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20120317 physicsbrainnetwork


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  • 1. Physics,  Network,  Brain Sunghyon  Kyeong National Institute for Mathematical Sciences, Computational Neuroscience Team Saturday  Science Sogang  University,  Korea,  17th  March  2012
  • 2. Contents• Physicsin  Magnetic  Resonance  Imaging  (MRI)   -­‐  Physics   -­‐  Principle  of  BOLD  signal  of  fMRI• Complex  Network Theory  /  Example -­‐  Introduction  to  Graph   -­‐  Social  Network  with  Twitter  Streamline• Brain  and  NeuroscienceHuman  Brain -­‐  Studying  complex  network  in   -­‐  Network  alteration  during  motor  task -­‐  TMS  for  motor  function  recovery 2
  • 3. Physics inMagnetic ResonanceImaging (MRI)
  • 4. Philips  3T  Scanner Siemens  3T  Scanner Philips  0.6T  Open  Scanner 4
  • 5. Components  of  MRI 1.  Magnetic  :  Static  Magnetic  Field  Coils 2.  Resonance  :  Radio  frequency  Coil 3.  Imaging  :  Gradient  Field  Coils                                    spatial  encoding  of  the  MR  signal -­‐  Shimming  Coils -­‐  Data  transfer  and  storage  computers 5
  • 6. What  is  Resonance  ? • Let’s  recall  the  resonance   frequency  when  you  learn   in  high  school  physics. • By  applying  small  pushes  at   the  resonance  (!  0)       frequency  of  the  swing   r set  ... 0 = g l • In  MRI,  amplitude  of  RF  is   very  small  compared  to  B0.
  • 7. Properties  of  Atomic  Nuclei • Nuclei  have  two  properties: -­‐  spin,  charge   • Nuclei  are  made  of  protons  and  neutrons: -­‐  both  have  spin  value  of  1/2 -­‐  protons  have  charge • Pairs  of  spins  tend  to  cancel,  so  only  atoms   with  an  odd  number  of  protons  or  neutron   have  spin. • The  spinning  particle  generates  an  angular   momentum  J. ⇥ µ= J ⇥ 7
  • 8. What  nuclei  can  we  measure? • Most  common  in  out  bodies: -­‐  Carbon,  Oxygen,  Hydrogen,  Nitrogen • Of  these,  only  Hydrogen  has  the  nuclear   magnetic  resonance  (NMR)  property. • Hydrogen  is  the  most  abundant  atom  in  the   body -­‐  Mostly  in  water  molecular  (H2O). 8
  • 9. MRI  Signal:    T1  and  T2 • Applying  RF  pulse  to  tip  down  bulk   magnetization  (Mz)  to  the  transverse  plane. • Mz  tends  to  align  the  external  magnetic  Tield   as  time  goes  on  (T1  recovery). • Mz  decays  in  the  transverse  plane  as  time   goes  on  (T2  decay).   Good  Contrast Good  Contrast 9
  • 10. T2*  Decay  in  Presence  of  B0 • The  time  constant  of  the  decay  is  called  T2.   • However,  in  physiological  tissue  the   transverse  relaxation  is  more  rapid  because   of  local  Vield  inhomogeneities. • When  the  Vield  inhomogeneities  are  present,   the  decay  constant  is  called  T2*. 1 1 T2⇤ = T + B0 2
  • 11. Tissue  SpeciGic  T1  and  T2 • T1  recovery  and  T2  decay  time  ranges  from  tens  to  thousands  of   milliseconds  for  protons  in  human  tissue  over  the  main  Tield.   Typical  values  for  various  tissues  are  shown  in  following  table. • Applying  the  pulse  sequences,  we  can  discriminate  brain   tissues;  The  different  sequences  should  be  applied  to  obtain  the   speciTic  image,  for  example,  anatomic,  functional,  angio  images. Tissue T1(ms) T2(ms)  Gray  matter  (GM) 950 100  White  matter  (WM) 600 80  Muscle 900 50  Cerebrospinal  Tluid  (CSF) 4500 2200 obtained  at  Fat 250 60 B0 = 1.5 T  Blood 1200 100~300 T = 37 C 11
  • 12.        Principles  of      Blood  Oxygen  Level              Dependent      Signal  for  fMRI
  • 13. Structural  vs.  Func4onal  MRI Structural  MRI  studies   Functional  MRI  (fMRI)   brain  anatomy studies  brain  function by  Eunha  Lim 13
  • 14. Detecting  fMRI  Signal • The  abbreviation  BOLD  fMRI  stands  for  Blood  Oxygen   Level  Dependent  functional  MRI. • The  BOLD  contrast  mechanism  alters  the  T2*   parameter  mainly  through  neural  activity–dependent   changes  in  the  relative  concentration  of  oxygenated   and  deoxygenated  blood.   • Deoxyhemoglobin  is  paramagnetic  and  inTluences   the  MR  signal  unlike  oxygenated  hemoglobin. 14
  • 15. Contrast  Agents  for  fMRI  ? • DeTinition  :  Substances  that  alter  magnetic   susceptibility  of  tissue  of  blood,  leading  to  changes   in  MR  signal -­‐  Affects  local  magnetic  homogeneity:  decrease  in  T2* • Two  types -­‐  Exogenous  :  Externally  applied,  non-­‐biological  compounds. -­‐  Endogenous  :  Internally  generated  biological  compound  (e.g.,  dHb) • BOLD  functional  magnetic  imaging  method  doesn’t   need  the  external  contrast  agents. 15
  • 16. Measuring  O2  Ratios  in  Brain • BOLD  contrast  measures  inhomogeneities  in  magnetic   Vield  due  to  changes  in  the  level  of  O2  in  the  blood. deoxyhemoglobin   oxyhemoglobin (paramagnetic)   (non-­‐magnetic) Hb dHb Normal  blood  *low High  blood  *low Low  ratio  deoxy  : High  ratio  deoxy  : →  oxygenated  blood   →  deoxygenated  blood   →  slow  decrease  in  MRI  signal →  fast  decrease  in  MRI  signal 16
  • 17. Mechanism  of  BOLD  Signal ↑  Neural  Activity ↑  Blood  Flow ↑  Oxyhemoglobin Signal ↑  T2* Mo sinθ T2* task T2* control Stask ↑  MR  Signal Scontrol ΔS Time TEoptimum 17
  • 18. Hemodynamic  Response 0.02 0.01• %  Signal  Change 0 Arbitrary Units −0.01 =  (point-­‐baseline)/baseline 0.03 0.02 0.01• Time  to  Rise 0 −0.01 0 5 10 15 20 25 30 time (sec) signal  begins  to  rise  after  stimuli  begin.• Time  to  Peak signal  peaks  approximately  6  seconds  after  stimulus  begins.• Post  Stimulus  Undershoot signal  suppressed  after  stimulation  ends. 18
  • 19. B D A C    Complex  Network          -­‐    Graph  Approach
  • 20. Types  of  Graph 1. What  is  degree?   2. betweenness  centrality? 1 3. global/local  network  ef*iciency? 4 2 3 5 undirected   binary  graph 6 Adjacency directed   Matrix binary  graph directed   0 1 1 0 0 0 weighted  graph 1 0 1 0 1 0 Aij  = 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 20
  • 21. Networks  in  the  Real  World • A  network  is  a  set  of  nodes  connected  by  edges. • Types  of  Networks: -­‐ Social  networks:  Facebook,  Twitter,  business   relations  between  companies,   -­‐ Information  networks:  network  of  citations  between   academic  papers,  World  Wide  Web  (web  pages  are   linked  from  one  page  to  other),  semantic  (how  words   or  concepts  link  to  each  other) -­‐ Biological  networks:  Food  web,  (functional/ structural)  brain  network 21
  • 22. Network  Analysis  Example semantic  networkco-­‐authorship  network   formed  by  free  association formed  by  author  list Steyvers,  Cognitive  Science  29  (2005)  41–78 Neumann,  PNAS  101  (2004)  5200-­‐5205   22
  • 23. https://www.facebook/com/sunghyon.kyeong project  members:Informa.on  Flow  Through  TwiLer  Data    Measuring  centrality  (hub  of  informa.on  flow)
  • 24. Measure  Social-­‐InfoGlow • Objective:  measuring  the  centrality  of  the  twitter  status   Vlow  (i.e.  information  propagation  via  retweet)  by   complex  network  analysis. • By  streaming  the  tweet  timeline  data  with  keyword  (i.e.   “총선”,  “화이트  데이”,  and  etc),     • Issues  for  this  project:  (1)  how  to  deal  with  the  large   data  set  (retweet  explosion  for  the  special  events,  i.e.,   “화이트  데이관련”  RT  at  midnight  3/14,  (2)  how  to   deVine  ‘node’  and  ‘edge’  to  measure  tweet  status  Vlow,   (3)  how  to  connect  other  social  networks  (FB,  blog) 24
  • 25. Facebook  vs.  Twitter Facebook Twitter 정보 공유 대상 상호 친구 관계를 맺은 사용자들 정보 구독 대상으로 설정한 사용 자의 새소식을 받아봄. 특징 친구 관계 또는 확장된 친구 관계 하나의 게시물은 140자 이내로 내에서 정보 공유. 작성됨. 정보 접근성 본인과 친구들의 정보 외에는 외부 계정명을 알면 게시물을 조회하 네트워크에서 정보를 조회할 수 없 거나 해당 계정에 메시지를 보낼 음. 수 있음. 전세계 등록 계정수 (2011 약 8억 개 약 2억 개 년) 한국 등록 계정수 (2011년) 약 6백만 개 약 3백만 개
  • 26. Collecting  Tweet  Data • 트위터의 게시물은 온라 인에 공개된 정보 • 게시물이 140자 이내로 작성되기 때문에 실시간 으로 수많은 데이터들이 생성됨 • 9일 이내 데이터에 대해 제한적으로 검색 가능
  • 27. betweenness centrality • Tweet  streamline  data  collected  with   keyword  =  [‘화이트데이’,  ‘화이트  데이’] • Retweeted  at  least  one  time. 3313ahn:  full  of  RT misunmoon1215:  대학생,  상품  소개.  이벤 트  소개.  등  트윗 minjungseo88:  유용한  정보  다량  소개out degree ahn3313:  full  of  RT  (뉴스관련) Trend_bot:    세계적으로  중요한  이슈  RT jeonYH153:  유용한  정보  RT batrobas:  정치  사회적  RT 27
  • 28. betweenness centrality • Tweet  streamline  data  collected  with   keyword  =  [‘총선’,  ‘411’] • Retweeted  at  least  one degree out degree 28
  • 29. Complex  Network  in  Human  Brainbetweenness  centrality  to  iden.fy  default  mode  network
  • 30. Network  Construc4on ... time  course  for   voxel  based  analysis Seed-­‐based   connectivity  (PCC) Spatial  Preprocessing -­‐  realign,  coreg,  norm,  smooth ... Parcellation   into  116  brain  regions Network properties Adjacent  Matrix AAL  map 30
  • 31. Default  Mode  Network • Brain  network  at  resting  state  (within  low  frequency   0.009~0.08  Hz)  including  posterior  cingulate  cortex,  lateral   parietal  area,  medial  frontal  cortex. • Patient  having  a  psychiatric  disease  shows  different  patterns  of   default  mode  network. • Node  betweenness  centrality  might  be  used  to  detect  default   mode  network   seed  based  connectivity node  betweenness  centrality free  download  the  brain  connectivity  matlab  toolbox:­‐connectivity-­‐
  • 32. Alteraon  of  Motor  Networkduring  Finger  Tapping • Data  Preprocessing  &  Network  Construction • Results  on  network  alteration  study  during  the  Tinger   tapping  tasks. 32
  • 33. Resting  State  fMRI • Subjects  were  asked  to  keep  resting  state  with  their  eyes   closed  but  not  sleep. • The  resting  state  fMRI  data  has  both  spatial  and  temporal   characteristic.  However,  it  doesn’t  have  design  matrices. • To  analyze  the  resting  state  fMRI  data,  we  calculate  the   correlation  coefTicients  between  a  region  of  interest  (ROI)   and  the  whole  brain. 33
  • 34. UnderstandingMotor  Cortex by  Eunha  Lim • Every  movements  depend  on  how  the  brain  networks   are  modulated  to  execute  a  speciTic  movement  tasks. • The  precentral  gyrus  plays  a  key  role  in  movement   task.  When  we  move  hands,  feet,  and  facial  parts,  the   corresponding  cortical  areas  in  the  precentral  gyrus   are  associated. 34
  • 35. Modulaon  of  Motor  Networksby  Brain  S4mula4on • Why  do  we  need  the  Brain  s.mula.on?   The  fMRI  data  describes  BOLD  acJvity  preKy  well.   However,  we  don’t  really  know  causality  of  the  network. • TMS  enables  us  to  study  the  brain  plasJcity  or  the   recovery  of  the  brain  funcJon. 35
  • 36. Magne4c  S4mula4on• TangenJal  component  of  the   magneJc  field  is  generated   beneath  the  figure-­‐of-­‐eight  coil.• Time  varying  B-­‐field  generates  E-­‐ field  (Faraday’s  Law).• TMS  generates  the  localized   magneJc  field  along  the  x-­‐ illustraJon   direcJon  not  along  the  z   by  Eunha  Lim direcJon. 36
  • 37. [Journal  Review] Altera.on  of Handedness  by  TMS • UC  Berkeley  researchers  (Oliveira,  et  al.  2010)    revealed   that  the  right-­‐handed  volunteers  were  more  likely  to  use   their  leZ  hand  to  perform  simple  one-­‐handed  tasks  when   the  leZ  posterior  parietal  cortex  of  the  brain  received  TMS.   • By  sJmulaJng  the  parietal  cortex,  which  plays  a  key  role  in   processing  spaJal  relaJonships  and  planning  movement,   the  neurons  that  govern  motor  skills  were  disrupted. 37
  • 38. [Journal  Review] Reduce   CigareHe  Craving   Consump4on  by  TMS • Ten  daily  rTMS  sessions  over  the  dorsolateral  prefrontal   cortex  (DLPFC)  reduced  cigareKe  consumpJon  and   nicoJne  dependence  (Amiaz,  et  al.  2009). • The  rTMS  of  the  DLPFC  might  alter  the  brain  networks   leading  to  reduced  impulsivity  and  enhance  inhibitory   control. 38
  • 39. Thank  you