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Intro PattRecog Simulation Optimization Conclusions
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
Concha Bielza, Pedro Larranaga
Departamento de Inteligencia ArtificialUniversidad Politecnica de Madrid
Discovery Science and Algorithmic Learning Theory - 2009Porto, October 3, 2009
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Introduction
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Introduction
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Introduction
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Neuroscience
Neuroscience, the science of the brainGoal: understand the relationship between the physical brain and the functionalmind
—how we perceive, move, think, and remember
A principle: all behavior is an expression of neural activity
Involves answers to scientific fundamental questions:
How does the brain develop?Are specific mental processes located in specific brain regions?How do nerve cells in the brain communicate with one another?How do different patterns of interconnections give rise to differentperceptions and motor acts?How is communication between neurons modified by experience?How is it altered by diseases or drugs?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Brain
real sagittal view
The most complex organ of our central nervous system and the most complexbiological structure known
Weight = 1.5kg, width = 140 mm, length = 167mm, height = 93mm
Consumes 20 % of total body oxygen
Stops growing at age 18
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Brain regions
regions lobes
Neuroanatomists distinguish main regions. Each has a complex internal structure
Left-right hemispheres covered by a thin layer of gray matter known as thecerebral cortex
Cerebral cortex, the most recently evolved region of the vertebrate brain, dividedinto four areas
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Brain areas: cerebral cortex
Some areas, such as the cortex and cerebellum, consist of layers, folded to fitwithin the available space
In mammals, neocortex: complex 6-layered structure, greatly enlarged inprimates, especially frontal lobe part (in humans, extreme enlargement).Concerned with the most evolved human behavior
Outermost=layer 1, innermost=layer 6
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Functions of each area (as currently understood)Medulla: sensory and motor functions
Cerebellum: fine motor coordination and body movement, posture, and balance(not needed, but it makes actions hesitant and clumsy)
Thalamus: acts as a switching center for nerve messages
Hypothalamus: control of sleep/wake cycles, eating and drinking, hormone
Hippocampus: involved in memory storage
Olfactory bulb: processes olfactory sensory signals
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Functions of cortex (as currently understood)
Occipital lobe: visual information
Temporal lobe: auditory signals, processing language, meaning of words
Parietal lobe: touch, taste, pressure, pain, temperature
Frontal lobe: motor activity, integration of muscle activity, speech, thought
Remaining parts associated with higher thought processes, planning, memory,personality and other human activities
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The human brain
Brain at microscopic level
Composed of neurons, blood vessels, glial cells (supporting cells of the CNS)
Neuron is the basic structural and functional unit of the nervous system –neurondoctrine– (S. Ramon y Cajal, late 19th century)
Just 4 microns thick→ could fit 30,000 neurons on the head of a pin
∼100,000 million= 1011 interconnected neurons
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
3 parts of a neuron
1 Dendrites receive info from another cell and transmit the message to soma2 Cell body (or soma) contains the nucleus, mitochondria and other organelles
typical of eukaryotic cells3 Axon conducts messages away to the next neuron via a specialized structure
–synapse–Axons fill most of the space in the brain→ >150,000 km in the human brain!!
Each neuron connected to 1,000 neighboring neurons
10,000 synaptic connections each→ 3 · 1014 synapses (adult) and 1015 (child)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
Variable in size and shapeNeurons are more varied than cells in any other part of the body
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
Different shapes for different functions?
Pyramidal activate other neurons, sometimes making connections far away→ long axons to carry theelectrical impulses
Interneurons (e.g., chandelier, double bouquet, spiny stellate, basket cells) create network interactionsbetween neighboring neurons→ short dendrites and axons
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
Synapse: junction between 2 neurons
Messages travel within the neuron as electrochemical impulses called actionpotentials, lasting less than a thousandth of a second at 1-100 m/s
Action potential arrives at a synapse and causes a chemical called aneurotransmitter, stored in small synaptic vesicles, to be released
Neurotransmitter binds to receptor molecules in the membrane of the target cell
Excitatory receptors (↑ rate of action potentials in the target cell) vs. inhibitory
Parkinson’s disease has a deficiency of the neurotransmitter dopamine. Alcoholweakens connections between neurons. Drugs such as caffeine, nicotine, heroin,cocaine, Prozac, act on some particular neurotransmitters
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
SynapseYour brain generates nearly 25 watts of power while you’re awake, which isenough to light up a light bulb
—-See video
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
The neuron
Special parts involved in synapses: spines
Spines are found on the dendrites of most principal neurons in the brain
Synapses typically formed on dendritic spines (discovered by Ramon y Cajal)
Many different shapes too. Spines with strong synaptic contacts typically have alarge spine head
Dendritic spines are very “plastic”, i.e. change significantly in shape, volume, andnumber in small periods
Spine plasticity is implicated in motivation, learning, and memory
—-See video
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Technology to help
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Technology to help
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Technology to help
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Observing the neurons
Optical (or light) microscope. Stain the tissue
Magnify image up to 2000 times Golgi’s method (1873) stains some cellsat random avoiding much overlap
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Observing the neurons
Modern electron microscope
Magnify image up to 2 million times 3D from multiple 2D images
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
“Visualizing” mental activities from brain images
Electroencephalography (EEG): electrical activity directly
with electrodes predictive maps
Frequency analysis of the signals in the different bands to reveal patterns
Epilepsy, sleep disorders (narcolepsy), for completely paralysed users (ALS) tointeract with the environment through imaginary hand movement
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
“Visualizing” mental activities from brain images
Electrical activity indirectly
Computed Axial Tomography (CAT) Positron Emission Tomography (PET) Magnetic Res. Imaging (MRI)
Functional NIR Single Photon Emission Computed Functional MRI (fMRI)
Spectroscopy (fNIRS) Tomography (SPECT)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
“Visualizing” mental activities from brain images
Electrical activity indirectly
fMRI fNIRS 3D (jazz) Integration
Changes in blood flow (SPECT, fMRI) or metabolic activity (PET).Reduced flow, less energy consumed = injured sites. Flow changes tocompensate for the increased metabolic demands of the area being used
Which brain structures are activated –structural info– and how –functional info–when performing different tasks (sound, movie, picture, press a button,improvising jazz...). Cognitive processes, causes of neuro-degenerative diseases
EEG: high temporal resolution (millisecond), but poor spatial resolution. fMRI:great 3D spatial resolution, but slow temporal resolution (seconds and minutes)→ Integrate methods
3D images —See 2 animations
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Activate/inactivate genes...
in specific parts of the brain and see the effects
Identify which genes contribute to disorders and predict an individual’ssusceptibility
We can experiment with much simpler systems because many properties ofneurons are maintained across species
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Efforts today?
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Efforts today?
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Efforts today?
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Reverse-engineer the brain
One of the 14 grand challenges for Engineeringthat will influence science and technology in the 21st century –National Academy ofEngineering in 2009, at the request of the NSF: http://www.engineeringchallenges.org
1 Make solar energy economical2 Provide energy from fusion3 Develop carbon sequestration methods4 Manage the nitrogen cycle5 Provide access to clean water6 Restore and improve urban infrastructure7 Advance health informatics8 Engineer better medicines9 Reverse-engineer the brain (the 4th in Internet votes)10 Prevent nuclear terror11 Secure cyberspace12 Enhance virtual reality13 Advance personalized learning14 Engineer the tools of scientific discovery
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Reverse-engineer the brain
One of the 14 grand challengesEngineers and neuroscientists should work together
Brain and computers: devices for processing info, but differently (optimized fordifferent things: face recognition vs. many sums)
Using computers to study the brain: model groups of neurons or specific parts(eqns describing electrochemical activity)
...but not a realistic computational simulation of the entire nervous system tounderstand how brains perform computation
Attempts to build (powerful) computers that operate on brainlike principles,replicating its communication skills, but to date without success
Supercomputing designs inspired by brain-like connectivity (e.g. IBM Blue Gene)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Reverse-engineer the brain
One of the 14 grand challenges
Some advances: neural prostheses to restore damaged hearing (cochlearimplants), sight (artificial retinas), allow movement, communicationBCIs: read the thoughts of paralyzed patients and translate into commands likemoving a cursor on a computer screen
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Blue Brain Project
Founded in 2005 by the Brain and Mind Institute of the Ecole Polytechnique inLausanne (Switzerland), directed by Henry Markram
An attempt to reverse-engineer the mammalian brain, in order to understandbrain function and dysfunction through detailed simulations
Between 1995 and 2005, Markram had mapped the types of neurons and theirconnections in a rat neocortical column, the smallest functional unit of theneocortex
In Dec’2006, the column was simulated (only 10,000 neurons; 60,000 in humans)
∼2 million columns in humans, 2mm tall
Markram predicts it to be built within the next 10 years
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
UPM+IC as partners of BBP
At the end of 2008, Universidad Politecnica de Madrid (UPM) and InstitutoCajal (IC) from the Spanish Research Council –subproject Cajal Blue Brain,till 2018
UPM, including Madrid Supercomputing and Visualization Center: data analysis,optimization and visualization
IC: morphology and function of neuronal cells
Cooperation from multiple disciplines: image analysis, machine learning,neuroscience, network analysis, high-performance computing...
Problems where machine learning can contribute –some already started(highlighted as green blocks during the talk)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
UPM+IC as partners of BBP
At the end of 2008, Universidad Politecnica de Madrid (UPM) and InstitutoCajal (IC) from the Spanish Research Council –subproject Cajal Blue Brain,till 2018
UPM, including Madrid Supercomputing and Visualization Center: data analysis,optimization and visualization
IC: morphology and function of neuronal cells
Cooperation from multiple disciplines: image analysis, machine learning,neuroscience, network analysis, high-performance computing...
Problems where machine learning can contribute –some already started(highlighted as green blocks during the talk)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
UPM+IC as partners of BBP
UPM IC
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Computational intelligence
Supervised classification
X1 . . . Xn C(x (1), c(1)) x (1)
1 . . . x (1)n c(1)
. . . . . . . . .
(x (N), c(N)) x (N)1 . . . x (N)
n c(N)
x (N+1) x (N+1)1 . . . x (N+1)
n ???
Unsupervised classification
X1 . . . Xn
x (1) x (1)1 . . . x (1)
n. . . . . .
x (N) x (N)1 . . . x (N)
n
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Computational intelligence
Supervised classification
X1 . . . Xn C(x (1), c(1)) x (1)
1 . . . x (1)n c(1)
. . . . . . . . .
(x (N), c(N)) x (N)1 . . . x (N)
n c(N)
x (N+1) x (N+1)1 . . . x (N+1)
n ???
Unsupervised classification
X1 . . . Xn
x (1) x (1)1 . . . x (1)
n. . . . . .
x (N) x (N)1 . . . x (N)
n
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Computational intelligence
Discriminant analysis
Classification trees
Bayesian classifiers
K-nn classifiers
Logistic regression
Support vector machines
Bayesian networks
Artificial neural networks
Rule induction
Markov random fields
Clustering
Regularized linear regression
Feature subset selection
Optimization techniques
Simulation models
...
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Computational intelligence
DifficultiesVariable C continuous or discrete
Variables X ’s too
Variable C is a vector
Noise in the data
2D and 3D data
Dynamic data
High-dim data (e.g. 105 voxels)
Much more variables than samples
Different scales: macrostructures (circuits, brain, CNS), microstructures (neuron,molecule)
Multimodal data: electrochemical, morphological, microarrays, neuroimaging...
Highly connected elements
High-processing requirements
Difficult to generalize (models for each subject?)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions
Ethics
Discovering how the brain works will offer rewards beyond building smartercomputers...
A world without pain and depressions
Reading others’ minds
A world without lies
Turn murderers into good people. Fuzzy frontiers. Difficult to program a brain tobe a banker or a robber⇒ [what is B. Madoff?]
Modify people’s brains. Make them to see things they in fact they don’t see.Delete selectively some traumatic memories [war experiences]
Alter children brains to influence their education, choosing their personality andprofessional vocation
Where to put the limits?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Interdisciplinary field
Neuroimaging as an interdisciplinary field at the intersection of imageprocessing, computer science, physics, statistics and neurosciences
Investigate mental activities in healthy as well as in diseased situations
Two classes of images:Anatomical or structural images: Brain structures MRI and SPECTFunctional images: changes –measured directly (MEG) or indirectly (fMRI) and (PET)–in neuronalpopulation activity, provoked by sensory stimulations or during cognitive tasks
Topics
Introduction: Classes of images and examples of problems to be solved
PreprocessingVoxel (pixel) (set) selection. Region of interest (ROI)Dimensionality reduction via feature extractionDimensionality reduction via feature selection
ModelingDiscovering associationsUnsupervised classificationSupervised classificationRegression3D ultrastructure of the cortical column
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Image. Voxels. Spatial. Temporal. Patients
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Introduction. Examples of problems to be solved
Differential diagnosis between Alzheimer’s disease (AD)and frontotemporal dementia (FTD), the two most frequentneurodegenerative cognitive disorders
Diagnosis of early states of Alzheimer dis-ease
Separation of cocaine addicted subjectsversus non-drug controls subjects
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Introduction. Examples of problems to be solved
Gender-related differentiation of the corpuscallosum based on discriminative morpho-metric characteristics between anatomicalbrain structures
Brain reading: classify patterns of fMRIactivation
Predicting activation of the brain afterhearing a word
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Introduction. Difficulties in modeling
Few examples: 10 - 100
Many features: 104 - 105
Noise:
The scannerThe brainThe patient
Intra-variabilityInter-variability
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Preprocessing
Spatial normalization because of variations of brain volume, shape and positionfrom one patient to another (Statistical Parametric Mapping Software (SPM2))
High dimensional data (e.g. in 3D images: 128 × 128 × n, where n is thenumber of slices)
Voxel (pixel) (set) selection. Region of interest (ROI)Dimensionality reduction via feature extractionDimensionality reduction via feature selection
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Preprocessing. Univariate voxel selection. Where is the signal?
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
NeuroimagingPreprocessing. Univariate voxel selection
Suboptimal if correlation among voxels carries important information
Filter approaches: provide a ranking of voxels
t-test for detecting active voxels (Friston et al., 1995), scoring the voxel byits corresponding p-value (valid for a 2 class problem)F -test for classification problems with more than 2 class labelsNon-parametric tests (e.g. Mann-Whitney), after rejecting the assumptionof Gaussian densities with the Lilliefors test
Wrapper approaches: provide a ranking of voxels
Accuracy: scores the voxel by how accurately a classifier –based only onthe voxel– is able to predict correctly the examplesSearchlight accuracy: the same as accuracy but instead of using the datafrom a single voxel, we use the data from the voxel and its immediatelyadjacent neighbours in three dimensions
Multiple comparison criteria
From the ranking decide on how many of the top-ranked voxels to useMultiple testing problem: (a) adjusting by (Bonferroni (1936)) or by(Benjamini, Hochberg (1995)) corrections (b) controlling the fraction offalse positive rate (false discovery rate) (Genovesse et al. 2002)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
NeuroimagingPreprocessing. Voxel sets and region of interest (ROI)
Voxel sets: Dynamic recursive partitioning (DRP) Kontos et al. (2009)
DRP partitions the two-dimensional image adaptively into progressivelysmaller subregions until statistically significant discriminative regions aredetected
By performing statistical tests on group of pixels rather than on individualpixels, the number of statistical tests is effectively reducedThe average value over the group of pixels becomes the value of thevariable (feature)
Region of interest (ROI)
ROIs as the result of the process of partitioning the digital image intomultiple segments (set of voxels (pixels))Feature value as the average of the values over the voxels (pixels) in suchROIClustering, edge detection, level set, watershed transformation, .....
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Preprocessing. Dimensionality reduction via feature extraction
Ignore the class label
Principal Component Analysis (PCA) (Hansen et al. 1999): the Meigenvectors with largest eigenvalues of the covariance matrix (fMRIimages: n ≥ 104 and Σ size is n2)
Singular Value Decomposition (SVD) (Bai et al. 2007): Given Xn×m thedata set, obtain Zn×k and Wk×m with k << n such thatminZ ,W ||X − ZW ||2 where k is the rank of the approximation
Independent Component Analysis (ICA) (McKeown et al. 1998):
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Preprocessing. Dimensionality reduction via feature extraction
Class label taken into account
Partial Least Squares (PLS) (McIntosh and Lobaugh, 2004) finds a linearmodel by projecting the predicted variables Yn×p and the observablevariables Xn×m to a new space (bilinear factor model). Given Xn×m andYn×p find T ,P,Q,E ,F such that:
X = TP t + E Y = TQt + F
where Tn×l is the factor matrix, and Pm×l , and Qp×l are the loadingmatrices. E and F are the error terms
Support Vector Decomposition Machine (SVDM) (Pereira and Gordon,2006):
minZ ,W ,Θ||X − ZW ||2 + f (Θ)
This objective trades reconstruction error (the first term) with a bound onclassification error (the second term)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Preprocessing. Dimensionality reduction via feature selection
Simulated Annealing (Yeh, Fu, 2005): more than 100,000 voxels
Recursive Feature Elimination (Li et al. (2007)): at each iteration, the worsefeature was identified and removed (SVM)
Fisher Criterion Score, Relief-F, Genetic Algorithms, Ensemble (Jim et al.(2009)): from 65,166 features to dozens (SVM)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Modelling
Gaussian naive Bayes: (Mitchell et al. 2004; Pereira et al., 2009; Zhang et al., 2005)
General linear model: (Bly, 2001; Friston et al., 1995)
Hidden Markov models: (Hojen-Gorensen et al., 1999)
Kernel logistic: (Horn et al., 2009)
k -nearest neighbor: (Fletcher-Heath et al., 2001; Haxby et al., 2001; Horn et al., 2009; Mitchell et al. 2004;Zhang et al., 2005)
Linear discriminant analysis: (Cox and Savoy, 2003; Horn et al., 2009; Pereira et al., 2009; Zhang et al.,2005)
Logistic regression: (Desco et al., 2001; Horn et al., 2009; Pererira et al., 2009)
Mixture of Gaussians (EM): (Desco et al., 2001; Tohka et al., 2007)
Multilayer perceptron: (Chaplot et al, 2006; El-Sayed et al., 2009; Horn et al., 2009)
Probabilistic graphical models: (Burge and Lane, 2007; Chen and Herskovits, 2007; Herkovits et al., 2004;Sun and Tang, 2009)
Sparse regression: (Carroll et al. 2009; Valdes-Sosa et al., 2005; van Gerven et al., 2009; Yamashita et al.,2008)
Support vector machine: (Chaplot et al., 2006; Cox and Savoy, 2003; El-Sayed et al., 2009; Horn et al.,2009; Jim et al., 2009; Li et al., 2007; Mitchell et al. 2004; Pereira et al., 2009; Zhang et al., 2005)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Discovering associations. Probabilistic graphical model (Burge and Lane 2007)
Bayesian network representing the relationships among ROIsNodes: groups of voxels together in a ROI (hierarchically related; activationas the average of voxel activation)Conditional probability tables: smoothing (small sample)Structure learning: Bayesian Dirichlet equivalence criteria (BDe)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
NeuroimagingUnsupervised classification. Genetic algorithms for finite mixture models (Tohkaet al., 2007)
Overcome the dependency of the expectation-maximization (EM) algorithmregarding its initialization
Finite mixture model: Given xi ∈ Rd with i ∈ {1, ..., n}, n number of voxels
f (x|Θ) =K∑
j=1
πj fj (x|θj )
To find Θ the maximum likelihood estimation of Θ the following optimizationproblem has to be solved: Θ = arg maxΘL(x1, ..., xn|Θ)
The EM algorithm is an standard approach (trapped in local optima)
Genetic algorithms (GAs) with real code
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Supervised classification. Comparing classifiers and classifier versusphysicians (Horn et al. 2009)
Automatic method for returning the probability that a patient suffers fromAlzheimer’s disease (AD) or frontotemporal dementia (FTD)
82 AD and 91 FTD patients characterized by 116 descriptors (average activity inROIs)
Single photon emission computed tomography (SPECT) images
Leave-one-out cross-validation
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
NeuroimagingRegression. Sparse Regression (Carroll et al. 2009)
2007 Pittsburgh Brain Activity Interpretation Competition (PBAIC)
fMRI data for the 33,000-35,000 voxels over 704 time points, 24 real-valuedresponse variables
Elastic net (Zou and Hastie, 2005): Lλ1,λ2= ||y− Xβ||22 + λ1||β||1 + λ2||β||22
LASSO (Tisbshirani, 1996): λ2 = 0Ridge regression (Hoerl and Kennard, 1970): λ1 = 0Ordinary linear regression: λ1 = 0 and λ2 = 0
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
3D ultrastructure of the cortical columnDecipher the wiring diagram and themap of connections at the synapticlevel of the cortical column
Electron microscopy 3D reconstructionmethods
A level of detail never reached before
Neuropil and synaptic connectivityUltrastructure of the neuropil: number and proportion of synapses; length,diameter and volume of axons, dendrites and glial; density of synaptic vesiclesper axon
Maps of synaptic connectivity: spatial distribution of synapses and spines;number of synapses per neuron —See 2 videos
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Neuroimaging
Modelling. Performance versus interpretabilityPrediction performance of a model is a good measure ofmodel validityIn the sciences, interpretation remains key when applyingpredictive modeling techniquesPrediction accuracy alone does not guarantee validity
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics
Genomics, proteomics, metabolomics. Integration of data-omics refers to Biology fields in which the objects of studyconforms a totalityGenomics (study of genome), proteomics (study ofproteome), metabolomics (study of metabolome)Neurological diseases: Alzheimer, Parkinson, Huntington,Amyotrophic Lateral Sclerosis, Creutzfeldt-Jakob...DNA microarrays, microRNA molecules, single nucleotidepolymorphisms, mass spectrometry, clinical data⇒Integrate them into a single research to study the possibleinfluences and interactions among them
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease (AD)
Alzheimer’s disease casesNeurological disorder primarily affecting the elderly that manifests throughmemory disorders, cognitive decline and loss of autonomy
17-20 million individuals affected worldwide
Every 70 seconds, someone develops Alzheimer’s
Alzheimer’s is the seventh-leading cause of death
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease (AD)
Alzheimer’s studiesDNA microarrays: Guo et al. (2001), Lamb et al. (2006), Small et al. (2005), Konget al. (2009)
MicroRNA molecules: Lukiw et al. (2008), Wang et al. (2008)
Single nucleotide polymorphisms (SNP): Williams et al. (2009), Amouyel et al.(2009)
Mass spectrometry: Lewczuk et al. (2004), Lopez et al. (2005)
Clinical data (like Mini-Mental Status Examination score): Ray, Zhang (2009) withmicroarrays
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease (AD)
Alzheimer’s studiesDNA microarrays: Guo et al. (2001), Lamb et al. (2006), Small et al. (2005), Konget al. (2009)
MicroRNA molecules: Lukiw et al. (2008), Wang et al. (2008)
Single nucleotide polymorphisms (SNP): Williams et al. (2009), Amouyel et al.(2009)
Mass spectrometry: Lewczuk et al. (2004), Lopez et al. (2005)
Clinical data (like Mini-Mental Status Examination score): Ray, Zhang (2009) withmicroarrays
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Genomics-AlzheimerGenetically complex and heterogeneous
Two types of neuropathologic lesions: (i) neurofibrillary degeneration fromintraneuronal accumulation of certain proteins; (ii) amyloid deposits fromextracellular accumulation of amyloid plaques, composed of Aβ peptides
Processes leading to form these lesions and their combined association with ADare not adequately understood
APOE on chromosome 19 is the only confirmed susceptibility locus for late-onsetAD (locus=specific location of a gene or DNA sequence on a chromosome)
Genes may have a role in more than 60 % of AD susceptibility and APOE mayaccount for as much as 50 % of this genetic susceptibility
More than 550 other genes proposed as candidates for AD susceptibility, but notconfirmed to have a role in AD pathogenesis
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Genomics-AlzheimerGenetically complex and heterogeneous
Two types of neuropathologic lesions: (i) neurofibrillary degeneration fromintraneuronal accumulation of certain proteins; (ii) amyloid deposits fromextracellular accumulation of amyloid plaques, composed of Aβ peptides
Processes leading to form these lesions and their combined association with ADare not adequately understood
APOE on chromosome 19 is the only confirmed susceptibility locus for late-onsetAD (locus=specific location of a gene or DNA sequence on a chromosome)
Genes may have a role in more than 60 % of AD susceptibility and APOE mayaccount for as much as 50 % of this genetic susceptibility
More than 550 other genes proposed as candidates for AD susceptibility, but notconfirmed to have a role in AD pathogenesis
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
DNA microarraysSimultaneously measure thousands of gene expression levels
The most common although recent (2000) technology for AD studies
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease (AD)
Microarrays: Kong et al. (2009)Extract the most informative features
⇒ 5 severe AD samples + 8 controls; 3,617 genes
⇒ PCA vs. ICA
⇒ Found 50 genes up-regulated and 37 down-regulated in severe AD
⇒ Unsupervised hierarchical clustering to validate the efficiency of ICA outputs todiscriminate controls and AD samples
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small et al. (2005)Microarrays as a powerful tool for isolating molecular differences betweenhealthy and diseased tissue, but with analytic challenges when applied to brain
Idea: generate microarray data selectively from the brain site most vulnerable toAD to maximice expression differences between AD and controls
Generate data also from a neighboring region relatively resistant to AD
Hippocampus is particularly vulnerable to AD (postmortem studies, fMRI data inliving subjects)
Regions: entorhinal cortex (EC) forms the main input to the hippocampus, themost vulnerable; dentate gyrus (DG), the most resistant
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small et al. (2005)Assume hypotheses: EC and DG are relevant regions and differences in ECbetween AD and controls are known to be age independent
Search for the variable(s) that best matches these hypotheses
Is some molecule differentially expressed in EC over DG, comparing AD tocontrols?
⇒ 6 diseased brains + 6 controls, obtained at autopsy⇒ 24 tissue samples
⇒ 7,610 molecules
⇒ Several ANOVA using group (AD vs. control), age (33-98 years old) andexpression levels (EC/DG)
⇒ Identified a molecule (VPS35) whose expression is abnormal in AD: it is reducedin the EC region (and it regulates Aβ peptide levels)
⇒ Validate the finding using cell culture studies
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Re-analyze the data differently to gain robustness (small sample size!)
Find out explicit new (or validate old) biological relationships and genes notpreviously reported
⇒ Learn a Bayesian network classifier (supervised classification task):Class={AD,control}. We use kDB structures
Naıve Bayes-based classifiers
Naıve Bayes - All variables
Selective naıve Bayes -Remove irrelevant var
Tree augmented naıveBayes - Tree with all var
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Re-analyze the data differently to gain robustness (small sample size!)
Find out explicit new (or validate old) biological relationships and genes notpreviously reported
⇒ Learn a Bayesian network classifier (supervised classification task):Class={AD,control}. We use kDB structures
Naıve Bayes-based classifiers
Naıve Bayes - All variables
Selective naıve Bayes -Remove irrelevant var
Tree augmented naıveBayes - Tree with all var
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Re-analyze the data differently to gain robustness (small sample size!)
Find out explicit new (or validate old) biological relationships and genes notpreviously reported
⇒ Learn a Bayesian network classifier (supervised classification task):Class={AD,control}. We use kDB structures
Naıve Bayes-based classifiers
Naıve Bayes - All variables
Selective naıve Bayes -Remove irrelevant var
Tree augmented naıveBayes - Tree with all var
C
X1 X4
X2 X3
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Re-analyze the data differently to gain robustness (small sample size!)
Find out explicit new (or validate old) biological relationships and genes notpreviously reported
⇒ Learn a Bayesian network classifier (supervised classification task):Class={AD,control}. We use kDB structures
Naıve Bayes-based classifiers
Naıve Bayes - All variables
Selective naıve Bayes -Remove irrelevant var
Tree augmented naıveBayes - Tree with all var
C
X1 X4
X2 X3
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Re-analyze the data differently to gain robustness (small sample size!)
Find out explicit new (or validate old) biological relationships and genes notpreviously reported
⇒ Learn a Bayesian network classifier (supervised classification task):Class={AD,control}. We use kDB structures
Naıve Bayes-based classifiers
Naıve Bayes - All variables
Selective naıve Bayes -Remove irrelevant var
Tree augmented naıveBayes - Tree with all var
C
X1 X4
X2
X3
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
kDBExtension to allow a variable to have at most k parents (excluding the class)
Search of structure based on the (conditional) mutual information metric
0-dependence Bayesian classifier← naıve Bayes classifier1-dependence Bayesian classifier← tree augmented naıve Bayes(n − 1)-dependence Bayesian classifier← the complete Bayesianclassifier (no conditional independencies in the structure)
2DB
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Induce many Bayesian classifiers by bootstrapping and assign confidence levelst to the arcs (# times each one must appear) to find robust gene interactions
Different confidence level t , different model Gt (from simple structures with a fewarcs when t ↑, to dense graphs when t ↓)⇒ Hierarchy of autoinclusive models G1 ⊇ G2 ⊇ ...Approach is a consensus feature selection on the final graph –gene interactionnetwork–
Consensus conclusions have shown to provide good results in microarray data
‘Robust arc identification’ algorithm (Armananzas et al, 2008)
Step 1. Repeat B times
Step 1.1. Randomly sample N instances with replacement
Step 1.2. Select an optimal feature subset and reduce the data set (CFS)
Step 1.3. Learn a kDB model
Step 2. Compute frequencies of all the included arcs out of the B models
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Induce many Bayesian classifiers by bootstrapping and assign confidence levelst to the arcs (# times each one must appear) to find robust gene interactions
Different confidence level t , different model Gt (from simple structures with a fewarcs when t ↑, to dense graphs when t ↓)⇒ Hierarchy of autoinclusive models G1 ⊇ G2 ⊇ ...Approach is a consensus feature selection on the final graph –gene interactionnetwork–
Consensus conclusions have shown to provide good results in microarray data
‘Robust arc identification’ algorithm (Armananzas et al, 2008)
Step 1. Repeat B times
Step 1.1. Randomly sample N instances with replacement
Step 1.2. Select an optimal feature subset and reduce the data set (CFS)
Step 1.3. Learn a kDB model
Step 2. Compute frequencies of all the included arcs out of the B models
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800 <798790
...700600500400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798 <790
...700600500400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790 <
...700600500400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700 <600500400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700600 <500400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700600500 <400
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700600500400 <
...100
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700600500400
...100 <
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Reliable-kDB classifier –An example
Gt
t800798790
...700600500400
...100 <
...1
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Gene-interaction network(s) of high reliability
⇒ 2 different analysis: EC-AD vs. EC-Control (12 samples, binary supervisedclassif problem) and EC-AD vs. EC-Control vs. DG-AD vs. DG-Control (24samples, multiclass)
⇒ k = 4,B = 10, 000, discretize into 3 intervals (up-regulated, down-regulated,baseline). t = 1, 000
⇒ 2 disconnected graphs. Genes related to neurological diseases: Huntington,Parkinson, bipolar disorder, depression→ related to AD? Some paper (2007) for Parkinson’s
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
Microarrays: Small and us, 2009 In progress
Gene-interaction network(s) of high reliability
⇒ 2 different analysis: EC-AD vs. EC-Control (12 samples, binary supervisedclassif problem) and EC-AD vs. EC-Control vs. DG-AD vs. DG-Control (24samples, multiclass)
⇒ k = 4,B = 10, 000, discretize into 3 intervals (up-regulated, down-regulated,baseline). t = 1, 000
⇒ 2 disconnected graphs. Genes related to neurological diseases: Huntington,Parkinson, bipolar disorder, depression→ related to AD? Some paper (2007) for Parkinson’s
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
SNP (single-nucleotide polymorphism)A SNP is a DNA sequence variation occurring when a single nucleotide –A, T, C,or G– in the genome differs between members of a species (or between pairedchromosomes in an individual)
Example: 2 sequenced DNA fragments from different individuals, AAGGGA toAAGGAA, contain a difference at a single base-pair location
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
SNP: New markers published Sep’2009 Nature GeneticsIn addition to APOE locus, identify other loci associated with the risk oflate-onset AD
Williams et al. (2009):
529,218 SNPs involving 3,941 cases and 7,848 controlsCollections from UK, Germany, USATwo loci gave replicated evidence of association: CLU on chromosome 8(SNP rs11136000) and PICALM on chromosome 11 (SNP rs3851179)
Amouyel et al. (2009):
537,029 SNPs genotyped in 2,032 cases and 5,328 controlsCollections from France, Belgium, Finland, Italy and SpainLoci: CLU (SNPs rs2279590, rs11136000, rs9331888) and CR1 onchromosome 1 (SNPs rs6656401, rs3818361)
Information on age, sex, geographical region and disease status
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
-omics: Alzheimer’s disease
SNP: New markers published Sep’2009 Nature Genetics
Chromosomalposition vs.− log10 P value
Threshold (redline) atP ≤ 9.4×10−10
with 13 SNPs
Filter FSS + logistic regression (which optional PCA to account for possiblepopulation stratification)
Additional and larger studies are required
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Introduction
Golgi and Ramon y CajalNeuromorphology intensively studied after the discovery of Golgi method (Golgi,1873) named the black reaction (la reazione nera), allowing to see the entireneuron
Ramon y Cajal (1911) developed the neuron doctrine pointed out the variety ofpatterns of neurons depending on particular places and emitted hypothesis onthe role that could be played by particular forms
A better knowledge of the different neuronal populations that compose thisheterogeneous brain structure may therefore contribute to elucidating theirspecific role
Traditionally neuronal cell types have been classified using qualitative descriptorswith criteria that vary across investigators
In the recent decade several attempts to classify neurons quantitatively(objective classification) from multimodal information (physiological, molecular,morphological) have been developed
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Introduction
Golgi and Ramon y Cajal
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Introduction
Naming neuronsLorente de No (1938) qualitatively described classes ofinterneuronsRamon-Moliner (1958) defined types of neurons accordingto their dendritic arborisationsTyner (1975). The naming of neurons: applications oftaxonomic theory to the study of cellular populations. BrainBehaviour Evolution, 12, 75-96Rowe, Stone (1977). Naming the neurons. Classificationand naming of neurons of cat retinal ganglia cells. BrainBehaviour Evolution, 14, 185-216
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
Neurolucida (morphological variables)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neuronsNeurolucida (morphological variables)
DENDRITIC VARIABLES AXONAL VARIABLES SOMATIC VARIABLESnumber of dendrites axonal node total somatic perimeterdendritic node total total axonal length somatic areatotal dendritic length ratio of axonal length to surface somatic aspect ratioaverage length of dendrites highest order axon segment somatic compactnesstotal surface area of dendrites axonal torsion ratio somatic form factorratio of dendritic length to surface axonal planar angle ave somatic roundnessdendritic torsion ratio axonal planar angle stdv relative distance to piadendritic planar angle ave axonal local angle avedendritic planar angle stdv axonal local angle stdvdendritic local angle ave axonal spline angle avedendritic local angle stdv axonal spline angle stdvdendritic spline angle ave ave tortuosity of axonal segmentsdendritic spline angle stdv stdv of tortuosity of axonal segmentsave tortuosity of dendritic seg axonal segment length avestdv of tortuosity of dendritic seg axonal segment length stdvdendritic segment length ave ave tortuosity of axonal nodesdendritic segment length stdv stdv tortuosity of axonal nodesave tortuosity of dendritic nodes number axonal sholl sectionsstdv of tortuosity of dendritic nodes axonal sholl length at 100µmnumber of dendritic sholl sections axonal sholl length at 200µmdendritic sholl length at 50µm axonal sholl length at 300µmdendritic sholl length at 100µm axonal length density2dendritic sholl length at 150µm axonal node density2convex hull dendrite area convex hull axon areaconvex hull dendrite perimeter convex hull axon perimeterconvex hull dendrite volume convex hull axon volumeconvex hull dendrite surface area convex hull axon surface areahighest order dendritic segment k -dim axon (fractal analysis)k -dim dendrites (fractal analysis) total surface area of axon
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
NeuroMorpho.Org
Inventory of digitally reconstructed neurons
Morphological reconstructions are collected, published, and shared
Over two-dozen labs
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
NeuroMorpho.Org
CELL TYPE NUMBER CELL TYPE NUMBERAdapting Non-pyramidal cell 1 Amacrine cell 1Basket 64 Biploar 6Bitufted 47 Calbindin 18Calretinin 29 Chandelier 4Cholecystokinin 14 Climbing Fiber 2Cone 25 Dopamine 4DSCT 8 Double bouquet 9Ganglion 64 Granule 122Ia inhibitory 8 Large aspiny cell 146Lateral horn neuron 19 Martinotti 36Medium spiny 239 Modulated 12Motoneuron 100 Multipolar 29Neurogliaform 30 Neuropeptide Y 11NotReported 514 Parvalbumin 28Purkinje 10 Pyramidal 3316Relay 5 Renshaw 4Reticular 1 Somatostatin 33Stellate 33 Tangential 13Tripolar 25 Uniglomerular projection neuron 233Von economo 29
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
Main interneuron types of layers II-IV
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
State of the art methodsHierarchical clustering (Ward’s method + Euclideandistance)
Iterated cluster analysis
K -meansEM algorithm with Gaussian finite mixture
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
Hierarchical clustering (Ward’s method + Euclidean distance)
AUTHORS NEURONS CLUSTERS VARIABLES FSSCauli et al. (2000) 60 rat neocortical 3 clusters 14 electrophysiology NoPNAS interneurons Thorndike 9 cellular receptors
21 glutamate receptors
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
Hierarchical clustering (Ward’s method + Euclidean distance)
AUTHORS NEURONS CLUSTERS VARIABLES FSSKozloski et al. (2001) 15 mouse primary 4 clusters 18 morphological PCAScience visual cortex 11 selected
from layer 5 5 PCs
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neuronsHierarchical clustering (Ward’s method + Euclidean distance). Iterated clusteranalysis
AUTHORS NEURONS CLUSTERS VARIABLES FSSHelmstaedter et al. (2009) 39 interneurons 9 clusters 11: electrical + NoCerebral Cortex + 4 pyramidal Cutoff (4 pyramidal) dendrite +
from layer 2, 3 Weights random axonal
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neuronsHierarchical clustering (Ward’s method + Euclidean distance) and K -means
AUTHORS NEURONS CLUSTERS VARIABLES FSSKaragiannis et al. (2009) 200 NPY cells Hierarchical (6) 1 laminar location NoThe Journal of interneurons K -means (5) 10 molecularNeuroscience Silhouette 32 electrophy.
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neurons
Hierarchical clustering (Ward’s method + Euclidean distance)
AUTHORS NEURONS CLUSTERS VARIABLES FSSWong et al. (2002) 36 drosophila 6 clusters 21 morpho. PCACell Protocerebrum 17 selec.
Pyramidal cells 7 PCsTsiola et al. (2003) 158 mouse primary 5 clusters 33 morpho. PCAThe Journal of Comparative visual cortex 14 selec.Neurology from layer 5 5 PCsHelmstaedter et al. (2009) 51 interneurons 4 clusters 5 morpho. NoCerebral Cortex layer 2,3 Thorndike axonal
rat cortexHelmstaedter et al. (2009) 64 interneurons 7 clusters 101 morpho. NoCerebral Cortex layer 2,3 validity dendritic
rat cortex 5 pyramidal
Tamas et al. (2003), Science
Karube et al. (2004), Journal of Neuroscience
Halabisky et al. (2006), Journal of Neurophysiology
Gallopin et al. (2006), Cerebral Cortex
David et al. (2007), Journal of Neuroscience
Andjelic et al. (2009), Journal of Neurophysiology
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Clustering methods for neuronsHierarchical clustering + K -means + EM Gaussian finite-mixture. Guerra et al.(2009)
231 cells from cerebral cortex: 216 interneurons (13 of them Chandelier) + 15pyramidal (Columbia University)
Neurolucida: 67 variables
Chandelier and pyramidal used for validating the results
No feature subset selection
Hierarchical: Unable to group Chandelier neither pyramidal neurons
K -means: 12 of the Chandelier in the same group, the pyramidal no more than 3in the same group
EM: 12 of the Chandelier in the same group, 5 of the pyramidal in the same group
Feature subset selection: from 67 to 48 variables
Hierarchical: bad results with both types of cells
K -means: almost all Chandeliers in the same group, 8 of the pyramidal in thesame group
EM: all Chandeliers in the same group, 9 of the pyramidal in the same group
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons
It is expected that in the near future (with the help of electron microscopy) thenumber of neurons digitally reconstructed will increase
Helpful to have a supervised classification model to automatically classify theneurons
The two principal neuronal types of the cerebral cortex are:
Pyramidal cells (≈ 80 % of the total population)Interneurons (≈ 20 % of the total population): a heterogeneous group
The different approaches will be presented in two groups:
Supervised classification of neurons via clusteringSupervised classification of neurons via supervised methods
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons via clustering
AUTHORS CLASSES METHOD VARIABLES FSSBenavides et al. (2005) 90 (30× 3) pyramidal Hierarchical: 156 morphological PCACerebral Cortex Three cortical regions Ward’s method 11 features
Mouse neocortex Euclidean distance
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons via clustering
AUTHORS CLASSES METHOD VARIABLES FSSDumitriu et al. (2007) 39 interneurons Hierarchical: 98 morpho. + PCA:Cerebral Cortex 3 subtypes: Ward’s method 8 electr. 61 morpho.
PV, NPY, and SOM Euclidean distance 9 morpho. + 4 electr.
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons via supervised methods
AUTHORS CLASSES METHOD VARIABLES FSSMarin et al. (2002) 161 projection cells LDA 37 morpho. + 15 selectedCell 10 classes 40 repeated 5-cv 8 electr.
Error 7.8± 0.01
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons via superv. methods. Guerra et al. (2009)
Objective: Machine learning classification methods for discriminate betweeninterneurons and pyramidal cells
Database containing 327 cells (199 interneurons + 128 pyramidal) fromColumbia University
Each cell characterized with 65 morphological variables (29 dendritic variables,29 axonal variables, 7 somatic variables) from Neurolucida software
Algorithms for supervised classifications (Weka)
Hierarchical clustering (Ward’s method + Euclidean distance)Naive BayesClassification treek -nearest neighborsMulti-layer perceptronLogistic regression
Feature subset selection (FSS):
Multivariate filtering (correlation-based feature selection (CFS)) versuswrapperForward versus backward versus genetic algorithms
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Neuroimaging -omics Clustering Classification
Supervised classification of neurons
Supervised classification of neurons via superv. methods. Guerra et al. (2009)Validation: 10-cv
NO FSS #Hierarchical clustering 59.33 65Naive Bayes 80.73± 10.44 65Classification tree 84.40± 3.84 65k -nearest neighbors 83.18± 7.15 65Multi-layer perceptron (1) 87.46± 9.06 65Logistic regression 82.26± 7.36 65
MULTIVARIATE FILTERING (CFS)Forward # Backward # Genetic algorithms #
Hierarchical clustering 77.68 10 71.25 17 79.82 16Naive Bayes 79.82± 9.86 10 79.51± 9.74 17 80.43± 7.07 16Classification tree (2) 82.26± 7.17 9 88.07± 6.09 11 81.65± 7.24 6k -nearest neighbors 83.79± 9.55 10 84.71± 6.03 17 85.01± 5.60 16Multi-layer perceptron 82.57± 9.54 10 87.77± 6.36 17 82.26± 9.17 16Logistic regression 82.26± 9.82 10 85.63± 8.56 17 83.49± 9.45 16
WRAPPERForward # Backward # Genetic algorithms #
Naive Bayes (3) 87.16± 6.34 8 83.18± 9.12 50 83.49± 8.55 23Classification tree (4,5) 86.85± 5.29 7 87.16± 5.83 12 86.85± 4.72 13k -nearest neighbors (6,7) 89.30± 7.58 6 86.85± 6.26 51 87.46± 5.68 34Multi-layer perceptron (8,9) 88.07± 4.99 10 88.07± 8.27 61 87.46± 6.26 37Logistic regression (10,11) 85.63± 9.79 7 84.71± 7.54 59 91.13± 5.95 33
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Dendritic morphologyDendritic branching influenced by complex interaction of synaptic activity,intracellular transport, extracellularly initiated signaling cascades, membranetension and electrical activity
Tree shapes help determine both interconnectivity and functional roles ofneurons
How and why vastly different shapes arise is still largely unknown
Understanding how formed in the brain, learn about their normal function andwhy they are often malformed in neurological diseases or under the effects ofsome drugs (cocaine, morphine)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Dendritic morphologyNeurons typically grouped in morphological classes based on prominentgeometrical features of their arborizations
E.g.: uniform distribution in space (stellate), planar (retinal ganglion cells), orpolarized structure (basal and apical in pyramidal)
stellate retinal ganglion pyramidal Purkinje
Can be specific to location in the brain: pyramidal are different in hippocampusand neocortex, and within the cortex, in different lobes (occipital or prefrontal)and layers (II or V)
An extreme: only present in specific regions, like Purkinje cells in cerebellum
There are no 2 neurons with the same morphology, although there existbranching patterns
⇒ Anatomical characterization is statistical in nature
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Computational modelingConstruct models that can simulate the huge variety of dendritic morphology
Synthetic neurons are generated within a virtual environment from amathematical-statistical model based on experimental measures collected fromreal traced cells
Simulated neurons should be indistinguishable (by statistical analysis or expertvisual inspection) from real cells
Steps1 Measure from real cells different parameters controlling branching behaviour2 Model these measures as statistical distributions3 Design a simulation model to account for all the parameters together and run it4 Compare formed virtual trees to reality
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Computational modelingConstruct models that can simulate the huge variety of dendritic morphology
Synthetic neurons are generated within a virtual environment from amathematical-statistical model based on experimental measures collected fromreal traced cells
Simulated neurons should be indistinguishable (by statistical analysis or expertvisual inspection) from real cells
Steps1 Measure from real cells different parameters controlling branching behaviour2 Model these measures as statistical distributions3 Design a simulation model to account for all the parameters together and run it4 Compare formed virtual trees to reality
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Data from real cellsDatabases at http://neuromorpho.org, with 5,500 neurons and animations
—-See animation
Own data
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Parameters. Donohue and Ascoli (2008)5 basic parameters: branch pathlength, taper rate, daughter ratio,parent-daughter ratio and probability of bifurcating
With every basic parameter, 3 fundamental determinants are also measured toconstrain the sampling of basic parameters according to their location within thetree: branch order (N. of bifurcations towards soma), path distance from somaand branch radius
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Parameters. Torben-Nielsen et al. (2008)Basic + emergent (coming from interaction between the basic) morphologicalproperties. E.g. total dendritic length emerges from the number of stems, stemlength and individual segment lengths
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Probability distribution modelsDifferent hypotheses:
Basic parameters are uniformly distributed throughout the dendritic tree(Donohue et al, 2002)
Dendritic segments are built from sequences of units of constant diameter,and distributions of the lengths of units of similar diameter is independentof location within a dendritic tree (Lindsay et al, 2007)
Different dimensions of distributions (Lindsay et al, 2007):
Univariate distributions (marginal and conditional): diameter of the childconditioned on the parent diameterBivariate: joint distribution of the parent and child diametersTrivariate: parent, child and peer diameters
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphologyProbability distribution models
Different distribution models:
Discrete (Donohue and Ascoli, 2005)
Gamma, Gaussian and uniform distributions (Donohue and Ascoli, 2008)
Kernel density estimators (Lindsay et al, 2007; Torben-Nielsen et al, 2008)
f (x) =1
nh
n∑i=1
K (x − xi
h)
x1, ..., xn the sampleh=bandwith→degree of smoothing(small=spiky=undersmoothing)
Gaussian: f (x) = 1nh√
2π
∑i exp
−(x−xi )2
2h2
Bivariate:f (x, y) = 1
nhx hy
∑ni=1 K (
x−xihx
)K (y−yi
hy)
Work in progressModels to capture interrelations between morphological properties?Bayesian networks
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphologyProbability distribution models
Different distribution models:
Discrete (Donohue and Ascoli, 2005)
Gamma, Gaussian and uniform distributions (Donohue and Ascoli, 2008)
Kernel density estimators (Lindsay et al, 2007; Torben-Nielsen et al, 2008)
f (x) =1
nh
n∑i=1
K (x − xi
h)
x1, ..., xn the sampleh=bandwith→degree of smoothing(small=spiky=undersmoothing)
Gaussian: f (x) = 1nh√
2π
∑i exp
−(x−xi )2
2h2
Bivariate:f (x, y) = 1
nhx hy
∑ni=1 K (
x−xihx
)K (y−yi
hy)
Work in progressModels to capture interrelations between morphological properties?Bayesian networks
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Simulation procedure
Donohue and Ascoli (2005) Torben-Nielsen et al. (2008)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
A recent model: NETMORPH (Koene et al, 2009) (http://www.netmorph.org)
Neuronal outgrowth is a stochastic process, where at each time step, branchingand elongation probab, direction of outgrowth, branching angles and segmentdiameters are functions with some random element (e.g. branch prob dependson dist from the soma, baseline branch rate exponentially decreases)
We can simulate:
Models of dendritic morphologiesModels of axonal morphologies
pyramidal & basket axon
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
A recent model: NETMORPHWe can simulate:
Network developmentSynaptic connectivity: look for sites where axon and dendrite areseparated less than a given distance...connection diagrams, freq of synapses, 3D visualizations...
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Comparing virtual and real cells: shape descriptorsMost commonly used method: Sholl’s analysis [Sholl, 1953], that uses std measures(length, surface area, number of branches...). Not orientation-sensitive
Or more sophisticated morphometrics related to branch patterns, defined in(Donohue and Ascoli, 2008)
Methods for orientation of the dendritic tree but losing important geometricinformation: “principal axes”, “circular orientation”, “cartesian grid density”(Uylings et al, 1986)
Fractal analysis to describe complexity of dendritic trees: do not discriminatebetween similar complexity but different spatial morphologies (Caserta et al, 1995)
Measure based on the Hausdorff distance, insensitive to outliers (also for 3Dstructures): connected and inconn parts are indistinguishable (Mizrahi et al, 2000)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Comparing virtual and real cells: dendrogramsTransform dendritic trees into dendrograms. Traditionally by hand. Now, e.g. withcomputer-vision techniques (segmentation based on curvature, edgedetection...) (Costa, 1999,2000)
Neuron Internal contours Skeleton
Dendrogram
(vertical width indicates width...)
But comparisons are qualitative and only capture some features of the structures
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Comparing virtual and real cells: an explicit distance between topologies
Comparison measure based on tree-edit distance (comp sci) (Heumann, Wittum, 2009)
Represent the topology as a tree (root=soma), assign to each vertex labels(describing the geometry of the section), compute distance between 2 trees asthe minimal cost of a feasible sequence of edit-operations –substitution,insertion, deletion– that converts T1 into T2
Dist=3
High computational cost and nodes with single label (Gillette, Grefenstette, 2009)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Morphology Models Validation
Computer simulation of dendritic morphology
Comparing virtual and real cells: clustering to validate this distance
Show that this distance D discriminates between different cell classes (a prioriknown)
Use matrix of distances D between every pair of cells and then ahierarchical-Ward clustering
Distinguish CA1 and CA3 pyramidal cells from hippocampus using volume for D:
Only 1 error
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Optimization
Two examples of optimization problemsNeuronal parameter optimization in compartmentalneuronal modelStructural optimization in brain networks
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimization
Compartmental modelsCompartmental models,based on a circuitrepresentation of a neuron(Hodgkin and Huxley, 1952),describe the potential alongthe dendritic tree
The model is described by asystem of ordinarydifferential equations
The model is used toreplicate the response of areal neuron to a set ofsimple current stimuli
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimization
The optimization problem in compartmental modelsThe process of identifying sets of parameters that lead to adesired electrical activity pattern in a neuron or neuralnetwork model that is not fully constrained by experimentaldata (Printz, 2007)
Related questionsChoice of an objective function or goodness measure toevaluate the possible solutionsSelection of an appropriate optimization algorithm
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimizationObjective functions
Point-to-point comparison: based on thecomparison of the electrophysiological tracesprovided by the data and the model
Feature-based: (1) spike rate; (2) anaccommodation index; (3) latency to firstspike; (4) average AP overshoot; (5) averagedepth of after hyperpolarization (AHP); (6)average AP width
Multi-objective: several feature-based criteriaat the same time
Optimization algorithms
Hand tuning: Bhalla and Bower, 1993; Foster et al., 1993; Prinz et al., 2003
Gradient descent: Bhalla and Bower, 1993
Evolutionary algorithms: Achard and De Schutter, 2006; Gerken et al., 2006;Keren et al., 2005; Pettinen et al., 2006; Vanier and Bower, 1999; Taylor andEnoka, 2004; van Geit et al., 2008
Hybrid methods: van Geit et al., 2007
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimization
Hybrid method (van Geit et al., 2007)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimization
Multi-objective (Druckmann et al., 2007)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Neuronal parameter optimization
GEneral NEural SImulation System (GENESIS) (Bower andBeeman, 1998)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Brain networksBrain activity can be modeled as a dynamical process acting on a network(DeLucia et al., 2005)
Each vertex of the structure represents an elementary component (brainareas, groups of neurons or individual cells)
Real and artificial brain networksThe network topology is a key element for understanding the behavior of theprocess
Based on real brain networks, similar artificial brain networks can be obtainedvia optimization
The artificial brain networks should satisfy some constraints identified in the realnetwork
The similarity is measured in terms of predefined topological characteristics(structural motifs and functional motifs)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Extraction of a real brain structural connectivity network
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Optimization approachStructural optimization in brain networks implies theidentification of a network topology that:
satisfies a number of constraints identified in the real brainnetwork (same number of node degrees)is optimal with respect to measure(s) defined in the spaceof networks (structural motifs and functional motifs)
Multiobjective optimization under constraints
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Structural motifs and functional motifsMotifs (Milo et al, 2002) as small network building blockswhich are defined by their size and interconnectionpatternsIt is possible (Sporns and Koetter, 2004) to gain insight intothe rules governing the structure of complex networks byinvestigating their composition from motifsStructural motifs as set of brain areas and pathways thatcan potentially engage in different patterns of interactionsFunctional motifs as specific combinations of nodes andconnections (contained in the structural motifs) that may beactivated in the course of neural information processing
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Structural motifs and functional motifs
A “motif” is a connected graph consisting of M vertices and a set of edges (arcs) (maximally M2 − M fordirected graphs, minimally M − 1) forming a subgraph of a larger network
For M = 2, 3, 4, 5 the corresponding numbers of motif types are 2, 13, 199 and 9,365 (Harrary andPalmer, 1973)
A “structural motif” of size M is composed of a specific set of M vertices that are linked by edges (arcs). Thename structural because a larger network could be structurally assembled from a finite set of such motifs
As different edges (arcs) become functionally engaged, different “functional motifs” emerge within a singlestructural motif
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Max. functional motifs and min. structural motifs (Spornsand Kotter, 2004)
Macaque visual cortex. Genetic algorithm.N = 30,K = 311. A: maximizing functionalmotifs. B: maximizing structural motifs
The results suggest the hypothesis thatbrain networks maximize the number offunctional motifs, while the repertoire ofstructural motifs remains small
Results are consistent with the hypothesisthat highly evolved neural architectures areorganized to:
maximize functional repertoires(maximizing the number of functionalmotifs)support highly efficient integration ofinformation (minimizing the number ofstructural motifs)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Compartmental model Brain networks
Structural optimization in brain networks
Non-dominated solutions learned in each run of the genetic algorithm (bluepoints) and absolute non-dominated solutions (red points) and original network(triangle)
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Outline
1 Introduction
2 Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
3 Computer simulation of dendritic morphologyDendritic morphologySimulation models of dendritic morphologyComparing virtual and real cells
4 Parameter and structural optimization in neuronal and brain modelsCompartmental modelBrain networks
5 ConclusionsChallengingMore material
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Computational intelligence for neuroscience
Neuroscience: challenging problems in modeling,simulation and optimization
Brain as the most complex organComputational intelligence
Pattern recognitionNeuroimaging-omicsClustering of neuronsClassification of neurons
Computer simulation of dendritic morphologyParameter and structural optimization in neuronal and brainnetworks
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Computational intelligence in neuroscience
ChallengingUnderstanding the human brain will have profoundconsequences for technology, health and society (twobillion people on the planet affected by mental disorder)Reverse engineering the brain and the mind has deepphilosophical consequences in addition to its practicalimplicationsHow long will it take? Depends on resources, quality andquantity of researchers, coordination, computing speed,storage capacity, automated imaging systems, ... (amulti-decade effort)Most important outcome: computational neuroscientistsarmed with knowledge and tools to contribute in the futureof humanity
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Relevant conferences
NIPS, INCFNeural Information Processing Systems (NIPS)
Key words: brain imaging, cognitive science and artificial intelligence,neuroscience2009 Workshops:
Connectivity inference in neuroimagingThe curse of dimensionality problem: How can the brain solve it?
Congress of Neuroinformatics (INCF)
The International Neuroinformatics Coordinating Facility (INCF) since2005 coordinates and fosters international activities for discovery andinnovation in neuroscience and related fields14 current member countries2009 Workshops:
Advances in the automatic analysis of multi-dim dataThe neuroinformatics of neural connectivityHigh performance computing and grid infrastructure forneuroinformatics applications
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Relevant competitions
INCF, DIADEM, PBAICQuantitative Single-Neuron Modeling (INCF)
Predict the timing of every spike that a neuron emits with certain precisionhttp://www.incf.org/community/competitions/spike-time-prediction/2009
Digital Reconstruction of Axonal and Dendritic Morphology (DIADEM). Still open
Automatically reconstruct (3D) neuronal morphologies. Compare againstmanually supervised reconstructions using a well-defined metrichttp://diademchallenge.org/
Pittsburgh Brain Competition (PBAIC) http://pbc.lrdc.pitt.edu/
Fall 2009: applying data mining to map the “cables of the human brain”(∼300,000 fiber streamlines) into 20-50 cables or tracts. Still open
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Relevant books
G. Ascoli (ed.) (2002) Computational Neuroanatomy: Principles and Methods,Humana Press
E. De Schutter (2003) Computational Neuroscience: Trends in Research 2003,Elsevier
R. Hecht-Nielsen, T. McKenna (2003) Computational Models for Neuroscience:Human Cortical Information Processing, Springer
P. Dayan, L.F. Abbott (2005) Theoretical Neuroscience: Computational andMathematical Modeling of Neural Systems, MIT
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Relevant books
S. Koslow, S. Subramaniam (eds.) (2005) Databasing the Brain: From Data toKnowledge, Wiley
L. Benuskova, N. Kasabov (2007) Computational Neurogenetic Modeling,Springer
K. Doya, S. Ishii, A. Pouget, R. Rao (eds.) (2007) Bayesian Brain: ProbabilisticApproaches to Neural Coding, MIT
J. McBrewster, F. Miller, A. Vandome (2009) Mind Uploading: Artificial NeuralNetwork, Artificial Intelligence, Neuroinformatics, Computational Neuroscience.Alphascript Press
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
Relevant journals
Science
Nature
Neuroinformatics
Neurocomputing
BMC Neuroscience
Journal of Neuroscience Methods
Journal of Computational Neuroscience
Mathematical Biosciences
Frontiers in Neuroscience
Bioinformatics
PLoS Computational Biology
NeuroImage
Artificial Intelligence in Medicine
Pattern Recognition Letters
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
Intro PattRecog Simulation Optimization Conclusions Challenging More material
COMPUTATIONAL INTELLIGENCE
FOR NEUROSCIENCE
Concha Bielza, Pedro Larranaga
Departamento de Inteligencia ArtificialUniversidad Politecnica de Madrid
Discovery Science and Algorithmic Learning Theory - 2009Porto, October 3, 2009
C. Bielza, P. Larranaga Computational Intelligence for Neuroscience
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