Neuroimaging: from image to Inference Chris Rorden fMRI limitations: relative to other tools used to infer brain function. fMRI signal: tiny, slow, hidden in noise. fMRI processing: a sample experiment. fMRI anatomy: stereotaxic space. See also: http://www.biac.duke.edu/education/courses/fall05/fmri/

Modern neuroscience Different tools exist for inferring brain function. No single tool dominates, as each has limitations. This course focuses on fMRI. poor (whole brain) iap eeg lesions erp nirs pet tms Spatial resolution fmri good (neuron) scr good (millisecond) poor (months) Temporal resolution

Directly measure neural activity. Exquisite timing information Single Cell Recording Directly measure neural activity. Exquisite timing information Precise spatial information Often, no statistics required! Each line is one trial. Each stripe is neuron firing. Note: firing increases whenever monkey reaches or watches reaching.

With SCR, we are very close to the data. Single Cell Recording With SCR, we are very close to the data. We can clearly see big effects without processing. Unfortunately, there are limitations: Invasive (needle in brain) Typically constrained to animals, so difficult to directly infer human brain function. Limited field of view: just a few neurons at a time.

fMRI Processing Processing Steps Unlike SCR, we must heavily process fMRI data to extract a signal. The signal in the raw fMRI data is influenced by many factors other than brain activity. We need to filter the data to remove these artifacts. We will examine why each of these steps is used. Processing Steps Motion Correct Spatial Intensity Physiological Noise Removal Temporal Filtering Temporal Slice Time Correct Spatial Smoothing Normalize Statistics

fMRI signal sluggish Unlike SCR, huge delay between activity and signal change. Visual cortex shows peak response ~5s after visual stimuli. Indirect measure of brain activity 2 1 0 6 12 18 24 Time (seconds)

What is the fMRI signal fMRI is ‘Blood Oxygenation Level Dependent’ measure (BOLD). Brain regions become oxygen rich after activity. Very indirect measure.

Lets conduct a study Anatomical Hypothesis: lesion studies suggest location for motor-hand areas. Ask person to tap finger while in MRI scanner – predict contralateral activity in motor hand area.. M1: movement S1: sensation

Task Task has three conditions: Up arrows: do nothing Left arrows: press left button each time arrow flashes. Right arrows: press right button every time arrow flashes. Block design: each condition repeats rapidly for 11.2 sec. No sequential repeats: block of left arrows always followed by block of either up or right arrows.

Data Collection Participant Lies in scanner watching computer screen. Taps left/right finger after seeing left/right arrows. Collect 120 3D volumes of data, one every 3s (total time = 6min).

Raw Data The scanner reconstructs 120 3D volumes. Each volume = 64x64x36 voxels Each voxel is 3x3x3mm. We need to process this raw data to detect task-related changes.

Motion Correction Unfortunately, people move their heads a little during scanning. We need to process the data to create motion-stabilized images. Otherwise, we will not be looking at the same brain area over time.

Spatial smoothing Each voxel is noisy By blurring the image, we can get a more stable signal (neighbors show similar effects, noise spikes attenuated).

Predicted fMRI signal We need to generate a statistical model. We convolve expected brain activity with hemodynamic response to get predicted signal. Neural Signal HRF Predicted fMRI signal =

Predicted fMRI signal We generate predictions for neural responses for the left and right arrows across our dataset. Statistics will identify which areas show this pattern of activity. Several possible statistical contrasts (crucial to inference): Activity correlated with left arrows: visual cortex, bilateral motor. More activity for left than right arrows: contralateral motor.

Voxelwise statistics We compute the probability for every voxel in the brain. We observe that right arrows precede activation in the left motor cortex and right cerebellum.

fMRI signal change is tiny, noise is high Right motor cortex becomes brighter following movement of left hand. Note signal increases from ~12950 to ~13100, only about 1.2% And this is after all of our complicated processing to reduce noise.

Coordinates - normalization Different people’s brains look different ‘Normalizing’ adjusts overall size and orientation Normalized Images Raw Images

Why normalize? Stereotaxic coordinates analogous to longitude Universal description for anatomical location Allows other to replicate findings Allows between-subject analysis: crucial for inference that effects generalize across humanity.

fMRI is notoriously difficult technique Goals for this course fMRI is notoriously difficult technique Sluggish signal Poor signal/noise Must find meaningful statistical contrasts This seminar reveals how to Devise meaningful contrasts Maximize signal, minimize noise Control for statistical errors.

MRI uses very strong magnet and radiofrequencies Safety MRI uses very strong magnet and radiofrequencies 3T= ~x60,000 field that aligns compass Metal and electronic devices are not compatible. MRI scanning makes loud sounds Rapid gradient switching creates auditory noise. Auditory protection crucial. MRI scanning is confined Claustrophobia is a concern.

Summary of Lectures Introduction Physics I: Hardware and Acquisition Physics II: Contrasts and Protocols fMRI Paradigm Design fMRI Statistics and Thresholding fMRI Spatial Processing fMRI Temporal Processing VBM & DTI: subtle structural changes Lesion Mapping: overt structural changes Advanced and Alternative Techniques

There are many tools available for analysis. Different strengths. Which tools There are many tools available for analysis. Different strengths. We predominantly focus on SPM and FSL. These are both free, popular and have good user support. Tool SFN04 SPM 78.5% AFNI 9.1% FSL 7.4% BrainVoyager 4.1% https://cirl.berkeley.edu/view/Grants/BrainPyMotivation

Reporting findings How do we describe anatomy to others? We could use anatomical names, but often hard to identify. We could use Brodmann’s Areas, but this requires histology – not suitable for invivo research. Both show large between-subject variability. Requires anatomical coordinate system.

Relative Coordinates On the globe we talk about North, South, East and West. Lets explore the coordinates for the brain.

Orientation - animals Dorsal back Caudal tail Ventral belly Rostral Caudal Ventral Cranial head Rostral beak Caudal tail Ventral belly

Coordinates – Dorsal Ventral Human dorsal/ventral differ for brain and spine. Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous. Dorsal Ventral Dorsal Ventral Dorsal Ventral

Coordinates – Human Human rostral/caudal differ for brain and spine. Head/Foot, Superior/Inferior, Anterior/Posterior not ambiguous. C R R R C C

Orientation Human anatomy described as if person is standing If person is lying down, we would still say the head is superior to feet.

Anatomy – Relative Directions lateral < medial > lateral Posterior <> Anterior Ventral/Dorsal aka Inferior/Superior aka Foot/Head Ventral <> Dorsal Anterior/Posterior aka Rostral/Caudal Posterior <> Anterior

Coordinates - Anatomy 3 Common Views of Brain: Coronal (head on) sagittal coronal axial 3 Common Views of Brain: Coronal (head on) Axial (bird’s eye), aka Transverse. Sagittal (profile)

Coronal Corona: a coronal plane is parallel to crown that passes from ear to ear

Transverse/Axial: perpendicular to the long axis Example: cucumber slices are transverse to long axis.

Sagittal – ‘arrow like’ Sagittal cut divides object into left and right sagittal suture looks like an arrow. top view

Sagittal and Midsagittal A Sagittal slice down the midline is called the ‘midsagittal’ view. midsagittal sagittal

Oblique Slices Slices that are not cut parallel to an orthogonal plane are called ‘oblique’. The oblique blue slice is neither Coronal nor Axial. Cor Oblique Ax