Large, High-Dimensional Data Sets in Functional Neuroimaging

Transcription

1 Goals of Functional Neuroimaging Identify Regional Specializations of the Brain Large, High-Dimensional Data Sets in Functional Neuroimaging 1 Goals of Functional Neuroimaging Goals of Functional Neuroimaging Identify Regional Specializations of the Brain Identify Regional Specializations of the Brain Understand Network Connectivity Basic Science Questions Medical or Surgical Intervention 2 Velocity Direction Physical Location Topology Temporal Information Texture Identity Color Vision Language 3 4

2 Goals of Functional Neuroimaging Identify Regional Specializations of the Brain Understand Network Connectivity Understand Multiple Levels of Organization Levels of Understanding fmri - functional nuclei or processing centers Fiber Tracing regional connectivity fmri-ephys EEG & Autoradiography cell assemblies Multi-unit Recording local circuits: columns, retina Single Unit Electrophysiology action potentials, chemomodulation Crystallography, Chromatography (etc ) transmitters, ion channels, membrane proteins fmri-eeg 5 The Big Problem isn t sparse... Goals of Functional Neuroimaging Identify Regional Specializations of the Brain Understand Network Connectivity Understand Multiple Levels of Organization Understand the Structure of Human Cognition Neuroimaging Tools Positron Emission Tomography (PET) functional MRI (fmri) Electro-encephalography (EEG) Magneto-encephalography (MEG) Near Infrared Spectroscopy (NIRS) 7 8

3 Positron Emission Tomography (PET) Positron Emission Tomography (PET) outside inside = Note: Recon similar to CT Phelps, Mazziotta, et al Tractography fmri up to 1m explores intensity variations in MR signal 40 µm intensity variations reflect venous [O2] 11 12

4 Traditional MRI Analysis - Model Driven Task Timing Traditional MRI Analysis - Model Driven Hemodynamic Response Model Task Model Observed Signals Signal Model z=5 z= Model-Free MRI Analysis Independent Components Analysis (ICA) Spatial ICA for fmri Location (space) # ICs Location (space) Time Scan #k fmri Image Data Time # ICs IC Spatial Maps data are decomposed into a set of spatially-independent maps and a set of time courses. impose spatial indepedence

5 ICA Exposes Functional Networks EEG: Hans Berger EEG vs. Magnetoencephalogaphy (MEG) EEG vs. Magnetoencephalogaphy (MEG) 19 20

6 EEG EEG Creutzfeld Jacob (prion) disease Left parieto-posterior temporal spikes during drowsiness Somatosensory Evoked Potentials EEG/MEG EEG/MEG is generally difficult to interpret Physicians frequently fail to detect abnormalities Electrical features are ambiguous Estimation of electrical sources from scalp Voltage is underconstrained with Steve Sands and Massoud Akhtari 24

7 Near Infrared Spectroscopy (NIRS) Neuroimaging Tools - Data Size/subject Positron Emission Tomography (PET) 128 x 128 x 12 2E5 samples functional MRI (fmri) 64 x 64 x 20 x E7 samples, but... Electro-encephalography (EEG) & Magneto-encephalography (MEG) 256 x 250samples/s x 600s 3.8E7 samples Near Infrared Spectroscopy (NIRS) 32 x 250 samples/s x 600s 4.8E6 samples Brain Reading Machine Learning in fmri Postulate: All interesting behavioral, affective, mental or cognitive states are the expression of, or reflected in, neural activity Resulting maps are difficult to interpret. Haxby, et al., Science 293:

8 Optimal Basis Selection Efficient Machine Learning Dimensions Are: Independent Measures (foot size + shoe size adds little) Sparse - Ideally the minimum number needed to categorize the data Too Many Dimensions Results in Errors! For Scientific Applications Dimensions Ideally Reflect Real Sample Properties and are Explanatory What are the Right Dimensions for Neuroscience? A (perhaps naïve) Model of Cognition Multiple Networks are Concurrently Active Many Such Networks are Common Across People Current Cognitive State Reflects the Contributions of all Currently Active Networks Perhaps: Current Cognitive State is the sum of Active Network Activity CS = α 1 N 1 + α 2 N 2 + α 3 N α j N j. Where: CS is the current cognitive state Nk is one among many networks αk is the activity level of the corresponding network CS and α are functions of time ICA Exposes Functional Networks IC Dictionary Elements Categorization and Generation of group-wide independent components in fmri using clustering. A Anderson1, J Bramen, A Lenartowicz, P Douglas, C Culbertson, A Brody, MS Cohen. OHBM

9 IC s as Classifier Dimensions CS = α 1 N 1 + α 2 N 2 + α 3 N α j N j. Belief Sam Harris Beliefs are actions in potentia Network 2 Network 3 I believe I a sandwich that the would infidels be must tasty die. now Cognitive State Instance Network 1 Why should belief and disbelief gate emotion and behavior in this way? Why should uncertainty not do so? 33 Belief Operationalized Belief Sam Harris Beliefs are actions in potentia I believe that the infidels must die. Autobiographical You own a toaster oven. Ethical It is good to help people in need. Factual Sugar is sweet. Geographical Nevada borders California. Mathematical (45/3) + 25 = 40 Religious Jesus was actually born of a virgin. Semantic gigantic means huge S Harris, SA Sheth and MS Cohen, Annals of Neurology, 63(2): p

10 Belief Detector Wavelet Spectrogram: Optimal Sampling Schedule PK Douglas, S Harris, A Yuille and MS Cohen, Performance comparison of machine learning algorithms and number of independent components used in fmri decoding of belief vs. disbelief. NeuroImage, 56(2): p frequency frequency Channel 19 Channel 145 time Diagnostic 1 sec Belief Disbelief Channel 8 Channel time time time Non-informative Spectrogram Power Sampled at 20 ms Intervals Highly Ranked Channels Determine Feature Time Points Optimal disbelief Time Point 500 ms After Belief Consistent with Behavioral Data Pamela Douglas, Edward Lau Agatha Lenartowicz, Wei Li Belief Disbelief Belief Disbelief Concurrent EEG and fmri Observable signals 1 sec Activity Observable with fmri Common Neural Substrate Activity Observable with EEG Noise Noise Filter Process e.g. hrf Filter Process e.g. coherent activity only Noise Noise fmri Signal EEG Signal based on a figure by Dan Ruan 39 40

11 Multimodal Imaging EEG & fmri Signal Strength α β The observed EEG is the linear sum of the underlying electrical activity: Synchronous Activity (SA) Asynchronous Activity (AA) The magnitude signal, EEG, is: EEG = k( SA + AA ) fmri signal is (probably) a function of the sum: fmri = f ( SA+AA) EEG-fMRI Coupling - A Variety of Mechanisms? Tomographic EEG Projection 5s 5s Xia Hongjing Organization for Human Brain Mapping 2012 Wei Li, Edward Lau Pamela Douglas, Agatha Lenartowicz, 43 44

12 What is Sparse in Functional Neuroimaging? What is Sparse in Functional Neuroimaging? Pixel-level analysis Axonal (fiber) connections Number of Brain States Blood flow responses w.r.t. driving functions Resolvable Electrical Sources Number of meaningful networks Shared Sources in Multimodal data 45 46