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MRI slike naopako u FSLVIEW-u

MRI slike naopako u FSLVIEW-u


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Vizualiziram strukturne podatke u FSLVIEW-u, ali sagitalna i koronalna ravnina su naopačke / nisu u konvencionalnoj orijentaciji, tj. Rotirane za 90º i 180º. Razumijem da je ovaj pogled naopačke posljedica slika koje se prikazuju redoslijedom na koji su pohranjene na disku, ali kako moram anatomski definirati ROI na svojoj strukturi, bilo bi prikladnije da je mozak ispravno orijentiran.

Imate li ideju kako rotirati ova dva pogleda? Stvarne orijentacijske oznake (S/I, A/P) su ispravne. Hvala!


Ovdje sam potpuno izvan moje lige, ali sudeći prema brzom Google pretraživanju, čini se da je ovo čest problem u FSL -u. Rekli ste da ovaj video na YouTubeu pomaže, pa je to dobro ... ali budući da koristi applecript, pretpostavljam da mu je potreban Mac OS.

Kažete da Freesurferov mri_convert radi u Linuxu pomoću ovog koda:

mri_convert --in_orientation [trenutni redoslijed pohrane datoteke, npr. LAS] -out_orientation [željeni nalog za pohranu]  

Jednostavnofslreorient2stdće orijentirati slike onako kako biste očekivali.

S https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Objašnjeno:

fslreorient2std - ovo je jednostavan alat dizajniran za preorijentaciju slike tako da odgovara orijentaciji standardnih slika predloška (MNI152) tako da se pojavljuju "na isti način" u FSLView -u.


Istraživači proučavaju kako mozak obrađuje slike

Poslane fotografije Studentica psihologije Jackie Ewald priprema se za pregled slika tijekom eksperimenta u novom laboratoriju kognitivne neuroznanosti.

FAIRBORN — Učenik nosi crvenu i plavu kapicu lubanje optočenu elektrodama. Sjedne pred monitor unutar zatvorene komore dok joj pred očima bljesnu slike ljudskih lica, motocikala i crkvenih svetišta. Neposredno vani, istraživači su zaključani u njezinim moždanim valovima, koji osvjetljavaju njihova računala.

Stvari bruje u novom laboratoriju kognitivne neuroznanosti Sveučilišta Wright, najsuvremenijem objektu koji obećava otključavanje misterija načina na koji ljudski mozak tumači vizualne slike. Potencijalne primjene kreću se od liječenja disleksije i poremećaja hiperaktivnosti s deficitom pažnje do obuke vojnih pilota i operatera bespilotnih letjelica.

Kognitivna neuroznanost je akademsko područje koje se bavi odnosom mozga i ponašanja kroz percepciju, pamćenje i pažnju.

Laboratorij, koji se nalazi na četvrtom katu Fawcett Halla, sagradio je docent psihologije i kognitivni neuroznanstvenik Assaf Harel, koji proučava kako mozak koristi vizualne slike za izvlačenje informacija o okolišu i osmišljavanje svijeta.

“Mi smo vizualne životinje, "#rekao je Harel.

Harel je doktorirao kognitivnu neuropsihologiju na Hebrejskom sveučilištu u Jeruzalemu (Izrael), gdje je studirao stručnost u prepoznavanju vizualnih objekata. Radio je postdoktorski rad na Nacionalnom institutu za mentalno zdravlje, proučavajući vizualno prepoznavanje pomoću funkcionalnih, najsuvremenijih tehnika MRI analize. Pridružio se fakultetu u Wright Stateu 2014. godine.

Harel izravno snima mjerenja mozga, što ima veliku prednost u tome što se ne mora oslanjati na usmene izvještaje ispitanika, što može biti pristrano.

“Izvlačenje značenja iz okoline vrlo je težak računalni problem,##rekao je. “Ako imate računalo koje pokušava razumjeti svijet — pokušavajući steći bogato iskustvo koje imamo i napraviti finu diskriminaciju koju možemo učiniti##to će#8217 tužno propasti. ”

No računalo se može programirati da se usredotoči na to kako vizualne slike utječu na moždane valove. Elektroencefalogram (EEG) koristi se za praćenje i snimanje uzoraka moždanih valova kroz elektrode postavljene na tjemenu koje šalju signale računalu.

Komora u laboratoriju obložena je metalom dizajniranim za filtriranje bilo kakvih električnih frekvencija ili smetnji koje bi mogle ometati snimanje podataka. Odzivi mozga mjereni strujom kapice sa 64 elektrode kroz pojačalo i fiberoptičku vezu izlaze na banku računala koja prikazuju EEG valove.

“To je neinvazivno i izravno mjerenje moždane aktivnosti, rekao je Harel. “Ima sve velike prednosti proučavanja odnosa između mozga i ponašanja. ”

Istraživači mogu mjeriti kako mozak različito reagira na slike lica i objekata bez lica, koliko brzo mozak prepoznaje slike i kako mozak prepoznaje velike prostorne slike kao što su planinski lanci i pustinje. Svodi se na otkrivanje gdje i kada u mozgu dolazi do različitih vizualnih procesa.

“Ono što mislim da ćemo otkriti je da je vizija vrlo interaktivan proces — gdje vas ono što mislite, što očekujete, što trebate učiniti s vizualnim informacijama zapravo ograničava i dovodi do određenih tumačenja, ” Harel rekao je. “Ne vidite ’ očima. Mi nismo fotoaparati. Mi smo zapravo kognitivni agenti i zapravo vidimo svojim mozgom. ”

Slike ne uključuju samo lica, već krajolike poput planina, plaža i šuma te predmete poput motocikala i rolera. Neke su slike okrenute naglavačke.

“Stvari imaju određenu strukturu, a ako ih obrnete, petljate se s tom strukturom, "rekao je Harel. “Teže vam biti teže zapamtiti lice i reagirati na njega. Pitanje je uzrokuje li to razliku u moždanim valovima. ”

Rezultati istraživanja mogli bi se potencijalno koristiti za rješavanje deficita pažnje.

“Zaista biste se mogli usredotočiti i usredotočiti na određene markere pažnje, a zatim pitati koji su to aspekti narušeni, "#rekao je. “Mogli biste ga koristiti i kao alat za vježbanje i pojačati pozornost. ”

Na primjer, operater UAV -a čije kognitivne sposobnosti nagrizaju dugi sati može se zatražiti da se preusmjeri ako bi EEG otkrio označavanje pažnje.

Novi laboratorij dijelit će se sa bilo kojim fakultetom ili odjelom Wright State -a, poput inženjeringa, znanosti, matematike i medicine koji trebaju EEG podatke za svoja istraživanja.

Kathrin Engisch, privremena dekanica Prirodoslovno -matematičkog fakulteta, nazvala je laboratorij “fantastičnom prilikom ” za cijelo sveučilište.

“To je#8217 divno za broj učenika koji će moći vidjeti ovako nešto,#rekao je Engisch. “Nikada prije nismo imali ništa slično. To nas razlikuje od većine sveučilišta oko nas. Ponosni smo na to. ”

Poslane fotografije Studentica psihologije Jackie Ewald priprema se za pregled slika tijekom eksperimenta u novom laboratoriju kognitivne neuroznanosti.


U čemu je velika stvar? Zašto je zapravo toliko važno da se Izbjegavajte koristite RTG i MRI za dijagnosticiranje bolova u leđima?

Nitko ne želi lažnu uzbunu, ali u čemu je problem s nekoliko dijagnostičkih crvenih haringa? To je gori problem nego što mislite (na barem dva) velika načina ...

Prvo, RTG i MRI iskreno su uplašili ljude! Snažno pojačava ideju da bi nešto moglo biti slomljeno ili krivo, uobičajena i krajnje pogrešna ideja o bolovima u leđima (i mnogim, mnogim drugim problemima8). I ništa nije gore od bolova u leđima od straha. Strah je "ubojica leđa". 9

Drugo, snimanje često samo ne uspijeva razjasniti situaciju ili zapravo zamućuje dijagnostičke vode. Brdo znanstvenih dokaza jasno ukazuje na to da bol u leđima zaista korelira, stvarno loše s ovim rezultatima ispitivanja. Mnogi ljudi bez boli imaju sve vrste "grešaka" na leđima, i obrnuto. Mnogi problemi otkriveni skeniranjem koji izgledaju kao "očiti" problemi nisu. Na primjer, ne samo da se barem polovica "skliznutih" diskova sama vraća tamo gdje im je mjesto, 10 već je to zapravo najgori one koje su najvjerojatnije kako bi se sami riješili.11 I tako se dijagnoza i liječenje često okreću u pogrešnom smjeru. Ovo je glavni dio razloga zašto postoje tako zastrašujuće statistike o ekonomskim troškovima bolova u leđima.

Postoje iznimke - ponekad snimanje pronađe nešto važno - i zato ovi testovi mogu biti prikladni za neke vrste teških i trajnih bolova u donjem dijelu leđa. Ali to je samo općenito loš način da pokušate shvatiti zašto vas bole leđa.

Gledajući gdje je (visokotehnološko) svjetlo

Prekomjerna upotreba magnetske rezonancije klasična je pogreška "efekta uličnog svjetla": fokusiranje samo tamo gdje je svjetlo dobro. MRI olakšava medicinsko isticanje onoga što se čini važnim kod bolova u leđima, ali stanje kralježnice je jedno, a bolovi u leđima drugo. Uzmite u obzir rezultate velikog Brinjikjijevog pregleda za 2015. godinu et al: znakovi degeneracije prisutni su u vrlo visokom postotku zdravih ljudi bez ikakvih problema. “Mnoge degenerativne značajke temeljene na snimanju vjerojatno su dio normalnog starenja i nisu povezane s boli.” 12

Dakle, bodlje obično izgledaju gore nego što jesu, a naizgled zastrašujuću degeneraciju kralježnice MRI otkriva u visokim postocima simptomatičnih osoba. Dijagnoza se temelji uglavnom na takvim nalazima često zavaravajuće.


Čitanje misli: Novi laboratorij izoštrava slike mozga

Novi integrirani laboratorij za snimanje pružit će znanstvenicima neusporediv pogled na funkciju mozga, što će pomoći donijeti visoko informativnu boju i oblik nekada eteričnim pojmovima o prirodi emocija, učenju i mentalnim poremećajima.

WM: 10 milijuna dolara Keck Laboratorij za funkcionalno snimanje mozga i ponašanje, koji se otvara u travnju u UW-Madisonu, jedinstven je u svijetu po fokusiranju najnovijih tehnologija snimanja, svaka s različitim sposobnostima, na pitanja vezana za aktivnost i ponašanje mozga. Povezano s Waisman centrom i Medicinskim fakultetom & HealthEmotions Research Institute, novi laboratorij nadovezuje se na više od desetljeća istraživanja emocija na sveučilištu.

“Istraživanje slika mozga okreće područja psihologije i psihijatrije naglavačke, ” kaže direktor Kecka Richard Davidson, profesor obje discipline. “Prošli smo od proricanja teorija o funkcioniranju mozga do doslovnog viđenja funkcije mozga u trodimenzionalnoj boji na izvrsno detaljnim slikama aktivacijskih uzoraka živog mozga na djelu. ”

Objekt od 17.000 četvornih metara sastoji se od prvog kata velikog dodatka Waisman Centru, nacionalnom centru za proučavanje razvoja i razvojnih teškoća. Laboratorij će uključivati ​​kućne urede za više desetaka nastavnika i osoblja, a bit će i resurs za više od 50 fakulteta neuroznanosti diljem kampusa.

Središnji dio objekta bit će dva stroja s najsofisticiranijom, neinvazivnom medicinskom tehnologijom snimanja- funkcionalnom magnetskom rezonancijom (fMRI) i pozitronskom emisijskom tomografijom (PET). Dvije tehnologije imaju snage koje, kada se kombiniraju, daju znanstvenicima bogatije i korisnije informacije nego ikad prije, kaže Davidson. Na primjer, fMRI prikazuje različite strukture mozga dok radi, dok PET može pomoći u praćenju biokemijske aktivnosti u mozgu. Koristit će se druge tehnologije koje pomažu u utvrđivanju električne aktivnosti mozga.

“Očekujemo da će nam ovaj novi laboratorij uvelike pomoći u identificiranju moždanih mehanizama uključenih u emocije koje su odgovorne za bolest, ali i za dobro zdravlje, "#kaže direktor HERI -a Ned Kalin. “Ove bi informacije trebale potaknuti novi napredak u sprječavanju i liječenju mentalnih poremećaja, kao i drugih problema koji utječu na kognitivne sposobnosti. ”

HERI projekti koji su već u tijeku razmatraju profile snimanja mozga koji se odnose na anksiozne poremećaje, depresiju i učinak antidepresiva. Znanstvenici će koristiti tu tehnologiju za proširenje svojih istraživanja pozitivnih emocija i njihovih odnosa na zdravlje, proučavajući, na primjer, ljude koji pokazuju visoku razinu otpornosti i one koji prakticiraju meditaciju.

Tehnike snimanja mogu se koristiti za praćenje fizičkih promjena koje se događaju u mozgu kao posljedica dugotrajnog treninga i učenja, kaže Davidson, pomažući pokazati kako je mozak "plastičan" i sposoban prevladati bolesti ili nedostatke.

Znanstvenici također planiraju buduća istraživanja snimanja mozga autizma, često teškog kognitivnog poremećaja dijagnosticiranog u djece koji pogađa oko 400.000 Amerikanaca. Drugi kandidati za istraživanje su Parkinsonova bolest i Alzheimerova bolest, oba progresivna neuro-degenerativna poremećaja koja mijenjaju motoričku kontrolu i pamćenje.

Davidson kaže da će laboratorij omogućiti izravno povezivanje s mnogim srodnim projektima o razvojnim teškoćama u Centru Waisman i način mjerenja učinkovitosti tretmana. Centar okuplja više od 500 nastavnika i osoblja s 27 odjela na sveučilištu, a njegov novi istraživački toranj od 25 milijuna dolara uključuje nova krila posvećena staničnoj terapiji, genskoj terapiji i drugim kliničkim granicama.

Svjedočanstvo nacionalne jedinstvenosti laboratorija, kaže Davidson, jest da je pomogao privući brojne nove, vrhunske fakultete neuroznanosti u UW-Madison. Mark Seidenberg, trenutno profesor psihologije i neuroznanosti na Sveučilištu Južna Kalifornija, jedan je od vodećih znanstvenika u razvoju jezika#8217. Andrew Alexander, docent medicinske fizike i psihijatrije UW -Madison, služit će kao laboratorijski fizičar za magnetsku rezonancu. On razvija načine korištenja MRI za vizualizaciju veza između različitih regija mozga.

Andrew Roberts, također medicinske fizike i psihijatrije, integralno je uključen u specifikacije opreme i instalaciju u laboratoriju. Još jedno nedavno zaposlenje je Paul Whalen, snimatelj mozga i neuroznanstvenik s Harvarda koji je sada docent za psihijatriju i psihologiju UW -a.


Za objašnjenje (jednostavno i detaljno) kako orijentacija radi s NIfTI datotekama i FSL -om pogledajte Orijentacija objašnjena. Trebao bi nikada koristite dolje navedene alate dok ne pročitate ovu stranicu o orijentaciji - općenito samo korištenje ovih alata (s izuzetkom fslreorient2std) koje preporučujemo je kada su vaše oznake, gledane u fsleyesu, netočne s obzirom na anatomiju.

Sljedeće informacije potrebne su samo iskusnim korisnicima ili onima koji imaju sliku na kojoj su oznake netočne.

U datotekama nifti moguće je samostalno prijaviti ili izmijeniti datoteku qform ili sform polja (dolje potražite osnovne informacije o NIfTI orijentaciji). Međutim, izlazne rutine FSL4.1 pokušat će se zadržati matrice qform i sform iste kad god bi netko inače bio neraspoložen. Stoga nije moguće, na primjer, izbrisati samo qform, kao da je sform postavljen, tada će to dovesti do toga da qform bude jednak (što je moguće bliže) sform -u. To se trenutno radi kako bi se pomogla interoperabilnost s drugim paketima. Međutim, ako se qform -u i sform -u daju različite vrijednosti, one se čuvaju izlaznim rutinama.

Ova naredba nimalo ne mijenja pohranu podataka - samo informacije o orijentaciji u zaglavlju pa je treba koristiti s velikim oprezom. Izmijenite podatke zaglavlja samo ako razumijete dolje navedene podatke o orijentaciji prema NIfTI -u ili slijedite (točno) niz koraka koje je netko propisao.

Nova orijentacija podataka određuje se odabirom što bi nove osi trebale predstavljati. To se može učiniti u smislu starih osi (x y z -x -y -z) ili u smislu anatomskih oznaka kada su ti podaci dostupni (na slici nifti gdje ili kôd sform ili qform nije nula). Anatomske oznake su RL LR AP PA SI IS. Imajte na umu da verzija anatomske naljepnice neće dopustiti promjenu konvencije lijevo-desno.

Obično fslswapdim promijenit će informacije o orijentaciji u zaglavlju, kao i preuređivanje podataka. To je tako da anatomske oznake ostanu pričvršćene na iste dijelove slike, a ne fiksirane na koordinate voksela. Stoga bi preusmjeravanjem koronalne slike na aksijalno rezanje oznake trebale biti pravilno pričvršćene na relevantne dijelove slike, sve dok su u početku bile točne. Ako su početne oznake netočne (pogledajte oznake u fslview) tada fleksibilan potrebno je koristiti zajedno sa fslswapdim kako bi se ovo ispravilo.

Kada fslswapdim ima argumente koji će promijeniti orijentaciju lijevo-desno, izdat će upozorenje da se orijentacija lijevo-desno preokreće. Također će pokušati izmijeniti informacije o orijentaciji u zaglavlju (samo ako kôd sform ili qform nije nula), ali ne na način koji mijenja ovu orijentaciju lijevo-desno u zaglavlju. Stoga dolazi do neto promjene u orijentaciji jer se podaci zamjenjuju, a zaglavlje nije. Ako se zamjena dogodi na osi x, zaglavlje se uopće ne radi. U suprotnom, osi koja se mijenja, zajedno s osi x, mijenja se njihova orijentacija. Na taj način se zadržava ručnost zaglavlja, oznake povezane s osi y i osi slijede promjenu slike, ali oznake osi x ne. Preporučuje se da, ako je potrebna zamjena lijevo-desno u pohrani (a to bi trebalo biti učinjeno samo ako je rekonstrukcija u početku netočna i ne može se popraviti pronalaženjem boljeg načina pretvorbe), tada argumenti -x y z treba koristiti jer je ovo najjednostavniji oblik zamjene jer utječe samo na podatke osi x i ne mijenjaju se oznake ili informacije zaglavlja.

Fslutils (posljednji ispravljen korisnik MarkJenkinson 05:48:13 18-04-2020)


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Keywords : Down syndrome, face resemblance, Face-n-Food paradigm, visual social cognition, neurodevelopmental disorders

Citation: Pavlova MA, Galli J, Pagani F, Micheletti S, Guerreschi M, Sokolov AN, Fallgatter AJ and Fazzi EM (2018) Social Cognition in Down Syndrome: Face Tuning in Face-Like Non-Face Images. Ispred. Psihol. 9:2583. doi: 10.3389/fpsyg.2018.02583

Received: 24 September 2018 Accepted: 03 December 2018
Published: 18 December 2018.

Marco Tamietto, Tilburg University, Netherlands
Giorgio Vallortigara, University of Trento, Italy

Copyright © 2018 Pavlova, Galli, Pagani, Micheletti, Guerreschi, Sokolov, Fallgatter and Fazzi. Ovo je članak s otvorenim pristupom distribuiran pod uvjetima Licence za dodjeljivanje autorskih prava Creative Commons (CC BY). Korištenje, distribucija ili umnožavanje na drugim forumima dopušteno je, pod uvjetom da su izvorni autor (i) i vlasnici (i) autorskih prava zaslužni (autor) i da se citira izvorna publikacija u ovom časopisu, u skladu s prihvaćenom akademskom praksom. Nije dopuštena upotreba, distribucija ili reprodukcija koja nije u skladu s ovim uvjetima.


DTI Tutorial 1 - From Scanner to Tensor

Starting out with data analysis often seems like a daunting task, as there are innumerable software packages with sometimes poor documentation. To make this process easier and help you get started with diffusion imaging analyses I put together this tutorial that will introduce you to the most important processing steps and tools. Always keep in mind that there are many ways of processing your data, and the examples used in this tutorial provide just one way of doing things. As you get more versed with the different steps and software you will be able to try different packages and see which you prefer best.

There will be questions in each section to allow you to actively work with data and tools. These question will sometimes ask you to install software or download example data, you can find the example data for this tutorial here. Most of the publicly available (and thus free) software tools used in neuro-imaging will only work in a bash terminal environment (linux or mac). If you are not already, try to become comfortable in a linux environment. An example of linux tutorials can be found online, for example the "UNIX Tutorial for Beginners" contains explanations of basic commands commonly used to navigate the terminal. If you would like some advanced tutorials that go into scripting in bash you can check out this linux tutorial, as scripting in linux will become an important skill when datasets get large. Finally, this DTI tutorial assumes a basic understanding of diffusion MRI, if you would like to catch up on that first, you can go trough these slides that introduce this tutorial. Beyond that you can check out the post on diffusion imaging 101, and start perusing other posts on this website.

In this first tutorial the overall goal is to demonstrate how to convert raw diffusion data files to tensor images. In order to get to tensor images there are a number of intermediate steps that are essential to go through:
1. You will have to move the data off the scanner and convert it to a usable format.
2 & 3. It is important to deal with distortions common to diffusion images, like EPI and eddy currents.
4. Make sure you remove any non-brain tissues.
5. Make the gradient directions file.
6. Fit the tensors.
7. Check the fit of the tensors.
At the end of the document you will find a pdf with the answers to the questions in this tutorial, although try to figure things out yourself as much as possible. If you run into issues going through this tutorial please direct your questions to the diffusion imaging forum, where there will likely be someone able to help you out. Please make sure to describe your question as clearly as possible. Remember to please direct software specific questions to their own respective forums or mail lists.


REZULTATI

The prescan practice trials were used to determine individual low and high contrast levels for each participant to be used during scanning. The mean high contrast level across participants was 0.0708 root mean squared energy (SNR = 0.6235) with a range of 0.035 to 0.181. The mean low contrast level across participants was 0.0338 root mean squared (SNR = 0.1226) with a range of 0.022–0.055. Contrast level was not a factor of interest in the experimental design thus, the remaining analyses were performed after collapsing across contrast level.

Trials for which participants gave no response were very few, but these were excluded from the analyses. Figure 2 shows accuracy for the five experimental stimulus types, presented in upright and inverted orientation. A repeated-measures ANOVA with Accuracy as the dependent variable and with two within-subject factors (Stimulus Type and Orientation) showed a significant effect of Stimulus Type, Ž(4, 92) = 19.08, str < .001, a significant effect of Orientation, Ž(1, 23) = 23.56, str < .001, and a significant interaction between Stimulus Type and Orientation, Ž(4, 92) = 5.55, str & lt .001. Five one-tailed planned contrasts—corrected for multiple comparisons using the false discovery rate (FDR) method—were used to assess the effect of inversion across each of the five stimulus types. These revealed that accuracy was better for the upright than inverted orientation for the eyes-mouth, eyes-nose-mouth, and whole-face stimulus types (all t(23) > 2.95, str & lt .01).

Accuracy as a function of stimulus type. 2E-M = eyes-mouth, 2E-N-M = eyes-nose-mouth. Error bars are 95% confidence intervals.

Accuracy as a function of stimulus type. 2E-M = eyes-mouth, 2E-N-M = eyes-nose-mouth. Error bars are 95% confidence intervals.

Two-tailed post hoc Tukey's tests were conducted to assess the other pairwise differences between means. Within the upright orientation, accuracy with the whole-face stimulus was significantly better than accuracy with all other stimulus types (all q(5, 23) = 6.05, str < .05) and accuracy with the single right-eye stimulus was significantly worse than with all other stimulus types (all q(5, 23) > 4.74, str & lt .05). No other comparisons among upright stimuli reached significance. Within the inverted orientation, accuracy with the whole-face stimulus was significantly better than with the eyes-nose-mouth, eyes-nose, and right-eye stimulus types (all q(5, 23) > 4.18, str < .05), but not better than the two-eyes stimulus type. No other comparisons among inverted stimuli reached significance.

A priori ROIs were localized using the data from the independent functional localizer run. The ROIs were determined from a group-averaged whole-brain fixed-effects general linear model thresholded using the FDR method (q = .05). The locations of the OFA and FFA were determined by contrasting the face and object stimulus conditions, and the location of the LO was determined by contrasting the object and noise stimulus conditions. The locations of the ROIs are shown in Figure 3, and the Talairach coordinates are shown in Table 1. Beta weights representing BOLD signal change were extracted from the ROIs for each participant. A summary of the data is shown in Figure 4. A repeated-measures ANOVA with BOLD percent signal change as the dependent variable and four within-subject factors (Region, Hemisphere, Stimulus Type, Orientation 3 × 2 × 5 × 2) revealed a significant two-way interaction between Region and Stimulus Type, Ž(8, 88) = 7.82, str < .001, and another significant two-way interaction between Region and Orientation, Ž(2, 22) = 8.60, str = .002.

Locations of object- and face-preferring ROIs. The object–noise contrast is shown with a threshold of t = 16. The face–object contrasts is shown with the FDR threshold (q = .05 voxel-wise t = 3.51). Z values are from the Talairach reference.

Locations of object- and face-preferring ROIs. The object–noise contrast is shown with a threshold of t = 16. The face–object contrasts is shown with the FDR threshold (q = .05 voxel-wise t = 3.51). Z values are from the Talairach reference.

Talairach Coordinates for Face-preferring ROIs

Regija . x . Y . Z .
L-FFA −46 −50 −13
R-FFA +36 −50 −13
L-LO −35 −81 −5
R-LO +33 −83 −1
L-OFA −22 −94 −9
R-OFA +21 −91 −8
L-STS −59 −53 +6
R-STS +50 −57 +14
L-IFG −43 +22 +8
R-IFG +52 +19 +18
Regija . x . Y . Z .
L-FFA −46 −50 −13
R-FFA +36 −50 −13
L-LO −35 −81 −5
R-LO +33 −83 −1
L-OFA −22 −94 −9
R-OFA +21 −91 −8
L-STS −59 −53 +6
R-STS +50 −57 +14
L-IFG −43 +22 +8
R-IFG +52 +19 +18

BOLD signal change as a function of stimulus type, hemisphere, and orientation for the FFA, OFA, and LO. Error bars are 95% confidence intervals.

BOLD signal change as a function of stimulus type, hemisphere, and orientation for the FFA, OFA, and LO. Error bars are 95% confidence intervals.

The main hypotheses were based on assessing inversion effects in BOLD fMRI activation across ROIs therefore, one-tailed planned comparisons of these effects were undertaken using the FDR method of correction for multiple tests (q & lt .05). The sizes of the inversion effects are shown in Figure 5. In the right FFA, only the whole-face stimulus showed a significant inversion effect (t(11) = 4.92, q & lt .05). Because the hemisphere factor did not produce any significant interactions with the other three factors (region, stimulus types, or orientation), BOLD activation in the OFA and the LO is shown collapsed across hemispheres in Figure 5. In the OFA and LO, unlike in the FFA, inversion effects with the whole-face stimulus were not significant. The OFA and LO did show significant effects of inversion with the eyes-mouth and eyes-nose-mouth stimulus types however, the preference was for inverted stimuli rather than upright (all t(11) > 2.67, q < .05), that is, a reverse inversion effect.

Inversion effects as a function of stimulus type and brain region. The vertical axis represents the difference in BOLD signal change between upright and inverted presentations. Error bars are 95% confidence intervals.

Inversion effects as a function of stimulus type and brain region. The vertical axis represents the difference in BOLD signal change between upright and inverted presentations. Error bars are 95% confidence intervals.

Because the behavioral inversion effects found with the eyes-mouth and eyes-nose-mouth stimuli were not reflected in any of the three independently defined ROIs, a further whole-brain analysis was performed to find the clusters that produced significant inversion effects in BOLD activation for those stimulus types. The left panel in Figure 6 shows a map of the contrast of upright versus inverted orientations, collapsed across the eyes-mouth and eyes-nose-mouth stimuli. Correction for multiple tests was performed by choosing a relatively liberal voxel-wise threshold (str < .01) and applying a cluster-size filter (Lazar, 2010 Thirion et al., 2007 Forman et al., 1995). The size of the cluster required to satisfy a family-wise error rate of str = .05 was determined using Monte Carlo simulation (Nichols, 2012 Goebel, Esposito, & Formisano, 2006) to be eight 3 × 3 × 3 mm voxels. Using that threshold, the only significant cluster was in the right inferior frontal gyrus (IFG), a region that is part of the extended face-preferring network (Ishai, 2008 Haxby, Hoffman, & Gobbini, 2000). For comparison, the right panel of Figure 6 shows a map from the independent functional localizer run from a contrast of faces versus objects, confirming that the IFG cluster shown in the left panel shows a preference for face stimuli. The plot below the map shows mean BOLD signal change in the IFG cluster and illustrates that the inversion effect in the IFG was not driven by a single stimulus type but was driven relatively equally by inversion effects with eyes-mouth, eyes-nose-mouth, and whole-face stimuli, but not two-eyes or right-eye stimuli.

Whole-brain maps of inversion effects and face-preferring regions. For the left and middle columns, the brain maps depict contrasts of upright versus inverted orientations. For the left column, the contrast included the eyes-nose and eyes-nose-mouth combination stimuli. For the middle column, the contrast included the eyes-nose, eyes-nose-mouth, and whole-face stimuli. For the right column, brain maps depicts a contrast of the face and object stimuli from the independent localizer run. Z values are from the Talairach reference.

Whole-brain maps of inversion effects and face-preferring regions. For the left and middle columns, the brain maps depict contrasts of upright versus inverted orientations. For the left column, the contrast included the eyes-nose and eyes-nose-mouth combination stimuli. For the middle column, the contrast included the eyes-nose, eyes-nose-mouth, and whole-face stimuli. For the right column, brain maps depicts a contrast of the face and object stimuli from the independent localizer run. Z values are from the Talairach reference.

The middle panel of Figure 6 shows the inversion effect contrast with whole faces added to eyes-mouth and eyes-nose-mouth stimuli (the three stimulus types that showed significant behavioral inversion effects), thresholded using the same voxel-wise and cluster criteria as above. For this contrast, the right IFG cluster expanded to include more ventral voxels and a small ventral cluster appeared in the left IFG. There were also significant clusters found in the bilateral STS, a region that is part of the core face-preferring network (Ishai, 2008 Haxby et al., 2000).


Rezultati

Hippocampal volumes and behavioural results

Hippocampal volumes and behavioural performance (accuracy, response time) are reported here in Table 1 as in [28]. There were no significant age differences in hippocampal volumes or task accuracy. Older adults responded more slowly (M = 2042 ms SD = 553 ms) than younger adults (M = 1543 ms SD = 205 ms), Ž(1,30) = 11.77, str < 0,001.

To ensure that age differences in response times were not due to issues of fatigue in the older adults, we examined whether older adults had disproportionately longer RTs for trials that appeared later in the study. In fact, both younger and older adults had brže response latencies on the last 102 trials (M = 1766 ms SD = 316 ms) than on the first 102 trials (M = 1900 ms SD = 353 ms), Ž(30) = 19.614, str < 0,001. There was no interaction with age. Therefore, the present findings, including any observed age-related changes in oscillatory activity, are not likely due to fatigue.

Note that, as previously reported [28], one older adult’s accuracy was within 2.5 SD of the mean for the older adult age group, but this participant’s response time was outside of the 2.5 SD range. Removing this older adult did not change any of the results with respect to average hippocampal volumes, accuracy, oscillatory activity, or the relationship among hippocampal volumes, accuracy, and oscillatory activity therefore this participant was included in these analyses. However, this participant was excluded from analyses that examined the brain-behaviour relationship between response-locked oscillatory behaviour and response times.

Head movement

To rule out the possibility that greater head movement in older adults may contribute to the effects reported here, we submitted absolute changes in fiducial marker positions between runs to a three-way mixed design ANOVA with age as a between subject variable (younger adults, older adults), and fiducials (nasion, left ear, right ear) and axis (x,y,z) as within subject variables. There was a significant three-way interaction Ž(120) = 2.88, str = 0.03, which revealed that younger adults showed greater movement between runs in the nasion fiducial along the z-axis (0.631) than did the older adults (0.255 cm), t(30) = 2.23, str = 0.03.

Recruitment of oscillatory activity during the delay phase

Neural activity during the delay phase was assessed by comparing the oscillatory power from the pre-stimulus interval (-0.25 s to 0 s) to that from the delay phase (0.25 s to 1.5 s) (see Fig 2B for the time windows in the time frequency plot). This compares a time period during which participants ostensibly are maintaining online an already-formed representation of the relative spatial positions among the objects (delay phase) to a time period with a similar amount of visual information presented (i.e., no objects on the screen), but there has not yet been an opportunity for relational binding or maintenance of such a bound representation (pre-stimulus interval). Changes in theta, alpha, and beta power were examined for each group and group differences were assessed. Mean-centered PLS identified a significant pattern of brain activity that accounted for 92% of the covariance in the data, and captured differences between the pre-stimulus interval and the delay phase (str < 0.0001) but did not differ between groups. The red nodes indicate a positive theta modulation (delay phase > pre-stimulus interval) in frontal and temporal areas, while the blue nodes indicate widespread negative modulations (delay phase < pre-stimulus interval) in all three frequency ranges in frontal, temporal, and parietal areas ( Fig 3A ).

Error bars signify confidence bounds (95%) obtained from the bootstrap distribution. Distribution of virtual channels that positively (red) and negatively (blue) express the contrast are shown on the bottom of each analysis. A: Both groups showed a widespread power decrease from the pre-stimulus period to the delay phase across all frequency ranges. B: Only older adults showed a widespread power decrease from the first study display to the test phase in all frequency ranges.

Recruitment of oscillatory activity during the test phase

Neural activity during the test phase was assessed by comparing the oscillatory power from the first study display (0.25 s to 2.5 s) to that from the test phase (0.25 to 2.5s) (see Fig 2B for the time windows in the time frequency plot). This compares a time period in which participants were presumably engaged in retrieval and comparison processes (test phase), to a time period in which visual information was also presented, but binding demands were minimal as only one object had been presented (first study display) (see S2 to S4 Figs for alternative analyses comparing test phase activity to pre-stimulus baseline). Changes in theta, alpha, and beta power were examined for each group and group differences were assessed. Comparisons between intact versus manipulated trials during the test phase revealed no significant effects thus, the presented results compare oscillatory activity from all trials in the test phase to all trials from the first study display of the encoding phase.

Mean-centered PLS identified a significant pattern of brain activity that accounted for 94% of the covariance in the data and captured group differences between the first study display and the test phase (str < 0.0001). The results in Fig 3B demonstrate that oscillatory power changes during the test phase are reliable for older adults only (confidence intervals cross zero for the younger adults). The red nodes indicate a positive theta modulation (test phase > first study display) in occipital areas, while the blue nodes indicate a widespread negative theta (first study display > test phase) modulation in frontal, temporal, and parietal areas. Older adults also showed negative modulations of alpha and beta frequencies across a widely distributed network.

It is possible that the effects observed in older adults ( Fig 3B ) may have overwhelmed a significant, albeit smaller effect in the younger adults. To explore this, a separate mean-centered PLS was performed on the younger participants only. This identified a significant pattern of activity that accounted for 99% of the covariance in the data (str < 0.0001). The results in Fig 4A demonstrate that during the test phase, positive modulations (test phase > first study display) were observed for younger adults in theta in frontal, temporal, and occipital areas, and beta in occipital areas. The blue nodes indicate that a negative modulation (first study display > test phase) was observed in alpha in temporal regions, and in beta in frontal and parietal areas.

Error bars signify confidence bounds (95%) obtained from the bootstrap distribution. Distribution of virtual channels that positively (red) and negatively (blue) express the contrast are shown on the bottom. A: Younger adults showed a frontal and occipital theta increase, temporal alpha decrease, occipital beta increase, and frontotemporal beta decrease from the first study display to the test phase. B: Older adults showed an occipital theta increase, and widespread power decreases across all frequencies.

A separate mean-centered PLS was also performed on the older participants only. This identified a significant pattern of activity that accounted for 99% of the covariance in the data (str < 0.0001). The results in Fig 4B show that during the test phase, positive modulations (test phase > first study display) were observed for older adults in theta in occipital areas. Widespread negative modulations (first study display > test phase) were observed in alpha and beta, and fronto-temporal theta.

Structure-function and brain-behaviour relationships during the test phase

To comprehensively understand the impact of age-related changes in oscillatory activity, a behavioural PLS was conducted to examine the relationship between oscillatory activity and behaviour (see S1 Fig for alternative analyses defining theta as 4-7Hz). There was a significant relationship between oscillatory activity in the test phase (minus the first study display) and accuracy (str = 0.05, crossblock covariance: 76%) that was reliable in younger, but not older adults (confidence intervals cross zero bounds, Fig 5A ). As shown in Fig 5B , the red nodes indicate a positive relationship between accuracy and theta, alpha, and beta power changes (r = 0.53) for younger adults in a number of frontal, temporal, and parietal virtual channels.

(A, left) Contrast shows group differences that are reliable for younger adults only. (A, right) The scatterplot recapitulates the effects by showing the distribution across individuals across groups with no obvious outliers present. (B) Distribution of virtual channels that positively express the contrast is shown for the three frequency bands of interest. Younger adults showed theta, alpha, and beta power increases that predicted higher task accuracy.

By contrast, there was a significant relationship between oscillatory activity in the test phase and response times (str = 0.05) that was reliable in older adults but not younger adults. This was still significant after removing the older adult with a mean response time outside of the normal range for his/her age group crossblock covariance: 86% Fig 6A ). As shown in Fig 6B , the blue nodes indicate a negative relationship between mean response time and beta power in frontal, temporal, parietal, and occipital areas (r = 0.61) such that longer response times were linked with greater power decreases. A similar negative relationship was also observed with theta power in frontal and parietal areas, as well as alpha power in parietal areas.

(A, left) Contrast shows group differences that are reliable for older adults only i.e. confidence bounds computed from bootstrap estimation are stable. (A, right) The scatterplot recapitulates the effects by showing the distribution across individuals across groups with no obvious outliers present. (B) Distribution of virtual channels that negatively express the contrast is shown for the three frequency bands of interest. Older adults showed a theta, alpha, and beta decrease that predicted longer response latencies.

Response-locked neural oscillations

To further explore the age-related power differences in the test phase, oscillatory activity preceding a behavioural response was examined. Trials in which participants responded faster than 1 second were excluded to avoid capturing activity from the delay phase. Mean-centered PLS identified a significant pattern of neural oscillations that differed between the first study display and the period immediately preceding a behavioural response and accounted for 97% of the covariance in the data (str < 0.0001). The results in Fig 7 demonstrate that the pattern of oscillatory activity prior to the response in the test phase was expressed more strongly by older than younger adults. The red nodes indicate a positive theta modulation (test phase > first study display) for both groups in occipital areas, while the blue nodes indicate a negative theta modulation (first study display > test phase) for both groups in frontal, temporal, and parietal areas. Widespread negative modulations of alpha and beta frequencies were also observed across a widely distributed network. Similar patterns of oscillatory activity was therefore observed between younger and older adults, and this pattern of activity differed from that observed for the stimulus-locked analyses that examined the relationship with response time.

Error bars signify confidence bounds (95%) obtained from the bootstrap distribution. Distribution of virtual channels that positively (red) and negatively (blue) express the contrast are shown on the right for three frequency bands of interest (theta, alpha, and beta). Older adults express the pattern more reliably than younger adults across all frequency ranges.

Response-locked structure-function and brain-behaviour relationships

To further probe the age-related oscillatory power changes in the response-locked analysis, a behavioural PLS was conducted to examine the relationship between oscillatory activity and behaviour. There was a marginally significant relationship between neural oscillations preceding the behavioural response and mean response times in older adults, but not younger adults (str = 0.087, crossblock covariance: 83%). This relationship was significant after removing the older adult with a mean response time outside of the normal range for his/her age group (str = 0.030, crossblock covariance: 85% Fig 8A ). As shown in Fig 8B , the blue nodes indicate a negative relationship between mean response time and theta power for older adults in the frontal, temporal, parietal, and occipital areas (r = 0.63) such that longer response times were linked with greater power decreases. A similar negative relationship was also observed with alpha power in the left supramarginal gyrus, and beta power in frontal and parietal areas.

(A, left) Contrast shows group differences that are reliable for older adults only (A, right) The scatterplot shows the distribution of individuals from each age group. (B) Distribution of virtual channels that positively (red colors) and negatively (blue colors) express the contrast are shown for three frequency bands of interest (theta, alpha, and beta). Older adults showed a power decrease in all three frequency ranges that predicted longer response times.


5. Author contributions

CMB is the PI of the study and is responsible for the conceptualisation and data acquisition and analyses of the study. CMB has also written the manuscript. JPM and EL were responsible for participant recruitment, data acquisition and MRI data processing. RS was responsible for the APOE genotyping. FF and JE have prepared the qMT and diffusion MRI protocols and have helped with MRI data processing. AKM and FG carried out the manual segmentations of the abdominal fat area regions. RJB was involved in the conceptualisation and has advised on statistical data analysis. EK and BE were responsible for the ELISA serum analyses. DKJ provided feedback on the study design and manuscript.


Gledaj video: MRI - Magnetna Rezonanca Poliklinika SUNCE Zenica (Svibanj 2022).