co ical s uc u es. In he wo p o ocols, we will adop a p obabilis ic a las o segmen he human
halamus o ob ain p ecise and eliable indi idual halamic nuclei, which allows acking whi e-ma e
ibe s om DWI da a in a mo e p ecise and accu a e ashion. The wo p oposed p o ocols will be
alida ed on a la ge da ase o p o e di e en ep oducibili y aspec s. In he end, I will w ap he wo
19
p o ocols in o con aine s ha a e easy o use and gua an ee ep oducibili y o o he esea che s who
a e in e es ed in halamic s uc u e connec i i ies.
In he hi d s udy, I will examine he in ol emen o he halamus in some main human
language sys ems. As will be in oduced in chap e 5, he egionaliza ion o language unc ions in he
human b ain has s a ed since he wo k o B oca and We nicke in he 19 h cen u y. A e hen, many
in luen ial neu obiological models we e p oposed o add ess he unde pinnings o human language
unc ions, ei he in a gene al app oach o ocused on speci ic language sys ems (such as eading
models, speech comp ehension and p oduc ion models). Mos o hese models ha e ocused on
co ical s uc u es ac oss he ce eb al co ex, while igno ing he con ibu ions o he halamus in
human language unc ion. Gi en he ex ensi e connec ions be ween he halamus and he ce eb al
co ex, i is wo h in es iga ing he in ol emen o he halamus in human language sys ems. The
h ee main human language sys ems, eading, speech comp ehension and speech p oduc ion, ely on
h ee senso imo o in o ma ion p ocesses: isual, audi o y and mo o in o ma ion, espec i ely. The
halamus is he c i ical hub o elay his senso imo o in o ma ion om he pe iphe y and ce ebellum
o p ima y senso imo o co ices. Mo e speci ically, he h ee i s -o de halamic nuclei a e
esponsible o his labo : LGN elays isual in o ma ion, medial genicula e nucleus (MGN) and
en al la e al nucleus (VLN) elay audi o y and mo o in o ma ion espec i ely. These h ee nuclei
also ecei e eedback axons om he p ima y senso imo o co ices, which p o ide he
neu oana omical basis o hem playing oles beyond being me e elays. Thus, in S udy 3, I will
examine he in ol emen o h ee i s -o de halamic nuclei du ing speci ic language asks using
ask-based unc ional MRI.
In he ollowing chap e s, I will i s e iew he s uc u e and unc ion o he halamus, he
whi e-ma e ibe connec ions o he halamus wi h co ical and subco ical s uc u es in chap e 4.
Then, in chap e 5, I will p o ide an o e iew o he his o y o he neu obiology o language and
some in luen ial neu obiological models o language. Following ha , I will p esen he empi ical pa
o he p esen doc o al disse a ion, which con ains h ee s udies. S udy 1 (chap e 6) and S udy 2
(chap e 7) in es iga e i s -o de and highe -o de halamic ac s wi h he aim o de eloping
ep oducible p o ocols o econs uc hem wi h DWI da a. S udy 3 (chap e 8) will be ocused on he
20
halamic in ol emen in di e en human language sys ems, including eading, speech comp ehension
and p oduc ion. Finally, chap e 9 will p o ide a gene al discussion o he o e all wo k conduc ed in
he p esen doc o al disse a ion.
21
4 Thalamus
4.1 Gene al in oduc ion o he s uc u e
The halamus e e s o a small g ay ma e s uc u e on each side o he midline, lying on he
do sal diencephalon (Figu e 4.1A). I s medial su ace makes up he la e al wall o he hi d en icle.
This o al s uc u e measu es abou 3 cm in leng h and makes up 80% o he diencephalon in humans.
Al hough he halamus mainly consis s o g ay ma e , a Y-shaped whi e ma e s uc u e named
in e nal medulla y lamina a els h ough i om pos e io o an e io . I b anches a he an e io
sec ion, sepa a ing he g ay ma e mass in o h ee pa s: he an e io , he medial and he la e al
(Figu e 4.1B). The cus oma y unde s anding o halamus unc ion belie es ha i se es as a elay
om pe iphe y o co ex. Vi ually all in o ma ion eaching he co ex (wi h he excep ion o he
ol ac o y in o ma ion) mus pass h ough he halamus (She man, 2005). Nowadays, he
unde s anding o he halamus and i s unc ional ole has de eloped and ecen indings o e he las
15-20 yea s ha e shown ha he elay unc ion is no he only ole played by he halamus in b ain
unc ioning. The halamus con inuously plays a c i ical ole in u he co ical in o ma ion p ocessing,
by ac ing as a hub ha ans e s in o ma ion be ween co ical s uc u es ia mul iple
co ico- halamo-co ical whi e-ma e ou es. The halamus is no a homogeneous s uc u e; i has a
e y complex in e nal o ganiza ion. I consis s o se e al s uc u ally and unc ionally dis inc cell
g oups, o nuclei, which a e usually named acco ding o hei opog aphic loca ion.
22
Figu e 4.1.A) Midsagi al iew o he halamus om a human, a monkey, a ca , and a a o show he
posi ion and ela i e size o he halamus (diagonal s ipes). Figu e adap ed om She man & Guille y
2006. B) The in e nal medulla y lamina sepa a es he halamus in o h ee pa s: an e io , medial and
la e al. Figu e adap ed om Wikipedia (h ps://en.wikipedia.o g/wiki/Thalamus).
4.2 Thalamic a lases and nuclea di isions
The complexi y o he in e nal o ganiza ion o he halamus has made i di icul o each a
consis en and de i able scheme abou how o ca ego ize and label i s nuclei. Figu e 4.2 shows some
di e en pa cella ion schemes in he his o y o halamus esea ch. The di e ences on how o
pa cella e he main di isions o he halamus a e ob ious, ega dless o di e en naming o indi idual
s uc u es. These pa cella ion di e ences a e mainly due o he exis ence o g ea indi idual a ia ions
o halamic opog aphy, age/disease- ela ed changes, and also di e ences o pe spec i e among
esea che s (Mai & Maj anik, 2019). Among hese h ee ac o s, he la ges a ia ion in pa cella ion
schemes migh come om he di e ences in pe spec i e among esea che s. Figu e 4.3 shows how
di e en specialis s delinea e and name he halamic nuclei on he exac same halamus sec ion.
Al hough i is clea ha he halamic nuclei should be dis inguished by ana omically signi ican
ea u es and named in elligibly, app eciable di e ences wi h espec o segmen a ion and e ms being
used o he halamic nuclei can be no iced among hose maps. Resea che s wi h di e en aining
backg ounds, ained in g oups wi h a gi en his o y o adi ion in he way o segmen and classi y
halamic nuclei, expe ience wi h animal o human b ain, and pe spec i e owa ds halamic unc ion
could possibly lead o di e ging pa cella ion schemes.
23
Figu e 4.2. The p o ile o 12 co onal sec ions h ough he halamus o di e en b ains cu a he le el
o he pos e io commissu e. Figu e adap ed om Mai & Maj anik 2019.
Some imes also he same esea che o esea ch g oup has adop ed di e en pa cella ion and
e minology h oughou hei academic li e. Fo ins ance, in Mai & Fo u an’s chap e Thalamus om
he book “The Human Ne ous Sys em” (Mai & Fo u an, 2012), hey classi y he nuclei in o six
g oups: supe io egion, medial egion, la e al egion, in alamina o ma ion, pe i en icula
o ma ion and pos e io o ma ion. In o al, hey epo ed 28 ana omically and unc ionally dis inc
s uc u es. While in 2019 (Mai & Maj anik, 2019), a e quan i a i ely compa ing se e al a lases, Mai
and Maj anik desc ibed 9 halamic nuclea g oups ha ha e di e en s uc u e han i in Mai &
Fo u an (2012). Fo example, in Mai & Fo u an (2012), he an e io nuclei (an e o en al nucleus,
an e omedial nucleus, an e odo sal nucleus, do sal supe icial nucleus) a e de ined as he supe io
g oup, while he same nuclei a e g ouped in o he an e odo sal g oup in Mai & Maj anik (2019).
24
Figu e 4.6. The OR econs uc ed in A) Benjamin e al. (2007); B) She bondy e al. (2008).
4.4.2 Medial genicula e nucleus
The MGN, also known as medial genicula e body, is an o al mass loca ed medially and
pos e io ly on he en al la e al su ace o he halamus (Figu e 4.1B). I is he synap ic s a ion o
acous ic in o ma ion lowing om he in e io colliculus o he audi o y co ex (Wine e al., 2005).
Al hough he MGN can be u he subdi ided in o h ee nuclei, due o he ela i ely small olume o
human MGN and ypically limi ed spa ial esolu ion o MRI images, he cu en wo k will no
di e en ia e he MGN sub egions ega ding he s uc u e and unc ion, bu will ake he MGN as a
whole.
The acous ic adia ion (AR) ibe s cons i u e he majo inpu o he MGN o he ipsila e al
p ima y audi o y co ex (A1, BA 41, and pa ly 42 o Heschl’s gy us) which lies in he empo al
ope culum. The AR main ains a opog aphical ep esen a ion on he co ex, simila o he
soma osenso y and mo o p ojec ions, bu also p esen s a ono opic o ganiza ion (see Che ches, 2016).
Func ionally, he AR is basically he main senso y pa hway elaying acous ic in o ma ion om he
MGN o he A1 (Be man e al., 2013), and being implica ed in mul iple high-le el beha io s such as
speech p ocessing (Ojemann, 1991). A ecen s udy showed ha he ask-dependen modula ion o he
31
le MGN was inc eased when p ocessing speech wi h noise backg ound in con as o he clea
speech p ocessing (Mihai e al., 2021). The e is an hypo hesis ha some phonological de ici s a e
caused by abno mal AR o MGN s uc u e. Fo example, his ological al e a ions we e ound in MGN
a e pos mo em examina ion o eade s wi h dyslexia (Galabu da e al., 1994). Addi ionally, one
unc ional MRI s udy ound ha he MGN ac i a ion pa e n is di e en when he ask equi ed
a ending o phonemes compa ed wi h o he speech ea u es in eade s wi h dyslexia, and he
ac i a ion le el is co ela ed wi h he sco es o he dyslexia diagnosis (Diaz e al., 2012). Di usion
e idence also showed ha he connec i i y s eng h o AR is educed in eade s wi h dyslexia
compa ed o no mal eade s (Tschen sche e al., 2019).
The e a e se e al s udies ying o ack he cou se o he AR based on in i o di usion MRI
da a (Beh ens e al., 2007; Be man e al., 2013; Ja ad e al., 2014; Ma ei e al., 2018, 2019; P o an e
al., 2014). The AR, as a non-dominan ibe g oup, c osses wi h o he ibe s, which esul s in
mul iple-o ien a ion signals in oxels a he c ossing sec ion. Thus, he AR is mo e di icul o iden i y
wi h a single di ec ion pe oxel ac og aphy model. Beh ens e al. (2007) de ined he MGN as a
cuboid medial o he LGN and s a ed acking om he e o he A1 using a p obabilis ic algo i hm. In
Beh ens e al’s (2007) s udy hey ailed o econs uc he AR wi h single ibe ac og aphy, bu
succeeded when hey used mul i- ibe ac og aphy. In a mo e ecen s udy, Ma ei e al. (2019)
explo ed how he di usion MRI acquisi ion pa ame e s and acking pa ame e s can a ec he
econs uc ion o AR. They ound ha highe b- alues (≥5,000 s/mm2) and mo e g adien di ec ions
(≥128) inc ease he accu acy o he econs uc ion o bo h p obabilis ic and de e minis ic acking
algo i hms, bu wi h low b- alues (≤3000 s/mm2) only he p obabilis ic algo i hm can success ully
econs uc he AR (Figu e 4.7).
32
Figu e 4.7. The e ec o DWI acquisi ion pa ame e s and acking algo i hms on he AR
econs uc ion. These images om one ep esen a i e subjec showed a ian econs uc ion o he AR
when applying bo h p obabilis ic and de e minis ic algo i hms o DWI da a wi h di e en di usion
b- alue shells. The whi e a ows indica e he alse posi i es o he econs uc ion. Figu e adap ed om
Ma ei e al. (2019).
4.4.3 Ven al la e al nucleus
TheVLN, lying in he en al la e al pa o he halamus, is an in eg a i e hub o mo o
con ol. The majo inpu s o VLN a e om deep nuclei o he ce ebellum, pallidum and he subs an ia
nig a. In u n, i p ojec s o he mo o a eas o he ce eb al co ex. On op o ha i also ecei es
eedback in o ma ion om he mo o a eas. Topog aphically he VLN can be di ided in o wo
subnuclei: he an e io VLN (VLa) and he pos e io VLN (VLp), wo ana omically, his ochemically
and unc ionally di e en sub egions. The in ol emen o he VLN in mo o unc ions has been
demons a ed using simple mo o - ela ed asks, such as inge apping (Lu z e al., 2000; Mallol e al.,
2007). E idence also ound ha ac i a ion o he VLN in a mo e complex mo o ask dec eased a e
in ensi e lea ning (Lehé icy e al., 2005). Success o speech p oduc ion needs mo o execu ion, hus
33
equi es he in ol emen o he VLN. Tou ille & Guen he (2011) included VLN as one impo an
node in hei speech p oduc ion neu oana omy model. Pa ien s wi h VLN lesions ha e been ound o
exhibi di icul ies in naming objec s and in sho - e m e bal memo y (see e iew Pe o ici, 1980).
The main ibe g oup connec ing VLp wi h ce ebellum is known as he den a o halamic ac
(DT). The DT o igina es om he den a e nucleus in he ce ebellum, p ojec s ia he supe io
ce ebella peduncle (i.e., b achium conjunc i um) o e mina e in he con ala e al VLp a e
decussa ing o he con ala e al ed nucleus (Coenen e al., 2014; Kwon e al., 2011). The DT is he
main ce ebella e e en ac and i is mainly in ol ed in mo emen con ol and mul iple mo o
beha io s such as speech p oduc ion (Ojemann, 1975). Lesions o he DT can p oduce abno mal
mo emen , including a axia, emo , and dys onia (Kwon e al., 2011). Some e ec i e su gical
in e en ions o pa ien s wi h essen ial emo o en a ge he VLp and adjacen whi e-ma e ac s
(Dallapiazza e al., 2019). The VLN p ojec s o he p ima y mo o co ex (M1) ia he mo o adia ion
(MR, Ilinsky & Kul as-Ilinsky, 2002). The VLN also p ojec s o he p eSMA (BA 6), which is an a ea
o he mo o co ex ha adi ionally has been assigned as esponsible o upda ing mo o plans and
lea ning new mo o sequences (Hamani e al., 2006). Func ionally, his VLN-M1 p ima y pa hway is
hough o be implica ed in ansmi ing ce ebella inpu s o he M1.
The ac ha he DT a els h ough deep and small nuclei makes i di icul o be iden i ied
wi h DWI echniques. The e a e only a hand ul o s udies ha success ully econs uc ed he DT om
DWI da a in heal hy popula ions (Figu e 4.8A; Kwon e al., 2011; Meola e al., 2016) o pa ien s
(Figu e 4.8B; Coenen e al., 2011, 2014; Nowacki e al., 2019). In Nowacki e al’s (2019) s udy, hey
es ed ou di e en acking p o ocols o iden i y he DT, which led o di e gen esul s. All he ou
p ocedu es we e based on de e minis ic algo i hms, bu no p obabilis ic algo i hms we e es ed. Some
s udies also econs uc he DT and MR as one single ac (Q. Ji e al., 2019; Vo e al., 2015). Hyam
e al. (2012) in es iga ed he whi e-ma e ibe bundles be ween VLN and mo o co ex and
success ully econs uc ed he MR. Bu his s udy only used pa o he VLN ( e e ed as en alis
in e medius and en alis o alis in hei s udy) as seed when applying he ac og aphy.
34
Figu e 4.8. DT econs uc ed in A) Kwon e al., 2011 and B) Nowacki e al., 2019 ( ou colo s
indica e he ou di e en me hodologies o acking he DT).
4.5 Highe -o de elay halamic nuclei
In con as o he i s -o de elay nuclei, which ecei e a e en inpu s om pe iphe al
senso y cen e s ( e ina, audi o y and soma osenso y elays, ce ebellum, e c.) and elay in o ma ion o
he ce eb al co ex; he highe -o de elay nuclei ecei e ew o no a e en s om pe iphe y, bu
ins ead ecei e hei a e en s om he ce eb al co ex. The majo halamoco ical ibe s om he
i s -o de elay nuclei p ojec o he p ima y co ical a eas, such as isual co ex, audi o y co ex,
soma osenso y and mo o co ex, whe eas he halamoco ical ibe s om he highe o de nuclei send
in o ma ion o a eas ha a e in ol ed in mo e complex unc ions and a e adi ionally called
associa ion co ical a eas (Guille y, 1995). Unlike a clea classi ica ion o i s -o de nuclei, he
highe -o de nuclei a e a om being ully de ined. Al hough la ge inconsis encies emain, he AN,
MD and pul ina a e he ones consis en ly ca ego ized as highe -o de elay nuclei.
4.5.1 An e io nuclea complex
The AN is loca ed in he os al and do sal pa o he halamus and sepa a ed om o he
do sal halamic nuclei by he Y-shaped in e nal medulla y lamina. The AN ypically consis s o h ee
nuclei: an e io medial, an e io do sal and an e io en al nuclei. In he p obabilis ic a las p oposed
by Iglesias e al. (2018), he halamic nuclei AV ac ually ep esen s he whole AN. In he
35
cu en hesis we will use his halamic nuclei bu wi h he name o AN o desc ibe he whole
an e io nuclea complex. The AN is a c i ical node in he Papez ci cui , which begins in he
hippocampus and con inues h ough o nix, eaching o he mammilla y body (Pa meggiani e al.,
1971). The mammilla y body connec s o AN wi h he mammillo halamic ac . The AN in u n
p ojec s h ough he cingulum bundle o cingula e co ex, e osplenial co ex, which connec s o
hippocampus, hus comple ing he ci cui (Shah e al., 2012). Mo e speci ically, he AN ecei es
a e en inpu s om he mammilla y body and elays i o an e io and pos e io cingula e, and
e osplenial co ex.
The AN has been ound in ol ed in memo y- ela ed p ocesses. Fo example, Aggle on &
B own (1999) p oposed ha he AN con ibu e o he in o ma ion e ie al om memo y du ing i em
absence h ough he connec ion wi h he hippocampus, and ha he di ec hippocampal-AN
connec ion and he indi ec hippocampal-mamillo-AN connec ion a e in ol ed in allocen ic spa ial
memo y. A MRI s udy also p esen ed a signi ican in ol emen o AN in ecall asks bu no in
encoding asks (Pe gola e al., 2013).
The e a e some s udies ha use di usion da a o econs uc he Papez ci cui , which includes
he whi e-ma e ibe s connec ion o he AN wi h o he s uc u es in ol ed in his ci cui (Concha e
al., 2005; G anzie a e al., 2011; Wei e al., 2017). Howe e , hose s udies ail o dis inguish he
whi e-ma e bundles connec ing he AN wi h o he s uc u es sepa a ely. Fo example, he cingulum
bundle consis s o he whi e-ma e ibe s o AN o an e io cingula e, pos e io cingula e, and
e osplenial co ex, while in he abo e-men ioned s udies, he cingulum bundle is aken as a whole
(Figu e 4.9).
36
Figu e 4.9. The cingulum bundle econs uc ed om di usion da a in A) Wei e al., 2017; B) Concha
e al., 2005.
4.5.2 Mediodo sal nucleus
The MD is an o oid s uc u e ex ending om he le el o he in a halamic adhesion un il he
le el o he habenula commissu e. I is ela i ely easy o dis inguish om o he s uc u es on mos
c oss-sec ional le els since i s medial su ace bo de s he hi d en icle and he es issu ounded by
he in e nal medulla y lamina wi h i s embedded nuclei. The MD is ypically di ided in o wo
cy oa chi ec onically dis inc pa s: he medial one- hi d o one-hal is magnocellula in Nissl
p epa a ions and he la e al hal o wo- hi ds, which has smalle cells and p esen pa ocellula
p ope ies (Jones, 1985). In he p esen doc o al disse a ion, we ea he MD as whole due o he
MRI spa ial esolu ion.
The MD is obus ly connec ed wi h he on al lobe, mo e speci ically he PFC. Indeed he
connec ion o MD and PFC has been playing an impo an ole in he esea ch his o y o PFC
unc ion: he classic de ini ion o PFC was he co ical p ojec ion zone o he MD (Mai & Fo u an,
2012). The MD no only p ojec s o PFC bu also ecei es a e en s om o he s uc u es, such as
amygdala (Aggle on & Mishkin, 1984), and he empo al pole (Gowe , 1989). The MD has been
p oposed o be in ol ed in highe cogni i e unc ions ia i s ex ensi e connec ions wi h on al lobe
and subco ical s uc u es (Mi chell, 2015; Ouhaz e al., 2018). Fo example, MRI esea ch wi h
humans has e ealed ha he MD-PFC ne wo k is ac i a ed du ing success ul encoding and e ie al
(Pe gola e al., 2013). Also, e idence om lesion s udies indica es ha he damage o MD could
37
dis up execu i e unc ions, a en ion con ol, p ospec i e memo y, a ousal, mo i a ion, language
unc ions (see e iews, Mi chell, 2015; Pe gola e al., 2018).
A numbe o s udies ha e used di usion MRI o econs uc he p ojec ions o MD (Figu e
4.10; Ecke e al., 2012; Gi aldo-Chica e al., 2018; Jakab e al., 2012; Klein e al., 2010; Lambe e
al., 2017; Le Res e e al., 2016; Li e al., 2022). Jakab e al. (2012) adop ed a p obabilis ic algo i hm
o ack he whi e-ma e ibe s om he la e al and medial MD o he es o he b ain. The
p obabilis ic ac og aphy showed ha he la e al MD is he sou ce o ibe s p ojec ing o he supe io
and middle on al gy i, while he ibe s o igina ed om medial MD mainly e mina e in he on al
o bi al co ex and a ious empo al loci.
Figu e 4.10. The ac og aphy o MD wi h he es o he b ain om Jakab e al. (2012). Wa m colo
oxels indica e he ajec o y o whi e-ma e ibe s o igina ed om la e al MD and cold colo oxels
o he medial MD. Figu e adap ed om Jakab e al. (2012).
4.5.3 Pul ina
The pul ina lies in he pos e io pa o he halamus and i is he la ges halamic nuclea
complex, eaching abou 30% o i s olume in humans (Mai & Fo u an, 2012). The pul ina is
ypically subdi ided in o ou subnuclei based on neu oana omical p ope ies (Jones, 1985): an e io ,
in e io , la e al and medial. This pul ina subdi ision was adop ed by he p obabilis ic a las used in
he cu en wo k (Iglesias e al., 2018). Aside om i s size, he pul ina is widely connec ed wi h all
38
he ce eb al lobes, which makes i one o he mos complex highe -o de halamic s uc u es (Mai &
Fo u an, 2012).
Speci ic unc ions associa ed wi h each co ico-pul ino-co ical pa hway ha e been explo ed
in neu oimaging esea ch, bu s ill hey a e a om being clea (Fiebelko n & Kas ne , 2019).
Resea ch ha e linked he human pul ina o mul iple cogni i e unc ions, such as emo ion ecogni ion
(Wa d e al., 2007), isual a en ion (Fische & Whi ney, 2012), isual mo ion (Villeneu e e al.,
2005), and ea ecogni ion (McFadyen e al., 2019). Also, he e is discussion abou he no ion ha he
pul ina beha es as a ‘connec ional hub’ in eg a ing con e gen in o ma ion and hen ansmi ing
p ocessed signals o co ical and subco ical s uc u es (B idge e al., 2016). Ye , li le is known
ega ding whe he he pul ina elay unc ion is o help communica ion be ween co ical a eas wi hou
changing he in o ma ion being elayed, o he pul ina also manipula es and p ocesses ha
in o ma ion (She man & Guille y, 2009).
To da e, he e a e ew ac og aphy s udies in es iga ing he connec ions o human pul ina ,
mainly due o i s complex whi e-ma e connec i i y wi h ex ensi e ce eb al co ex. Leh e al. (2008)
used di usion enso imaging ac og aphy and econs uc ed pul ina ac s (Figu e 4.11A). The
econs uc ed ac s we e ound o p ojec o he on al eye ields, p e on al a eas, isual co ex, and
pa ie al associa ion a eas. Ano he ac og aphy s udy ocused on he op ic ne e ha connec s he
op ic chiasm wi h pul ina (Maleki e al., 2012), in which hey used he op ic chiasm and pul ina as
inclusion ROIs and LGN and p ima y isual co ex (V1) as exclusion masks (Figu e 4.11B). The
in es iga ion o he s uc u al connec i i y o he pul ina and i s unc ional ole in language- ela ed
p ocesses a e beyond he scope o he p esen doc o al wo k, due in pa o i s complexi y and he
limi ed ime. Howe e , his is one o he i s esea ch lines we a e planning o pu sue a e he
doc o al disse a ion.
39
Figu e 4.11.A) Recons uc ed ac s ha connec he pul ina wi h co ical and subco ical s uc u es
in Leh e al., (2008). B) Op ic ne e om op ic chiasm o he pul ina econs uc ed in Maleki e al.
(2012).
40
Figu e 5.4. Neu ological and cogni i e model o language p oposed in P ice 2000. Figu e adap ed
om P ice 2000.
5.2.1.2 MUC model
Bo h he Classic model and P ice’s model cons ain hei amewo k a wo d-le el language
p ocessing and p eclude any language componen s beyond wo d p ocessing. Hagoo (2005, 2013)
p oposed a Memo y-Uni ica ion-Con ol (MUC) model ha akes in o accoun wha goes on beyond
p oduc ion and comp ehension o single wo ds. The MUC model di ides language p ocessing in o
h ee componen s: Memo y, Uni ica ion, and Con ol (Figu e 5.5A). The Memo y componen
ep esen s he linguis ic in o ma ion ha ge s encoded and consolida ed memo y du ing language
acquisi ion, such as phonology and phoneme knowledge, seman ic memo y and syn ac ic p ope ies o
wo ds. This componen p ojec s o he le empo al co ex acco ding o he MUC model. The
Uni ica ion in his con ex is he ope a ion o uni ying lexical in o ma ion in o o e all ep esen a ions
ha span mul i-wo d u e ances, which is c i ical in highe le el language p ocessing. The Uni ica ion
akes place in he le in e io on al co ex, wi h a spa ial g adien (Figu e 5.5B). Depending on he
ype o in o ma ion, seman ic in o ma ion is uni ied in pa s o bi alis; syn ac ic uni ica ion ec ui s
pa s iangula is; phonology uni ica ion in ol es he pa s ope cula is. The Con ol componen e e s
o a en ional con ol and ac ion planning du ing a con e sa ional se ing, which, o example, allows
a bilingualism o swi ch o he co ec language du ing con e sa ion. The model sugges s ha he
Con ol componen in ol es he an e io cingula e co ex (ACC) and he do sola e al p e on al co ex
(dlPFC). The MUC model is a subs an ial augmen a ion o he Classic model wi h h ee majo
addi ions: i s , he connec i i y o he c i ical language egions is mo e expanded and no es ic ed o
he a cua e asciculus, which is p oposed by he Classic model. Second, he componen s in ha model
a e no sepa a ed in e ms o p oduc ion and comp ehension as in he Classic model, bu ins ead a e
di ided in o memo y, uni ica ion and con ol. Thi d, he ne wo k p oposed is mo e ex ended han i in
he Classic model, which was mainly based on e idence om single wo d p ocessing. Al hough he
ne wo k is no exclusi ely o language p ocessing, i is necessa y o be ec ui ed o he sake o
success ul language p ocessing.
47
Figu e 5.5.A) The MUC model p oposed by Hagoo (2005, 2013). Memo y (yellow) in he le
empo al co ex, Uni ica ion (blue) in le IFG, and Con ol (pink) in he dlPFC. The ACC (pa o he
Con ol componen ) is no shown. Figu e adap ed om Hagoo (2013). B) The uni ica ion g adien in
he le in e io on al co ex.
5.2.1.3 Lau’s model
Simila o he MUC model, Lau and colleagues p oposed a seman ic p ocessing in con ex o
sen ence p ocessing model ha di ides seman ic p ocessing in o ou componen s: lexical s o age and
access, lexical e ie al, lexical selec ion and combina o ial seman ics (Figu e 5.6). Bo h he seman ic
in o ma ion s o age and combina o ial seman ics componen s a e sha ed be ween he MUC model and
Lau’s model, while he neu oana omical labo di isions a e sligh ly di e en . In Lau’s model, lexical
in o ma ion is s o ed in pos e io MTG. The an e io empo al co ex and angula gy us a e in ol ed
in combining incoming lexical in o ma ion wi h exis ing seman ic and syn ac ic ep esen a ion. In he
MUC model he seman ic memo y s o age was p edic ed o ec ui he whole empo al co ex, and he
combina o ial seman ic ope a ions we e p edic ed o ake place in IFG. In con as , he IFG is
in ol ed in di e en unc ions in Lau’s model: he an e io IFG (aIFG) is ec ui ed in lexical
ep esen a ion e ie al and con ol, and he pos e io IFG (pIFG) media es lexical selec ion om
mul iple ac i e candida es.
48
Figu e 5.6.A) Schema ic model o seman ic p ocessing o wo ds in con ex p oposed by Lau e al.
2008.B) The co esponding unc ional neu oana omic amewo k. Figu e adap ed om Lau e al.
2008.
5.2.2 Reading models
A eading dual pa hway model was p oposed by Pugh and colleagues (Pugh e al., 2000,
2001) based mainly on neu oimaging e idence o single wo d eading om eading-impa ied and
no mal popula ions (Figu e 5.7). In his model, he eading ne wo ks comp ise an e io and pos e io
ci cui s. The an e io ci cui s a e loca ed in he IFG and a e associa ed wi h phonological ecoding
du ing eading. The pos e io eading ci cui s include bo h do sal (angula gy us, SMG and pSTG)
and en al (occipi o- empo al junc ion) componen s. The do sal ci cui s a e c i ical in mapping he
isual inpu o p in ed wo ds o phonology and he en al ci cui s a e in ol ed in isual o hog aphic
in o ma ion p ocessing.
49
Figu e 5.7. Th ee c i ical componen s in he dual pa hway model p oposed by Pugh and colleagues.
Figu e adap ed om Pugh e al. (2001).
5.2.3 Speech comp ehension models
The e a e wo popula neu oana omical models explaining speech comp ehension. Bo h o
hem could be simply e e ed o as dual s eam models, p oposed by Hickok and Poeppel (2000,
2007), and by F iede ici (2002, 2011, 2012).
50
Figu e 5.8. The dual s eam model p oposed by Hickok and Poeppel. Figu e adap ed om Hickok &
Poeppel 2007.
Acco ding o he Hickok and Poeppel dual s eam model, he speech comp ehension s a s
om he bila e al audi o y co ex, which in ol es spec o empo al analysis (Figu e 5.8). The
phonological p ocessing in ol es he bila e al mid-pos STS. A e wa ds, wo s eams eme ge and
ca y he phonological in o ma ion o he on al lobe h ough di e en pa hways. The do sal pa hway
goes h ough he Syl ian issu e a he pa ie o- empo al bounda y and eaches he IFG and p emo o
co ex. This pa hway mainly maps phonological in o ma ion on o a icula o y ep esen a ions in he
on al co ex, which is c i ical o speech de elopmen and p oduc ion. The en al pa hway goes
h ough he pos e io MTG and ITS a e lea ing he mid-pos STS, and eaches he an e io MTG and
ITS. This pa hway is esponsible o accessing seman ic ep esen a ion om he phonological
in o ma ion (pos e io MTG and ITS) and combina o ial seman ics (an e io MTG and ITS). In
gene al he do sal pa hway is le hemisphe e-dominan while he en al pa hway is bila e al.
F ide ici’s dual s eam model sha es neu oana omical egions wi h Hickok and Poeppel’s
model (Figu e 5.9). This model is de i ed mainly om speech sen ence p ocessing and p oposes ha
bo h he do sal pa hway and he en al pa hway se e mo e unc ions han he ones o iginally
p oposed by Hickok and Poeppel. Fo example, he do sal pa hway no only subse es mapping om
audi o y o mo o , bu can also be in ol ed in syn ac ic p ocessing in sen ence comp ehension. Taking
51
in o accoun whi e-ma e ibe s in ol ed in he speech comp ehension ne wo k, wo do sal pa hways
a e p oposed in his model: one connec ing he pSTG/STS o he p emo o co ex (audi o y-mo o
mapping), he o he pa hway eaching o he pos e io IFG (BA44) om pSTG/STS ia he a cua e
asciculus (syn ac ic p ocessing). Simila ly, he en al s eam can be also subdi ided in o wo
pa hways: one pa hway connec s he empo al co ex wi h BA45 and BA47, which is suppo ing
audi o y-seman ic mapping; he o he pa hway connec s he an e io STG o on al ope culum and
seen as suppo ing he combina ions o adjacen elemen s in a sequence, which is impo an in
sen ence comp ehension.
Figu e 5.9. The dual s eam model p oposed by F ide ici. Figu e adap ed om F ide ici 2012.
5.2.4 P oduc ion models
Inde ey and Le el p oposed a wo d p oduc ion model based on he ime cou se o c i ical
componen p ocesses and he in ol emen o b ain egions in wo d p oduc ion (Inde ey, 2011;
Inde ey & Le el , 2004). The wo d p oduc ion ne wo ks consis o he le pos e io IFG, he le
p ecen al gy us, he supplemen a y mo o a ea (SMA), he le mid and pos e io pa s o he STG
and MTG (Figu e 5.10). The halamus and ce ebellum a e also included in his model, being in ol ed
in phone ic encoding and a icula ion p ocesses.
52
Figu e 5.10. Neu oana omical model o wo d p oduc ion p oposed by Inde ey and Le el . The
numbe s wi hin egions indica e median peak ac i a ion ime es ima es in milliseconds. Figu e
adap ed om Inde ey (2011).
Tou ille and Guen he de eloped he DIVA model o speech p oduc ion (Figu e 5.11;
Tou ille & Guen he , 2011). This model is buil on a compu a ional model unde he same name, and
uni ied neu oana omical e idence ha in ol es speech acquisi ion and p oduc ion. This model
comp ises mul iple componen s ha con ibu e he successes o speech p oduc ion and assign hem o
co esponding b ain egions (see ana omical labels in Figu e 5.11). In he DIVA model he halamus
also plays a c i ical ole wi h i s p ojec ions o he mo o co ex se ing as ga es on he ou low o
mo o commands, and being in ol ed in ep esen ing he eed o wa d mo o p og ams.
53
Figu e 5.11. The DIVA model o speech acquisi ion and p oduc ion. In he box he e a e he cogni i e
componen s o he model and he co esponding ana omical egions. Figu e adap ed om Tou ille &
Guen he (2011).
In sum, since B oca and We nicke’s pionee ing wo k linking human language unc ions and
speci ic b ain egions, he knowledge abou he neu obiological unde pinning o language has been
emendously expanded. Many neu obiological models ha e been p oposed since hen o accoun o
he ep esen a ions o language unc ions in di e en b ain egions. The in luen ial models e iewed
abo e made connec ions be ween c i ical componen s o language p ocessing and b ain s uc u es.
Howe e , mos o hese models a e ocused on co ical s uc u es, neglec ing o some ex en he ole
o halamus in language (only he DIVA model on speech p oduc ion included he halamus in he
speech a icula ion phase) despi e i s widesp ead connec ions wi h he ce eb al co ex. One o he
main goals o he p esen doc o al wo k is o in es iga e he ole ha halamus is playing in some o
he main language sys ems.
54
6 S udy 1: S uc u al connec ion o i s -o de halamic
nuclei
The p esen s udy was aimed a de eloping and es ing a ep oducible p o ocol o
ob aining ou i s -o de elay halamic inpu and ou pu whi e-ma e ac s. The no el y o
his p o ocol capi alizes on 4 aspec s: (1) i is ocused on well-known whi e-ma e ac s
cons i u ed by myelina ed axons ha o igina e and/o a ge he i s -o de elay nuclei o he
halamus, es ing hem wi hin he same s udy, and using simila me hods and econs uc ion
p ocedu es ac oss hem; 2) di e en om mos p e ious s udies, he e we speci ically
in es iga ed in a la ge da ase he eliabili y o he p o ocol in e ms o bo h compu a ional
and es - e es ep oducibili y; 3) he p esen p o ocol uses s a e-o - he-a MRI p o ocols
(mul iband, mul i-shell) and ac og aphy me hods wi h he aim o de eloping an ad anced
p o ocol ha can be applied o cu en ongoing s udies and u u e esea ch; and, 4) he
p o ocol is designed o be ep oducible, easy o use and au oma ized, which un o una ely has
no been he no m in he pas . Also, i builds on p e ious well- alida ed ools including he
i s p obabilis ic a las o he halamus based on combining high- esolu ion ex i o MRI and
his ology (Iglesias e al., 2018) and he ep oducible- ac -p o iles (RTP2) con aine ized ool
which is based on s a e-o - he-a echniques implemen ed on op o Vis aso 's code, which
ha e been es ed and used in many publica ions o e he las 15 yea s
(h ps://www.gi hub.com/ is alab/ is aso ; Le ma-Usabiaga e al., 2022). The ul ima e goal
o his wo k was o p o ide a eliable p o ocol o ob aining and es ima ing i s -o de elay
halamic pa hways o basic esea ch and clinical s udies.
55
To his end, we i s de ined mul iple pa ame e s o op imally econs uc he
abo e-men ioned ou halamic pa hways in le and igh hemisphe es. Second, we es ed he
compu a ional and es - e es ep oducibili y o ou p o ocol by examining a ange o
whi e-ma e p oxies ela ed o he mic os uc u al and mac os uc u al p ope ies o hese
ac s. To examine he eliabili y o he p o ocol we ob ained ac s om DWI o 113 no mal
adul s. The p o ocol consis ed o h ee componen s: De ining he egions-o -in e es (ROI);
p ep ocessing DWI da a; modeling whi e-ma e ac s and ac ome y. Rep oducibili y was
es ed using wo app oaches: 1) Compu a ional ep oducibili y, es ed by iden i ying each
ac using he same pa ame e s 10 independen imes o all 113 subjec s, and 2) Tes - e es
ep oducibili y, es ed by e-scanning a subse o 24 pa icipan s using he same MRI p o ocol
wice wi hin an a e age in e al o 15 days. Ou hypo hesis was ha we would ob ain a high
deg ee o ep oducibili y o he mic os uc u al and mac os uc u al p ope ies o hese
ac s. Howe e , we expec ed some a iabili y in speci ic ac s, such as he DT which c osses
hemisphe es and i is ela i ely long, and hypo hesized ha his a iabili y would be highe
o es - e es han o compu a ional ep oducibili y.
6.1 Me hods
6.1.1 Subjec s
A o al o 113 heal hy olun ee s (mean age = 24.5 yea s, SD = 4.33 yea s; 65
emales) pa icipa ed in he s udy. Twen y- ou o he olun ee s (mean age = 24.7 yea s, SD
= 4.06 yea s; 13 emales) e u ned o a second session in which hey we e scanned using
exac ly he same MRI p o ocol (mean in e al = 15 days, SD = 21.82 days, ange: 7-104
56
To e alua e hese wo ypes o ep oducibili y a he mic os uc u al scale, we
pe o med pai wise co ela ions on ac p o iles o all possible pai s om he 10 epea ed
compu a ions o measu e compu a ional ep oducibili y, and ac oss es and e es o measu e
es - e es ep oducibili y. Fo simplici y, we only show he co ela ions o FA alues.
A he mac os uc u al le el, we quan i a i ely analyzed ac olume o e lap,
s eamline densi y and dis ance o check he ep oducibili y o ac shapes. These analyses
included: (1) Dice simila i y index o check o olume-based o e lap o all ac pai s; (2)
densi y co ela ion o he oxel-le el s eamline densi y o all ac pai s; and, (3) bundle
adjacency, he a e age dis ance be ween s eamlines om wo ac s. The measu emen s used
o examine compu a ional ep oducibili y and es - e es ep oducibili y we e compu ed using
he package scilpy (see de ails in Schilling e al. 2021 and h ps://gi hub.com/scilus/scilpy).
These analyses we e conduc ed ac oss all possible pai s o compu a ional ep oducibili y and
es - e es ep oducibili y, as well as o each ac .
63
Figu e 6.1. The ep oducibili y measu emen scheme. A) Compu a ional ep oducibili y
( ep oducibili y ac oss compu a ions); es - e es ep oducibili y ( ep oducibili y ac oss es and e es
sessions). B) The Dice o e lap o MR econs uc ed a he i s second compu a ions, om subjec
S038. C) Co ela ion o he FA p o ile o MR om subjec S038’s es and e es sessions.
6.2 Resul s
In he p esen s udy, we ob ained and measu ed ibe s bundles connec ing h ee
i s -o de senso y (LGN, MGN) and mo o (VLN) halamic nuclei wi h hei main
co esponding co ical a ge a eas. In addi ion, we econs uc ed he subco ical inpu
pa hway o VLp om he den a e nucleus o he ce ebellum. These ou ac s we e iden i ied
as homologous ac pai s in he le and he igh hemisphe e. Figu es 6.2 and 6.3 show hese
ac s in a ep esen a i e subjec . To examine he ep oducibili y o ou p o ocol, we ollowed
a double analy ical app oach es ing: (1) compu a ional ep oducibili y by epea ing he
compu a ion on he same di usion da a 10 imes and quan i ying changes om compu a ion
o compu a ion o he same ac ; and, (2) es - e es ep oducibili y, by ob aining DWI da a
64
om he same subjec s and using he same MRI p o ocols ac oss wo di e en sessions o
quan i y es - e es changes in he same ac s.
Figu e 6.2. The OR (A) and AR (B) econs uc ed in a ep esen a i e subjec . A1 and B1 show he 3D
ep esen a ions o he OR and AR in yellow. A2 and B2 show he posi ions om which he slices in
A3 and B3 a e espec i ely d awn. A3 and B3 depic axial and co onal iews o he co e
subcomponen s o OR and AR, espec i ely. G een colo indica es he co ical ROIs V1/V2 and A1.
65
Figu e 6.3. The MR and DT econs uc ed in a ep esen a i e subjec . A shows he 3D ep esen a ions
o he MR and DT. B shows he posi ions om which he slices in C and D a e d awn. C and D depic
co onal iews o he co e subcomponen s o MR and DT. G een colo indica es he co ical ROI M1.
Yellow s eamlines a e MR and blue s eamlines ep esen he DT. D shows he axial iew o he co e
subcomponen s o he DT.
6.2.1 Compu a ional ep oducibili y
Fo he ou pai s o whi e-ma e ibe s wi h he es ablished p o ocol, he epea ed
compu a ions on same di usion da a esul ed in mos ly iden ical ac p o iles and high
ag eemen o s eamlines. The mean co ela ions o FA p o ile we e abo e 0.99 o all he
ac s examined (see Table 6.2; Figu e 6.4B). A indi idual le el, o all he possible pai s o
compu a ion, mos co ela ion coe icien s we e highe han 0.97 o each ibe , excep o le
AR and igh DT, which ha e a long ail owa ds 0.82. Bea ing his in mind, wi h 10 epea ed
66
compu a ions, he e will be a leas 9 ela i ely low coe icien s i only one compu a ion
esul ed in a di e en ac han all he o he s.
Ag eemen indices also showed ha he iden i ied whi e-ma e ibe s ha e consis en
shapes and densi y ac oss epea ed compu a ions (Table 6.2). Figu e 6.4C shows ag eemen
indices o each indi idual pai o compu a ions. These h ee ag eemen indices e ealed he
same pa e n as he one obse ed in he co ela ion coe icien o FA p o ile, wi h mo e
a iabili y in le AR. And he same pa e n was also ound o he igh homologous AR. I is
no ewo hy ha some o his a iabili y in ag eemen indices de i es om he same single
subjec (e.g., ou lying clus e s o bundle adjacency and Dice coe icien in igh AR, and o
Dice coe icien and densi y co ela ion in le OR).
Table 6.2. Rep oducibili y indices and hei s anda d de ia ions (in pa en heses) o all
measu es and ibe bundles.
compu a ional
es - e es
FA p o ile
co ela ion
bundle
adjacency
dice index
densi y
co ela ion
FA p o ile
co ela ion
bundle
adjacency
dice index
densi y
co ela ion
L OR
0.9996(0.0016)
0.09(0.01)
0.92(0.01)
0.998(0.001)
0.9956(0)
0.11(0.01)
0.90(0.01)
0.990(0.005)
R OR
0.9996(0.0005)
0.09(0.01)
0.92(0.01)
0.998(0.001)
0.9944(0)
0.11(0.01)
0.90(0.01)
0.989(0.005)
L AR
0.9976(0.0074)
0.18(0.09)
0.85(0.05)
0.990(0.008)
0.9265(0.12)
0.39(0.27)
0.76(0.09)
0.907(0.082)
R AR
0.9989(0.0013)
0.11(0.09)
0.90(0.04)
0.997(0.002)
0.9473(0.07)
0.34(0.41)
0.80(0.12)
0.940(0.047)
L MR
0.9985(0.0012)
0.09(0.01)
0.91(0.01)
0.993(0.001)
0.9787(0.02)
0.13(0.05)
0.89(0.01)
0.971(0.010)
R MR
0.9982(0.0017)
0.09(0.01)
0.91(0.01)
0.993(0.001)
0.9713(0.03)
0.12(0.03)
0.89(0.01)
0.971(0.014)
L DT
0.9988(0.0038)
0.07(0.03)
0.93(0.02)
0.994(0.004)
0.9638(0.03)
0.21(0.11)
0.82(0.08)
0.853(0.097)
R DT
0.9986(0.0032)
0.06(0.01)
0.94(0.01)
0.994(0.004)
0.9458(0.06)
0.22(0.14)
0.82(0.09)
0.782(0.182)
67
Figu e 6.4. E alua ion o compu a ional ep oducibili y o he OR, AR, MR and DT. A) Examples o
g oup a e age FA p o iles o le MR om he i s (g ay con inuous line) and second (g een dashed
line) compu a ions. The ligh g een shaded a ea indica es he s anda d de ia ion. B) S ip plo s
showing he dis ibu ion o co ela ion coe icien s be ween all possible pai s compu ed o each ac
and each subjec (ligh e colo columns ep esen he le hemisphe e, da ke colo columns ep esen
he igh hemisphe e). Each do ep esen s he co ela ion coe icien o a speci ic compu a ion pai
o one pa icipan . C) Ag eemen indices dis ibu ion: bundle adjacency ( op), Dice coe icien
(middle), and densi y co ela ion (below) o all possible compu a ion pai s o each ac and each
subjec (ligh colo columns ep esen he le hemisphe e, da ke colo columns ep esen he igh
hemisphe e).
6.2.2 Tes - e es ep oducibili y
To examine es - e es ep oducibili y, 24 pa icipan s came back o a e es session
whe e we used exac ly he same MRI p o ocol. The mean o FA p o ile co ela ions was
abo e 0.9 ac oss he en ac s o in e es , al hough as expec ed he alues we e nume ically
lowe han hose obse ed in he compu a ional ep oducibili y analyses. As in he
compu a ional ep oducibili y analysis, he le AR also showed highe a iabili y in he
es - e es ep oducibili y analysis, wi h lowe alues wi hin he mean co ela ion coe icien s
(0.93, Table 6.2 and Figu e 6.5A & 6.5B). Ne e heless, i is impo an o highligh ha all o
hese alues e lec a high deg ee o ep oducibili y.
68
Tes - e es ep oducibili y was also con i med by he ag eemen indices. The g oup
a e ages o bundle adjacency we e all unde 0.4 o he en ac s, indica ing ha he
s eamlines iden i ied in he es we e e y close o he s eamlines iden i ied in he e es (see
Table 6.2 and Figu e 6.5C). High ep oducibili y was also e lec ed by he Dice index and
s eamline densi y co ela ion. Among he en ac s, he AR and DT ended o show mo e
a iabili y bila e ally.
Figu e 6.5. E alua ion o es - e es ep oducibili y o he OR, AR, MR and DT. A) Examples o
g oup a e age FA p o iles o le MR om es (g ay con inuous line) and e es (g een dashed line).
The ligh g een shaded a ea indica es he s anda d de ia ion. B) S ip plo s showing he dis ibu ion o
he co ela ion coe icien s be ween es and e es o each ac and each subjec (ligh e colo
columns ep esen he le hemisphe e, da ke colo columns ep esen he igh hemisphe e). C)
Ag eemen indices dis ibu ion: bundle adjacency ( op), Dice coe icien (middle), and densi y
co ela ion (below) o es and e es o each ac and each subjec (ligh e colo columns ep esen
he le hemisphe e, da ke colo columns ep esen he igh hemisphe e).
6.3 Discussion
We p esen a ep oducible p o ocol o ac og aphy econs uc ion o i s -o de
human halamoco ical ac s, which play a c i ical ole in senso y and mo o in o ma ion
elay be ween he halamus and co ex. We es ed he ep oducibili y o ou p o ocol o
69
ob aining hese ac s o in e es by examining hei mic os uc u al ac ome ic p ope ies
and olume-based mac os uc u al simila i y ac oss epea ed compu a ions and es - e es
sessions. Resul s showed nea ly pe ec compu a ional ep oducibili y ac oss en epe i ions
and high- o-excellen es - e es ep oducibili y.
In e ms o compu a ional ep oducibili y, i is wo h highligh ing ha ac oss en
sepa a e and independen compu a ions using he same aw da a and p o ocol, ep oducibili y
was nea ly pe ec ; o example, p o iding an a e age FA p o ile co ela ion o 0.99
(indi idual-subjec alues anging om 0.82 o 0.99) and an a e age Dice simila i y alue o
0.91 (indi idual Dice index alues anged om 0.66 o 0.97) ac oss all he bundles examined.
We expec ed compu a ional ep oducibili y would be high, bu i was impo an o
demons a e ha he p o ocol is eliable and app op ia e o ob aining he ac og aphy
measu es o in e es . Conce ns abou his ype o ep oducibili y, which we e e o as
compu a ional ep oducibili y, ha e g own in ecen yea s (e.g., Theaud e al., 2020). One
goal o ou p o ocol is o o e neu oscien is s and medical p ac i ione s e icien and
ep oducible p ocessing guidelines o econs uc i s -o de halamoco ical ac s. Hence, we
packed ou solu ion in so wa e con aine s ha can be un using Singula i y o Docke
echnologies. In bo h cases, exac ly he same se o algo i hms, associa ed lib a ies and
ope a ing sys ems can be un in a compu a ionally ep oducible manne . This allows
esea che s o un p e iously as well as ecen ly acqui ed da a using exac ly he same
so wa e and con igu a ion op ions many imes.
Tes - e es eliabili y, which ensu es s abili y ac oss ime, is one o he mos widely
used measu es o a p o ocol o ool. Since es - e es ep oducibili y en ails inpu ing
di e en da a, we expec ed o ob ain lowe nume ical ac ome ic and olume-based
simila i y alues han hose obse ed in ou compu a ional ep oducibili y calcula ions. A he
mic os uc u al le el, ou da a showed an a e age 0.97 (indi idual-subjec alues anged
70
om 0.50 o 0.99) es - e es ep oducibili y o FA p o ile co ela ions a e an a e age o 2
weeks. These co ela ion coe icien alues o ac p o iles a e high and consis en wi h he
alues ob ained in a p e ious s udy aimed a alida ing es - e es eliabili y in classic ibe
bundles (K upe e al., 2021).
A he mac os uc u al le el, he Dice index is commonly used o assess he o e lap o
bundles econs uc ed a wo ime poin s (Besseling e al., 2012; Boukadi e al., 2019;
Cousineau e al., 2017). In he p esen s udy, Dice index alues a e aged 0.85
(indi idual-subjec alues anged om 0.39 o 0.94) o all whi e-ma e bundles examined.
Based on he Dice index alues epo ed in p e ious s udies ocused on whi e-ma e bundles
(e.g., minimum g oup-le el Dice index o 0.70 in Besseling e al. 2012 and Cousineau e al.
2017; 0.71 in Boukadi e al. 2019), he ange o he Dice index epo ed he e (i.e., 0.76-0.90
ac oss ac s a g oup le el) indica es ha all he whi e-ma e bundles we in es iga ed had
high es - e es eliabili y.
I is impo an o unde s and he na u e o he a iabili y obse ed in hese wo ypes
o ep oducibili y measu emen s. Ou esul s showed ha compu a ional ep oducibili y is
nea ly pe ec , bu he e is some a iance ac oss epea ing compu a ions. The main sou ce o
his a iance is andom seed gene a ion. Basically, he e a e s eps in ou econs uc ion
pipeline ha in ol e non-de e minis ic p ocesses, which will be di e en ac oss
compu a ions. A ully ep oducible pipeline can be achie ed by ixing he andom seed
ini ializa ion, bu we decided no o p oceed in his way. We used a p obabilis ic algo i hm o
gene a e he s eamlines, whose ad an age has been discussed by many esea che s (see o
ins ance (Bonilha e al., 2015; G iso e al., 2021; Khalsa e al., 2014). Indeed, ixing andom
seed ini ializa ion would wo k agains he p obabilis ic na u e o he algo i hm and he
philosophy o p obabilis ic ac og aphy. In addi ion, ixing he andom seed is no
compa ible wi h using mul i- h eaded s eps ha allow o as e compu a ion. Mul i- h eading
71
in oduces andomness in e ms o he o de o s ep execu ion, which will gene ally a ec he
ep oducibili y o esul s. I is ele an o no e ha p e ious s udies ha ocused on
p obabilis ic ac og aphy ha e no examined compu a ional ep oducibili y. Toge he wi h
andom seed gene a ion, he e could possibly be o he ac o s con ibu ing o some ex en o
he compu a ional ep oducibili y a iance. Ins ead o assuming no o sligh changes among
compu a ions, esea che s should be awa e ha compu a ional a iance exis s depending on
he pa ame e s chosen o conduc he ac og aphy. I is impo an ha u u e esea ch u he
in es iga es, in a sys ema ic manne , ac o s ha migh be associa ed wi h compu a ional
a iance in p obabilis ic ac og aphy beyond andom seed gene a ion.
The es - e es ep oducibili y epo ed he e showed some a iabili y especially o
speci ic bundles. We obse ed o e all mo e a iabili y in es - e es ep oducibili y o he
AR and DT ac s. This can be in pa explained by speci ic cha ac e is ics o hese ac s, o
ins ance: i) he small seed used o econs uc ion (i.e., MGN/A1, den a e nucleus); ii) long
s eamlines (i.e., DT); and iii) ana omical complexi y (i.e., DT). P e ious s udies ha e ela ed
lowe ep oducibili y o smalle seed size (Bonilha e al., 2015; Buchanan e al., 2014; Zhang
e al., 2019) and longe s eamlines (Bonilha e al., 2015; Mo i & Van Zijl, 2002; Tsai, 2018).
Fibe acking om small seeds may be in luenced by sys ema ic e o s and noise, leading o
spu ious indings. Mo eo e , smalle seeds a e likely o gene a e less ibe s and he e o e
dec ease he likelihood o success ul acking. Simila ly, s eamlines wi h longe pa hs lead o
mo e in e up ions in ibe acking, which can also lead o la ge a ia ion. Also, he
ana omically complex DT connec s small and deep nuclei, making i mo e ulne able o
pa ial olume e ec s be ween di e en issues (Mo i & Van Zijl, 2002). Small ROIs, long
ibe s, and ana omical complexi y a ec compu a ional ep oducibili y in he same manne as
o he es - e es ep oducibili y. Ne e heless, ou p o ocol was able o econs uc he ou
pai s o ac s in all 113 subjec s. This is an impo an achie emen conside ing he
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Fo each ac and each subjec , we ex ac ed he DWI noise by i s c ea ing a mask
o he ac and co esponding seed and a ge ; hen we applied his mask o he noise image
gene a ed in he DWI p ep ocessing s ep o ex ac he a e age noise wi hin his mask. The
s eamline leng h was calcula ed by a e aging he leng h o each s eamline in one speci ic
ac . Bo h he s eamline leng h and amoun we e calcula ed o each ac and each subjec .
The ac s used in his pos -hoc analysis we e gene a ed by he i s compu a ion.
T ac -wise co ela ion analysis was conduc ed be ween he h ee possible ac o s and
he ou ep oducibili y indices o compu a ional and es - e es ep oducibili y sepa a ely. In
o de o do he co ela ion analyses, h ee ep oducibili y indices, FA p o ile co ela ion,
densi y co ela ion and Dice index we e ans o med o a no mal dis ibu ion. Fishe -Z
ans o m was adop ed o he wo co ela ion indices, o ans o m he Pea son co ela ion
coe icien o a Z sco e. The Dice index has a es ic ed ange o [0,1] and is o en close o he
alue o 1. A logi ans o m was applied o he Dice index, whe e logi (Dice) =
ln(Dice/(1-Dice)). This mono one ans o ma ion maps he Dice ange o [0,1] o [-∞, +∞],
and logi (0.5) = 0. This dis ibu ion is close o a no mal dis ibu ion o a la ge sample size
(Zou e al., 2004). Fo each ac , he g oup le el a e age o he possible ac o s and
ep oducibili y indices (a e ans o ma ion i applied) we e calcula ed ac oss subjec s. Then,
pai ed-sample co ela ion analyses we e conduc ed o each pai o ac o and ep oducibili y
index alues a ac le el.
7.2 Resul s
In he p esen s udy, we econs uc ed 42 pai s o ibe bundles connec ing wo highe
halamic nuclei wi h hei main co esponding co ical a eas (3 AN- ela ed and 39 MD- ela ed ibe
bundles) om DWI da a. To examine he ep oducibili y o ou p o ocol, we ollowed a double
analy ical app oach es ing: (1) compu a ional ep oducibili y by epea ing he compu a ion on he
same di usion da a 10 imes and quan i ying changes om compu a ion o compu a ion o he same
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ac ; and, (2) es - e es ep oducibili y, by ob aining DWI da a om he same subjec s and using he
same MRI p o ocols ac oss wo di e en sessions o quan i y es - e es changes in he same ac s.
The esul s a e p esen ed wi h he ac s o in e es di ided in o 5 g oups: (1) AN ela ed ac s (Figu e
7.1); (2) ac s o MD wi h do sola e al p e on al egions (dlPFC, Figu e 7.2); (3) ac s o MD wi h
medial p e on al egions (mPFC, Figu e 7.3); (4) ac s o MD wi h o bi al and on al pola egions
(Figu e 7.4); (5) ac s o MD wi h in e io gy us egions (IFG, Figu e 7.5).
Figu e 7.1. The compu a ional and es - e es ep oducibili y o ac s connec ing AN wi h cingula e
and e osplenial co ex. A) The la e al (A1) and medial (A2) iews o he econs uc ed ac s in a
ep esen a i e subjec . Colo scheme is he same as used in B and C. B) Compu a ional
ep oducibili y: B1) s ip plo s showing he dis ibu ion o co ela ion coe icien s be ween all
possible pai s compu ed o each ac and each subjec (ligh e colo columns ep esen he le
hemisphe e; da ke colo columns ep esen he igh hemisphe e). Each do ep esen s he co ela ion
coe icien o a speci ic compu a ion pai o one pa icipan ; Box-and-whiske plo s a e o e laid on
op o show he qua iles o he dis ibu ion. B2) Ag eemen indices dis ibu ion: bundle adjacency
( op), Dice coe icien (middle), and densi y co ela ion (below) o all possible compu a ion pai s o
each ac and each subjec (ligh e colo columns ep esen he le hemisphe e, da ke colo columns
ep esen he igh hemisphe e). C) Tes - e es ep oducibili y wi h he same layou as panel B. The y
axes o bo h panel B and C show he a ge s o hose econs uc ed ac s.
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Figu e 7.2. The compu a ional and es - e es ep oducibili y o ac s connec ing MD wi h dlPFC
sub egions. De ails abou each panel can be ound in Figu e 7.1 legend.
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Figu e 7.3. The compu a ional and es - e es ep oducibili y o ac s connec ing MD wi h mPFC
sub egions. De ails abou each panel can be ound in Figu e 7.1 legend.
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Figu e 7.4. The compu a ional and es - e es ep oducibili y o ac s connec ing MD wi h sub egions
o o bi al and pola on al co ex. De ails abou each panel can be ound in Figu e 7.1 legend.
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Figu e 7.5. The compu a ional and es - e es ep oducibili y o ac s connec ing MD wi h IFG
sub egions. De ails abou each panel can be ound in Figu e 7.1 legend.
7.2.1 Compu a ional ep oducibili y
Fo he 42 pai s o whi e-ma e ibe s wi h he es ablished p o ocol, he epea ed
compu a ions on he same di usion da a esul ed in mos ly iden ical ac p o iles and high
ag eemen o s eamlines. The g oup mean co ela ions o FA p o iles we e abo e 0.97 o all
he ac s examined. A he mac os uc u al le el, he ep oducibili y is measu ed by
calcula ing he adjacency, he s eamline densi y co ela ion, and he Dice index o wo
epea ed ac s. The esul s showed ha he ac s ha e high ep oducibili y a mac os uc u al
le el in all h ee indices. The adjacency, which e lec s he a e age dis ance be ween he wo
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ac s, anges om 0.05 o 0.20 ac oss all ac s. In all he ac s o in e es , simila o i in FA
p o ile co ela ion, he ac s connec ing he igh AN wi h he pos e io cingula e and
e osplenial co ex ha e he highes adjacency, which a e bo h 0.20. Simila high
ep oducibili y is ound in esul s o s eamline densi y co ela ion and Dice index. The
s eamline densi y co ela ion anges om 0.94 o 0.99. The Dice index showed ha all ac s
had high o e lap be ween epea ing compu a ions, anging om 0.84 o 0.95. Among all he
ac s, he ac s connec ing he igh AN wi h he pos e io cingula e and e osplenial co ex
showed good ep oducibili y bu ela i ely low Dice index o 0.84 and 0.85 espec i ely.
7.2.2 Tes - e es ep oducibili y
Twen y ou pa icipan s we e scanned o a second ime wi h he exac same MRI
p o ocol as he i s scanning session. In his 24 pa icipan es - e es subse , we measu ed
how he p o ocol could econs uc he ac s o in e es ep oducibly ac oss es and e es .
The same indices e lec ing ep oducibili y a he mic os uc u al and mac os uc u al le els
we e calcula ed. In gene al, he ac s o in e es showed good es - e es ep oducibili y
ega ding he mean o FA p o ile co ela ions, al hough as expec ed he alues we e
nume ically lowe han he ones obse ed in he compu a ional ep oducibili y analyses.
The e a e ac s showing ela i ely high a iabili y, mos ly he ac s connec ing MD wi h
o bi al on al co ex, such as he bila e al MD-A ea25, wi h he g oup mean o 0.64 (le , he
lowes co ela ion among all he ac s o in e es ) and 0.71 ( igh ). Ne e heless, i is
impo an o highligh ha all he ac s ha e a high deg ee o ep oducibili y wi h a
co ela ion a e age abo e 0.80, wi h only a ew excep ions.
A he mac os uc u al le el, he ag eemen indices showed simila pa e ns o
ep oducibili y ac oss ac s o in e es . The g oup a e age o bundle adjacency, we e all
unde 1 o all he ac s excep he le MD-A ea 25, which has an adjacency o 1.2,
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indica ing he s eamlines iden i ied in he es ha e e y close dis ance o he s eamlines
iden i ied in he e es . High ep oducibili y is also e lec ed by he Dice index, and
s eamline densi y co ela ion. Mos ac s ha e he es - e es g oup a e age Dice index and
densi y co ela ion abo e 0.7. Simila o he mic os uc u al le el o ep oducibili y, he ac s
connec ing MD wi h he o bi al on al co ex showed a ela i ely lowe Dice index and
densi y co ela ion.
7.2.3 Pos -hoc analysis esul s
Pos -hoc analysis was conduc ed sepa a ely o compu a ional and es - e es
ep oducibili y, o in es iga e he associa ions be ween he ep oducibili y and h ee ac o s o
in e es (di usion da a noise, ac s eamline leng h and quan i y). The esul s ela ed o
compu a ional ep oducibili y showed ha he di usion noise has li le co ela ion wi h he
ep oducibili y indices excep he FA p o ile co ela ion (Figu e 7.6A), which has a mode a e
nega i e co ela ion (-0.38, highe noise in di usion da a wi h lowe FA p o ile co ela ion).
The s eamline leng h and coun bo h show high co ela ion wi h all he ou ep oducibili y
indices, only wi h he opposi e di ec ion. The leng h is nega i ely co ela ed wi h
compu a ional ep oducibili y: he longe he s eamlines, he less ep oducible ac oss
compu a ions. S eamline coun has he opposi e pa e n: he mo e s eamlines one ac is
composed o , he highe he ep oducibili y ac oss compu a ions.
The co ela ion esul s o he es - e es ep oducibili y ha e simila di ec ion as in
he compu a ional ep oducibili y (Figu e 7.6B). The noise has a nega i e co ela ion wi h
es - e es ep oducibili y, anging om -0.69 o -0.48. Opposi e o he s ong co ela ion
be ween he ac s eamline leng h and he compu a ional ep oducibili y, he leng h has a
null co ela ion wi h es - e es ep oducibili y. Rega ding he co ela ion o he ac
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s eamline coun , i has a simila s ong co ela ion wi h es - e es ep oducibili y as i does
wi h compu a ional ep oducibili y.
Figu e 7.6. DWI noise, s eamline leng h and s eamline coun we e ound o be co ela ed wi h
ep oducibili y ac oss ac s. A) The co ela ion be ween he h ee ac o s wi h ou compu a ional
ep oducibili y indices. B) The co ela ion be ween he h ee ac o s wi h ou es - e es
ep oducibili y indices. *p< .05, **p< .01, ***p< .001. The symbols o he co ela ion coe icien s
wi h bundle adjacency we e e e sed o simpli y he illus a ion, as he alue o bundle adjacency
indica es he a iance be ween ac s ins ead o simila i y.
7.3 Discussion
The cu en s udy p oposes a ep oducible p o ocol o econs uc he ac og aphy o
he AN and MD om he human halamus. These nuclei play impo an oles in a ious
cogni i e p ocesses, in pa icula du ing apid in eg a ion o new lea ning, wo king memo y,
decision making and beyond. The ep oducibili y o he p o ocol o eliably ob aining hese
ac s o in e es was measu ed by examining hei mic os uc u al ac ome ic p ope ies and
olume-based mac os uc ual simila i y ac oss epea ing compu a ions and es - e es
sessions. Resul s e ealed nea ly pe ec compu a ional ep oducibili y ac oss en epe i ions
and high- o-excellen es - e es ep oducibili y o he AN and MD ela ed halamoco ical
ac s.
The s uc u al connec i i y p o ile o he highe -o de halamic nuclei, AN and MD,
has been examined in nume ous expe imen s on non-human p ima es (Bay & Ça da , 2013;
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G oenewegen, 1988; Lozsádi, 1995; Ray & P ice, 1992; Vann e al., 2007) and pos mo em
s udies on humans (Blennow e al., 2000; Cullen e al., 2003). Howe e , he in i o
examina ions o he whi e-ma e connec ion o hose nuclei wi h subco ical and co ical
egions in humans a e a e. Due o he lack o a well de ined and alida ed halamic
segmen a ion, mos ac og aphy s udies on humans used he whole halamus (Fan e al.,
2014; O’mui chea aigh e al., 2015; Pelze e al., 2017) o segmen a ion in a common space
(Klein e al., 2010) o es ima e he s uc u al connec ions o he halamus. Unlike he
i s -o de halamic nuclei ha ing ela i ely s aigh o wa d s uc u al pa hways wi h he
senso imo o co ical egions, he AN and especially MD, ha e mo e ex ensi e and complex
e e en and a e en pa hways wi h co ical s uc u es (Aggle on & Mishkin, 1984; Gowe ,
1989; Mai & Fo u an, 2012; Shah e al., 2012). The cu en s udy adop ed he i s
p obabilis ic a las combining ex i o MRI and his ological da a o de ine he halamic seeds,
which is implemen ed in F eesu e (Iglesias e al., 2018). I p o ides a p ecise, eliable and
au oma ic halamic ROI de ini ion, which is c i ical o he e y basis o a ep oducible and
eliable p o ocol o halamic ac og aphy. Mo eo e , he majo halamic ac s o AN and
MD we e econs uc ed om s a e-o -a DWI sequences acco ding o he physiological
desc ip ions om li e a u e and guidance om an expe ana omis . The ep oducibili y o he
econs uc ion p ocedu es is demons a ed in he cu en s udy. The econs uc ion p ocedu es
a e implemen ed in a con aine ized pipeline ha allows o he esea che s o econs uc he
same ac s wi h hei da a. In he nex sec ion I will discuss he ep oducibili y o he
p o ocol om wo di e en app oaches.
The compu a ional ep oducibili y measu es how eliable he p o ocol is o econs uc
he ac s o in e es ac oss mul iple compu a ions wi h he same da a and same compu a ion
pa ame e s. As expec ed, ac oss en sepa a e and independen compu a ions using he same
aw da a and p o ocol, he ep oducibili y is nea ly pe ec . Mos ac s ha e high
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p esen ed wi h an ins uc ion asking hem o p oduce noun wo ds wi h hei eyes closed. Pa icipan s
we e ins uc ed o p oduce one wo d du ing each silence o he spa se-sampling MRI p o ocol.
Simila ly, in he non-linguis ic mo o condi ion pa icipan s we e asked o p oduce 12 unin elligible
sounds du ing each silence b eak while ha ing hei eyes closed. In e e y ac i a ion block du ing
which he pa icipan s had hei eyes closed (i.e., audi o y linguis ic, audi o y non-linguis ic, mo o
linguis ic and mo o non-linguis ic condi ions), he e was a bell sound playing h ough he scanne
sound sys em o signal pa icipan s he end o he ac i a ion block and ha hey ha e o open hei
eyes again.
A o al o 168 Basque noun wo ds (e.g. pol sa,bag in English) we e selec ed o he isual
and audi o y asks. Hal o he wo ds we e p esen ed isually in he eading ask, and he emaining
hal we e p esen ed audi o ily in speech comp ehension asks, in which he audio was eco ded by a
emale Basque na i e speake . Sc ambled wo ds in he isual non-linguis ic ask we e designed by
c ea ing 10 × 10 pixel iles and mixing hem andomly, using he same wo d images om he eading
ask. The noise audios in he audi o y non-linguis ic ask we e c ea ed by andomly shi ing he
audi o y signal in ime domain, using he same speech wo ds used in he speech comp ehension ask.
The s imuli in isual and audi o y modali ies we e coun e balanced ac oss pa icipan s such ha he
isual wo d images and co esponding pe cep ual s imuli used in hal o he pa icipan s will be
p esen ed in audi o y, and ice e sa o audi o y modali y s imuli. In he linguis ic mo o o speech
wo d p oduc ion ask, pa icipan s we e ins uc ed o p oduce objec names exis ing in hei amilia
en i onmen , such as a able o a keyboa d in an o ice. In he non-linguis ic mo o , pa icipan s ha e
o p oduce unin elligible sounds ha he expe imen e showed o he pa icipan be o e unde going
MRI scanning. These unin elligible sounds (e.g., pala al click sound) had no seman ic meaning.
Be o e pa icipan s unde wen MRI scanning, hey p ac iced a beha io al e sion wi h he six main
condi ions o he MRI expe imen o amilia ize hem wi h he p ocedu e and he ins uc ions
p esen ed du ing he asks.
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8.1.3 MRI da a acquisi ion
Whole-b ain MRI da a acquisi ion was conduc ed on a 3-T Siemens PRISMA Fi whole-body
MRI scanne (Siemens Medical Solu ions) using a 64-channel whole-head coil. Func ional images
we e acqui ed wi h a spa se-sampling pa adigm (e ec i e epe i ion ime (TR) = 2.9 s, eal TR = 1.7
s) in a single g adien -echo echo-plana mul iband pulse sequence wi h he ollowing acquisi ion
pa ame e s: TE =35 ms; MB accele a ion ac o = 5; 65 axial slices wi h a 2.4 mm3 oxel esolu ion;
no in e -slice gap; lip angle = 56º; FoV = 210 mm; 1169 olumes in 7 uns. High- esolu ion
MPRAGE T1-weigh ed s uc u al images we e also collec ed o each pa icipan wi h he ollowing
pa ame e s: TR = 2530 ms; TE = 2.36 ms; lip angle = 7°; FoV = 256 mm; oxel esolu ion = 1 mm3;
176 slices. In o al 100 di usion weigh ed images we e acqui ed wi h he an e io o pos e io
phase-encoding di ec ion and 50 iso opically dis ibu ed di usion-encoding g adien di ec ions. The
100 di usion weigh ed images included 50 images wi h b- alues o 1000 s/mm2and 50 images wi h
b- alues o 2000 s/mm2. Twel e images wi h no di usion weigh ed (b- alues o 0 s/mm2) we e
ob ained o mo ion co ec ion and geome ical dis o ion co ec ion, which comp ised i e images
wi h he same phase-encoding di ec ion as he DWI images and se en images wi h he e e sed
phase-encoding di ec ion (pos e io o an e io ). Bo h DWIs and b0 images sha ed he ollowing
pa ame e s: TR = 3600 ms, TE = 73 ms, FA = 78°, oxel size = 2 mm iso opic, 72 slices wi h no gap
and a mul iband accele a ion ac o o 3.
8.1.4 MRI da a analysis
Fo s uc u al image analysis, he T1w images we e p ocessed using RTP-ana ROIs, which
in ol es p ocessing he subjec s’ ana omical T1w image wi h econ-all om eesu e
(h p://su e .nm .mgh.ha a d.edu) and ex ac ROIs. Fi s , F eesu e was used o pe o m
co ical/subco ical segmen a ion and pa cella ion. Nex , he halamic nuclei we e ob ained by unning
he halamic segmen a ion module implemen ed in F eesu e on a p obabilis ic a las buil based on
his ological and high- esolu ion ex i o MRI da a (Iglesias e al., 2018). Fo his s udy, we only
conside ed i s -o de elay nuclei as ROIs: he LGN, MGN and VLN. Fo unc ional and s uc u al
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connec i i y analyses, we also ex ac ed he co ical egions V1/V2, A1 and M1. To pa cella e he
isual co ex we an he Neu opy hy (Benson & Winawe , 2018;
h ps://gi hub.com/noahbenson/neu opy hy) ool on he F eesu e esul s. A combina ion o he
esul ing V1 and V2 ROIs was used o ou isual co ex ROI. A1 and M1 we e con e ed om he
human connec ome p ojec (HCP) a las (Glasse e al., 2016). To con e hem o indi idual subjec
space, we pe o med a non-linea egis a ion o a 1 mm3MNI empla e using Ad anced
No maliza ion Tools (ANTs, h p://s na a.gi hub.io/ANTs/).
Fo unc ional images p ep ocessing, we used SPM12 (Wellcome Cen e o Human
Imaging, London) p ep ocessing ou ines and analysis me hods. Images we e co ec ed o di e ences
in slice acquisi ion iming ac oss e e y unc ional scan and hen ealigned o mo ion co ec ion.
A e wa ds, each subjec ’s unc ional olumes we e smoo hed using a 2 mm ull-wid h hal -maximum
(FWHM) Gaussian ke nel. Mo ion pa ame e s we e ex ac ed om he ealignmen s ep o in o m a
olume epai p ocedu e (A Repai ; S an o d Psychia ic Neu oimaging Labo a o y) ha iden i ied
bad olumes on he basis o scan- o-scan mo emen (>0.5 mm) and signal luc ua ions in global
in ensi y (>1.3%) and co ec ed bad olumes ia in e pola ion om he nea es non- epai ed scans.
Fi e pa icipan s wi h mo e han 15% o-be-co ec ed ou lie unc ional olumes we e excluded. A e
olume epai , unc ional olumes we e sepa a ely co egis e ed in wo di e en ways: 1) o MNI
space in o de o conduc whole-b ain con as s in no malized MNI space a he g oup le el; 2) o
high- esolu ion ana omical T1 images and esliced om he o iginal 2.4 mm3 unc ional oxel
dimensions o 1 mm3 oxels in ana omical T1 space o ROI analysis and unc ional connec i i y.
Finally, ime se ies we e empo ally il e ed o elimina e con amina ion om slow equency d i
(high-pass il e : 128s).
S a is ical analyses we e pe o med on each subjec da a om bo h he MNI space and
indi idual space using he gene al linea model (GLM). A se ies o impulses con ol ed wi h a
canonical hemodynamic esponse unc ion (HRF) we e used o model he MRI ime se ies da a. The
six main expe imen al condi ions in ou design (i.e., 2 Task X 3 Modaly) we e modeled as epochs
om he onse o he i s ial wi hin each block un il he las ial wi hin he block, esul ing in 20.4s
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(12 x 1.7) pe iods. These unc ions we e used as co a ia es in he GLM. The mo ion pa ame e s o
ansla ion (i.e., x, y, z) and o a ion (i.e., yaw, pi ch, oll) we e used as co a ia es o non-in e es in
he GLM. SPM12 FAST was used o empo al au oco ela ion modeling in his GLM due o i s
op imal pe o mance in e ms o emo ing esidual au oco ela ed noise in i s -le el analyses
(Olszowy e al., 2019). The leas -squa es pa ame e es ima es o he heigh o he bes - i ing
canonical HRF o each condi ion we e used in pai wise con as s. A he g oup le el, whole-b ain
con as s be ween condi ions we e compu ed in MNI space by pe o ming pai ed - es s on hese
images om di e en condi ions, ea ing pa icipan s as a andom e ec . Th ee whole-b ain con as s
we e conduc ed: Visual asks - (Audi o y + Mo o asks), Audi o y asks - (Visual + Mo o asks) and
Mo o asks - (Visual + Audi o y asks). These h ee con as s we e selec ed o examine he
modali y-speci ic egions a he whole-b ain le el. Ou s anda d s a is ical h eshold o whole-b ain
maps was a Family Wise E o (FWE) se o p< .05 a he oxel le el.
Indi idual ROI analysis was pe o med on GLM esul s om he indi idual space wi h he
MARSBAR oolbox o use wi h SPM12. Gi en ha his s udy ocused on he in ol emen o
i s -o de halamic nuclei in language p ocessing, he LGN, MGN and VLN we e selec ed om bo h
hemisphe es as ROIs. Then, in line wi h ou main expe imen al design, we ex ac ed pa ame e
es ima es (i.e., scaled % signal change alues) o each single egion and subjec indi idually and used
hem as dependen a iables in 2 (Task: linguis ic and non-linguis ic) by 3 (Modali y: isual, audi o y
and mo o ) epea ed measu es ANOVAs. As we we e in e es ed in whe he he e is a dissocia ion o
halamic in ol emen be ween linguis ic and non-linguis ic asks, we es ed he le and igh halamic
ROIs sepa a ely and compa ed hei in ol emen be ween linguis ic and non-linguis ic asks in hei
co esponding modali y based on ou hypo heses.
Func ional connec i i y was examined be ween each i s -o de halamic nuclei o in e es
(LGN, MGN and VLN) and he si e o co ical e mina ion o he co esponding senso imo o
pa hways (V1/V2, A1 and M1). The unc ional connec i i y analyses we e conduc ed using he
be a-se ies co ela ion me hod (Rissman e al., 2004), implemen ed in SPM12 wi h cus om Ma lab
sc ip s. The canonical HRF in SPM was i o each ial om each expe imen al condi ion and he
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esul ing pa ame e es ima es (i.e., be a alues) we e so ed acco ding o he s udy condi ions o
p oduce a condi ion-speci ic be a se ies o each oxel. Pai wise unc ional connec i i y analysis
be ween halamic nuclei and co ical egions we e conduc ed a he indi idual-subjec le el o each
ask in he co esponding senso imo o modali y (LGN-V1V2 in isual, MGN-A1 in audi o y and
VLN-M1 in mo o ). The be a-se ies co ela ion alues ( alues) we e ans o med o Fishe ’s z alues
by applying an a c hype bolic angen ans o m (Fishe , 1921) a he subjec le el o each pai o
ROIs and each expe imen al condi ion. Since he co ela ion coe icien is inhe en ly es ic ed o
ange om −1 o +1, his ans o ma ion ensu ed he null hypo hesis sampling dis ibu ion app oached
ha o he no mal dis ibu ion. To assess he signi icance o he co ela ion indings a he g oup le el,
he z- ans o med co ela ion o he indi idual subjec s we e compa ed agains ze o a g oup le el.
S uc u al connec i i y analysis was conduc ed by using he ac og aphy p o ocol p oposed
in S udy 1 o econs uc he i s -o de elay halamic ac s on he DWI da a collec ed in his s udy.
Th ee pai s o i s o de halamic ac s we e econs uc ed: bila e al OR, AR and MR. The FA p o ile
o each ac om each subjec was ob ained using RTP-pipeline. Mo e de ails can be ound in chap e
6, sec ion 6.1.4. The mean o he FA p o ile was calcula ed as he index by a e aging he 100 FA
alues in he FA p o ile o each ac and each subjec . Fo each i s -o de halamoco ical pa hway
o in e es , o examine he associa ions be ween he s uc u al connec i i y and unc ional
connec i i y, co ela ion analyses we e conduc ed be ween he FA alues om s uc u al connec i i y
and z alues om unc ional connec i i y o he co esponding modali y ask.
8.2 Resul s
8.2.1 Whole-b ain con as s
To iden i y b ain egions associa ed wi h p ocessing speci ic modali ies ac oss all pa icipan s
and linguis ic-non linguis ic asks, we compu ed h ee whole-b ain con as s: isual > (audi o y +
mo o ); audi o y > ( isual + mo o ) and mo o > ( isual + audi o y). These con as s e ealed he
in ol emen o he i s -o de halamic elay nuclei and he p ima y co esponding senso imo o
co ical egions in modali y speci ic asks. The isual > (audi o y + mo o ) con as showed inc eased
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ac i a ion in bila e al LGN and p ima y and seconda y isual co ex in isual asks compa ed o asks
in o he wo modali ies (Figu e 8.1A). Simila ly, he audi o y > ( isual + mo o ) con as e ealed he
audi o y halamic nuclei bila e al MGN and A1 (Figu e 8.1B). The mo o > ( isual + audi o y)
con as showed highe in ol emen o he mo o halamic nuclei (bila e al VLN) and M1. Ve mis III
in he ce ebellum also showed speci ic in ol emen in he mo o con as (Figu e 8.1C). Using a mask
o he en i e halamus wi h hese same con as s e ealed he speci ici y o each o hem showing
unc ional ac i a ion o he expec ed halamic nuclei: LGN o isual > (audi o y + mo o ) con as ;
MGN o audi o y > ( isual + mo o ) con as ; and, VLN ex ended o mediodo sal nucleus o he
mo o > ( isual + audi o y) con as (Figu e 8.1D)
Figu e 8.1.A) B ain sec ions showing ac i a ions o he isual > audi o y + mo o whole-b ain
con as ac oss all subjec s. B) B ain sec ions showing ac i a ions o he audi o y > isual + mo o
whole-b ain con as ac oss all subjec s. C) B ain sec ions showing ac i a ions o he mo o > isual
+ audi o y whole-b ain con as ac oss all subjec s. D) B ain sec ions showing he same h ee
con as s using a mask o he en i e halamus (g een = isual > audi o y + mo o , ed = audi o y >
isual + mo o , blue = mo o > isual + audi o y). All b ain sec ions p esen ed he e a e in MNI space.
The s a is ical h eshold was p < 0.05 FWE- co ec ed a he oxel le el.
8.2.2 ROI esul s
ROI analyses we e conduc ed o cha ac e ize he ac i a ion p o ile o he h ee halamic ROIs
(LGN, MGN and VLN) bila e ally o he main expe imen al asks in h ee pe cep ual modali ies. We
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ex ac ed MRI pa ame e es ima es om hese six ROIs and conduc ed hypo hesis-d i en analyses
based on planned compa isons be ween condi ions.
LGN. Resul s om bila e al LGN a e p esen ed in Figu e 8.2A. A epea ed measu es analysis
o a iance (ANOVA) was conduc ed o he LGN pa ame e s es ima es om bo h hemisphe e
sepa a ely showed a main e ec o Modali y, o le LGN [ F(2,228) = 26.39; p< 0.001, ηp
2= 0.19]
and igh LGN [F(2,228) = 23.30; p< 0.001, ηp
2= 0.17], wi h s onge ac i a ion o he isual asks
ela i e o he audi o y and mo o asks. Planned compa isons e ealed ha LGN showed highe
ac i a ion in bo h linguis ic and non-linguis ic asks in isual modali y compa ed o hei coun e pa s
in audi o y and mo o modali ies, wi h he excep ion o he igh LGN ac i a ion o non-linguis ic
asks ha ing no di e ence be ween isual and mo o modali ies. Mo e impo an ly, since we a e
in e es ed in possibly di e en in ol emen o he i s -o de halamic ROIs be ween linguis ic and
non-linguis ic asks in he co esponding modali y, simple-e ec s compa isons be ween linguis ic and
non-linguis ic asks in isual modali y we e conduc ed o bo h le and igh LGN. The esul s
e ealed ha he le LGN showed highe in ol emen o isual linguis ic ask compa ed o isual
non-linguis ic ask (p< 0.05), while his di e ence was no obse ed o igh LGN (p= 0.17).
MGN. The epea ed measu es ANOVA conduc ed o bila e al MGN showed he main e ec
o Modali y, o le MGN, [F(2,228) = 3.77; p< 0.05, ηp
2= 0.03]; o igh MGN, [F(2,228) = 5.27;
p< 0.01, ηp
2= 0.04], e ealing s onge ac i a ion o audi o y asks ela i e o he mo o asks (Figu e
8.2B). Simple e ec s compa isons e ealed ha le MGN showed highe ac i a ion o linguis ic
asks in he audi o y modali y compa ed o bo h isual and mo o modali ies. Simila pa e ns we e
also ound in igh MGN, along wi h highe ac i a ion in non-linguis ic asks in he audi o y modali y
han in he isual modali y. Finally, he le MGN showed s onge engagemen in he audi o y
modali y o he linguis ic ask han o he non-linguis ic ask (p< 0.01), and his e ec was no
obse ed o he igh MGN (p= 0.79).
VLN. As expec ed, same ANOVA conduc ed o bila e al VLN showed he main e ec o
Modali y o le VLN, [F(2,228) = 48.8; p< 0.001, ηp
2= 0.30] and igh VLN, [F(2,228) = 49.3; p<
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0.001, ηp
2= 0.30], wi h s onge ac i a ion o mo o asks ela i e o he isual and audi o y asks
(Figu e 8.2C). Simple-e ec s compa isons conduc ed sepa a ely o linguis ic and non-linguis ic
e ealed highe ac i a ion in bo h le and igh VLN in he mo o modali y compa ed o bo h isual
and audi o y modali ies. Di e en om he esul s o le and igh LGN and MGN, compa ison in he
VLN be ween ac i a ion o linguis ic and non-linguis ic asks in mo o modali y did no e eal
s a is ically signi ican di e ences (ps > 0.12).
Figu e 8.2. ROI analyses o h ee i s -o de elay halamic nuclei: A) bila e al LGN; B) bila e al
MGN and C) bila e al VLN. Ba g aphs show a e aged pa ame e es ima es (% signal change) o
each halamic nuclei o linguis ic and non-linguis ic asks in he h ee modali ies: isual, audi o y and
mo o . *p< 0.05, **p< 0.01.
8.2.3 Func ional connec i i y esul s
Pai wise unc ional connec i i y analyses o h ee i s -o de elay halamic pa hways o he
linguis ic and non-linguis ic asks o he co esponding modali ies we e examined: LGN-V1/V2 isual
pa hway o linguis ic and non-linguis ic asks in he isual modali y; MGN-A1 audi o y pa hway o
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linguis ic and non-linguis ic asks in he audi o y modali y; and, VLN-M1 mo o pa hway o
linguis ic and non-linguis ic asks in he mo o modali y. To examine he signi icance o he
co ela ion indings a he g oup le el, he z- ans o med co ela ion alues o he indi idual subjec s
we e compa ed agains ze o. The esul s e ealed ha p edic ed unc ional connec ions be ween
egions based on known neu oana omy we e s a is ically signi ican o bo h linguis ic (Figu e 8.3A)
and non-linguis ic asks (Figu e 8.3B). Fo he isual pa hway, unc ional coupling o LGN wi h
V1/V2 o linguis ic asks (z=0.56, p< 0.001 o le and z=0.58, p< 0.001 o igh ) and
non-linguis ic ask (z=0.52, p< 0.001 o le ; z=0.51, p< 0.001 o igh ). Simila ly signi ican
unc ional coac i a ion was ound be ween MGN and A1 o audi o y linguis ic and non-linguis ic
asks, as well as be ween he VLN and M1 o mo o linguis ic and non-linguis ic asks. Simple-e ec
analyses we e conduc ed o examine i he e was any s a is ically signi ican di e ence be ween he
unc ional connec i i y in linguis ic and non-linguis ic asks o each o hese i s -o de halamic
pa hways, wi h none o hese compa isons e ealing s a is ically signi ican di e ences (ps ≥ 0.54).
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Figu e 8.3. Func ional connec i i y analyses o h ee i s -o de elay halamic pa hways in A)
linguis ic and B) non-linguis ic asks o each o he co esponding modali ies. Lines wi h an a ow
ep esen he connec ion o he i s -o de halamic elay nuclei wi h he co ical p ima y senso imo o
egions. Thalamic nuclei, co ical egions and he lines a e colo ed as a unc ion o senso imo o
modali y: g een indica es isual modali y, ed indica es audi o y modali y and blue indica es mo o
modali y. The alues along he a ow lines a e he Fishe - ans o med z alues. All he epo ed z
alues a e signi ican agains ze o wi h p< 0.001 a e Bon e oni FWE co ec ion o mul iple
compa isons.
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9 Gene al Discussion
The cu en disse a ion is ocused on he human halamus s uc u e and unc ion. In he i s
wo empi ical s udies, I examined he whi e-ma e ibe connec ions be ween he halamus and
co ical s uc u es and subco ical s uc u es, mo e speci ically he i s -o de halamic ac s o LGN,
MGN and VLN in S udy 1, and he highe -o de halamic ac s o AN and MD in S udy 2. The
p o ocols o he econs uc ion o hose ac s om DWI da a ha e p o ed o ha e high
ep oducibili y, and a e made public o he scien i ic communi y o ep oducibly econs uc he same
ac s in hei own da a. Mo eo e , as pionee ing wo k, in s udy 3 we examined he unc ion o human
halamus in language unc ions, mo e speci ically, he in ol emen o he i s -o de halamic nuclei
in some o he main human language sys ems. We showed ha he LGN and MGN ac i a ion is
modula ed as a unc ion o he s imuli ype. These esul s sugges ha he halamus is in ol ed in
human language p ocessing and unde sco e he need o mo e sys ema ic s udies o he in ol emen
o he halamus in language p ocessing.
In S udy 1 o his doc o al disse a ion, we ocused on he i s -o de halamic whi e-ma e
ac s, namely, he OR, AR, MR and DT. These halamic ac s o in e es a e c i ical s uc u es in
elaying in o ma ion om pe iphe y o ce ebellum o co ical s uc u es. They a e con as ed o
highe -o de halamic whi e-ma e ac s, such as hose epo ed in S udy 2, which a e belie ed o
se e as links in co ico- halamo-co ical pa hways ha con inue he in o ma ion low be ween
co ical s uc u es (Ramcha an e al., 2005). The ac og aphy echnology allows o isualize whi e
ma e ibe s in i o and o e s he oppo uni y o ex ac ing mic os uc u al in o ma ion o pe o m
quan i a i e analyses. On he o he hand, i is also well known ha p obabilis ic ac og aphy can
come wi h alse posi i e o nega i e esul s due o da a noise, pa ial olume e ec s, and complex
ana omical p ope ies such as c ossing ibe s (Pie paoli e al., 2001; Wiegell e al., 2000). In ac , he
ep oducibili y o ac og aphy has been unde discussion o a long ime, bu he e a e no gene al
answe s o how o imp o e ep oducibili y o ac og aphy, as i depends on he MRI sequences,
acking pa ame e s and he speci ic ac s unde in es iga ion. P e ious s udies ha e used in i o
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ac og aphy on DWI da a o econs uc hese i s -o de halamic ac s, bu only a ew o hem ha e
epo ed ep oducibili y measu es abou he econs uc ion o he speci ic whi e-ma e ac s.
Di e en om p e ious wo k, his s udy es ed he i s -o de halamic ac s in he same
s udy using simila me hods and econs uc ion p ocedu es ac oss all hem. Each ac has gone
h ough mul iple pa ame e i e a ions o ob ain he mos op imal ajec o y ha aligns wi h he ex an
neu oana omical knowledge. Mo e impo an ly, we speci ically in es iga ed in a la ge da ase he
eliabili y o he econs uc ion p o ocol in e ms o bo h compu a ional and es - e es ep oducibili y.
The ep oducibili y was measu ed a bo h mic os uc u al and mac os uc u al le els o each ac .
Resul s om S udy 1 e ealed ha he p oposed p o ocol could eliably econs uc hese i s -o de
halamic ac s, and ob ain ep oducible mic os uc u al and mac os uc u al measu emen s ha can
e lec he cha ac e is ics o hese whi e-ma e bundles. This p o ocol has been implemen ed in a
docke con aine and made publicly a ailable, so i can be easily used by o he esea che s in he
communi y. This unique ool allows esea ch o es hypo heses as o whe he any o hese speci ic
i s -o de halamic ac s a e ela ed wi h cogni i e unc ions o diseases o in e es , wi h he accu acy
and ep oducibili y o he ac econs uc ion gua an eed.
The S udy 2 ex ends he a ionale o S udy 1 o highe -o de halamic whi e-ma e bundles.
Con en ionally, he halamic elays can be classi ied in o i s - and second (o highe )-o de .
Fi s -o de halamic ac s connec hese i s -o de halamic elays wi h hei co esponding
senso imo o co ical a eas. In con as , highe -o de halamic ac s elay in o ma ion be ween
co ical a eas, such as he whi e-ma e ac s connec ing he MD and PFC, ep esen ing a
co ico- halamo-co ical ci cui (Mi chell, 2015; She man, 2017). The highe -o de halamic ac s
o en ha e mo e s uc u al and unc ional complexi y and a e a om being ully unde s ood in
humans. Fo example, he MD has ex ensi e connec ions wi h p ac ically he whole PFC, which is
in ol ed in nume ous cogni i e unc ions, such as wo king memo y, a en ion, and decision making
(Cla k e al., 2010). Each sub egion o he PFC has ypically speci ic oles in cogni ion. These PFC
sub egions ecei e a e en ibe s om he MD in he halamus ia he an e io halamic adia ion. In
S udy 2, he a e en ibe ac s om AN and MD we e econs uc ed om DWI da a and he
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co esponding compu a ional and es - e es ep oducibili y was examined. Ou p o ocol included 42
pai s o le and igh hemisphe ic ac s, wi h 3 ac s being examined o he AN and 39 ac s being
in es iga ed o he MD). The ep oducibili y esul s showed ha in gene al hese ac s can be
eliably econs uc ed om DWI da a wi h he p oposed p o ocol. The e we e speci ic ac s showing
ela i ely lowe ep oducibili y, o example he AN ela ed ac s and ac s connec ing MD wi h
o bi al on al co ex. To explo e he associa ions be ween he ep oducibili y and he possible
in luen ial ac o s on ep oducibili y, a pos -hoc analysis was conduc ed. The esul s un eiled h ee
ac o s s ongly associa ed wi h ep oducibili y o speci ic ac s. Fo example, noise in he di usion
da a has s ong nega i e co ela ion wi h he es - e es ep oducibili y. Also, he compu a ional
ep oducibili y is linked wi h he s eamline leng h and coun . I one ac has longe o less amoun o
s eamlines, i migh lead o ela i ely lowe compu a ional ep oducibili y. To he bes o ou
knowledge, no s udy has in es iga ed so a he compu a ional ep oducibili y o ac og aphy
me hods, as esea che s assume ha he same da a and same me hods mos ly lead o he same esul s.
These insigh s a e aluable as i should p omo e he sys ema ic in es iga ion on compu a ional
ep oducibili y o a og aphy.
In he hi d and inal empi ical s udy, we in es iga ed he in ol emen o he h ee i s -o de
halamic nuclei (LGN, MGN and VLN) in h ee main human language sys ems: eading, speech
comp ehension and p oduc ion. The esul s e ealed s onge engagemen o he LGN, MGN and
VLN o bo h linguis ic and non-linguis ic asks in hei co esponding modali ies. Mo e impo an ly,
we ound s onge ac i a ion o linguis ic e sus non-linguis ic s imuli in eading and speech
comp ehension o LGN and MGN, espec i ely. Fo example, he LGN showed highe ac i a ion o
eading wo ds han o seeing sc ambled pixels ha a e pe cep ually equal o isual wo ds. Ve y ew
s udies ha e in es iga ed he subco ical con ibu ions on high-le el cogni i e unc ions such as
language (Pa izi, 2009), wi h mos o he s udies o da e examining he neu obiology o language
being ocused on he co ical a eas (F iede ici, 2002; Hickok & Poeppel, 2007; P ice, 2000). To he
bes o ou knowledge, his s udy is he i s o in es iga e he h ee modali ies in ela ion o he
in ol emen o i s -o de halamic elays in hei espec i e human language sys ems, and p o ed he
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associa ions be ween linguis ic and non-linguis ic asks in he LGN and MGN. Ou indings showed
ha he esponses in LGN and MGN du ing he p ocessing o isual and audi o y s imuli a e ask
dependen , which implies he exis ence o a eedback mechanism ha suppo s he ecogni ion o
isual and audi o y in o ma ion a he le el o senso y halamic nuclei. Gi en ha his associa ion has
been ound in he le halamic nuclei bu no on he igh , we pos ula e ha his eedback mechanism
can be, e y possibly, language-speci ic.
The s udies in he cu en disse a ion ha e some limi a ions. The i s wo s udies ha e es ed
he ep oducibili y o he p oposed p o ocol using a s a e-o -a DWI sequence, which includes, o
example, mul iband and mul i-shell echniques. Al hough we canno gua an ee ha he exac same
compu a ional and es - e es ep oducibili y will be ob ained wi h di e en DWI acquisi ion
p o ocols, we do no expec ha he ep oducibili y p o iles desc ibed he e o hese halamoco ical
p ojec ions may change d ama ically when using o he DWI p o ocols widely used in he pas , such as
monoband o single-shell da a. Despi e he ac ha in his s udy we decided o go wi h s a e-o - he-a
DWI sequences, he cu en p o ocols can be easily adap ed o di e en sequences. Fu he mo e, he
ep oducibili y o he p oposed p o ocol was measu ed on DWI acqui ed om a heal hy popula ion. I
would be also ele an in he u u e o examine he ep oducibili y o econs uc he halamic ac s
om DWI da a in clinical popula ions. In S udy 2, he ep oducibili y has been linked o some ac o s
o he DWI da a o he neu oana omical cha ac e is ics o speci ic ac s. Th ee ac o s we e unde
in es iga ion in his wo k, and in u u e s udies o he possible ac o s ha could be associa ed wi h
ep oducibili y, such as pa hological ea u es o in an popula ions, should be examined. In he
ask-based MRI S udy 3, he expe imen al design examined he ac i a ion p o ile o he i s -o de
halamic nuclei o linguis ic and o non-linguis ic asks a ending o h ee modali ies ( isual,
audi o y, mo o ). This s udy pa es he oad o ollow-up analyses and expe imen s. Fi s , an open
ques ion om his s udy is he ask-dependency on i s -o de halamic nuclei in hei
language-speci ic modali y. We hypo hesized ha his is e y likely language-speci ic as we obse ed
di e ences as a unc ion o he linguis ic na u e o he s imuli in LGN and MGN only in he le
hemisphe e, which is he dominan hemisphe e o language unc ion. We plan o u he explo e hese
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di e ences and y o answe his ques ion. Second, he ole ha ask modula ion on i s -o de
halamic nuclei plays in high-le el cogni i e unc ions, such as language, is s ill no clea . Fu u e
s udies including mo e sys ema ic manipula ions migh shed ligh on he mechanisms unde lying ask
modula ion on i s -o de halamic nuclei.
In sum, he cu en disse a ion success ully econs uc ed i s -o de and highe -o de
halamic whi e-ma e ac s om DWI da a, and has p o ed high ep oducibili y o he econs uc ion
p o ocol. This p o ocol could bene i he ac og aphy communi y o be e unde s and he s uc u al
connec i i y o he halamus wi h co ical and subco ical s uc u es and acili a e he esea ch on
halamoco ical pa hways in humans. We also ound e idence o di e ences in he p ocessing o
linguis ic and nonlinguis ic s imuli in i s -o de halamic nuclei h ough a ask-based MRI s udy.
These esul s sugges ha he i s -o de halamic nuclei play oles in human language ha a e beyond
elaying senso y in o ma ion om pe iphe y o ce eb al co ex. These indings a e impo an o push
o wa d ou unde s anding on he ole o subco ical s uc u es, such as he halamus, in human
language unc ions, and o u ge a e isi a ion o exis ing language models aking he halamus in o
conside a ion.
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