NILab. NeuroInformatics Laboratory
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1 NILab NeuroInformatics Laboratory
2 NILab Premise Office Building, Mattarello Center for Information Technology Fondazione Bruno Kessler Center for Mind/Brain Sciences Università degli Studi di Trento
3 NILab Outline Computational Methods Machine learning for data analysis Neuroscience Laboratories Heterogeneous sources of data Software Tools Open source projects based on Python
4 Outline Software Tools PyMVPA Multivariate Pattern Analysis in Python pymvpa.org DiPy Diffusion Imaging in Python nipy.sourceforge.net/dipy
5 Outline Neuroscience Labs 4T Bruker MedSpec MRI functional MRI diffusion MRI EEG MEG Elekta Neuromag 306 Channels
6 Outline Brain Decoding Prediction of mental state Brain Mapping Task-related brain segmentation Brain Connectivity Analysis of structural connectivity
7 Brain Decoding Non invasive technologies, like fmri and MEG, allow to record the brain activity when a subject is accomplishing a cognitive task. The challenge is to interpret the brain recordings in order to infer the corresponding mental task.
8 Brain Decoding Supervised Learning of Mental State Gaussian Processes and Recurrent Neural Networks to decode non conventional protocol of stimuli without any assumption on haemodynamic model. KEYWORD Gaussian Processes Regression, Recurrent Neural Networks AWARD 1st Prize PBAIC-2006 DATASET [fmri] Pittsburgh Brain Activity Interpretation Contest [fmri] Pittsburgh Brain Activity Interpretation Contest PARTNER CIMeC REFERENCE D.Sona, E.Olivetti, P.Avesani, S.Veeramachaneni, Learning to Interpret Cognitive States from fmri Brain Images, in Computational Intelligence and Bioengineering
9 Brain Decoding Real-time fmri Brain Decoding The challenge is twofold: on one hand to compute just in time the mental state, on the other hand to deal with dataset shift which occurs between different fmri recordings or between different subjects. KEYWORD Dataset Shift, Domain Adaptation, Functional Alignment DATASET [fmri] Neuroeconomics Trust Game, 2010 PARTNER CNRS, Lyon, France REFERENCE (ongoing work)
10 Brain Decoding Tensorial Kernel for Brain Data A kernel-based model designed to deal with tensorial encoding of multidimensional brain data for increasing learning performance when few data are available. KEYWORD Multilinear Ranks, Tensorial Kernel, Cumulants Kernel DATASET [MEG] ICANN Contest, 2011 [MEG] Biomag Contest, 2010 PARTNER Katholieke Universiteit Leuven REFERENCE (ongoing work)
11 Brain Decoding Unbiased Error Estimate To prevent circular analysis in computing error estimate which might invalidate the conclusion of the neuroscientific investigation. KEYWORD Error Estimation, Double Dipping DATASET [MEG] Biomag Contest, 2010 PARTNER CIMeC ot violated. for D (i) P artition(d n,k) do Di train = D n \ D (i) for θ Θ do for D (i,j) P artition(di train,k) do Di,j train = VariableSelection(Di train \ D (i,j) ) g = G(θ,Di,j train ) θ,i,j = (g, D (i,j) ) θ,i = mean j ( θ,i,j ) i = (G(argmin θ ( θ,i ),D train i ),D (i) ) return mean( i ) REFERENCE E. Olivetti, A. Mognon, S. Greiner, P.Avesani, Brain Decoding: Biases in Error Estimation, ICPR Workshop on Brain Decoding, 2010.
![Brain Decoding Hypothesis Testing Is there information about the stimulus given to the subject within brain data? DATASET [fmri] Knops et al.](/docs-images/96/127310625/images/12-3.jpg ", Science 324 (5934), 2009 [MEG] Biomag Contest, 2010 PARTNER Thomson Reuters, USA GfK, Warszawa, Poland REFERENCE E. Olivetti, S. Veeramachaneni, E.")
12 Brain Decoding Hypothesis Testing Is there information about the stimulus given to the subject within brain data? DATASET [fmri] Knops et al., Science 324 (5934), 2009 [MEG] Biomag Contest, 2010 PARTNER Thomson Reuters, USA GfK, Warszawa, Poland REFERENCE E. Olivetti, S. Veeramachaneni, E. Nowakowska, Bayesian Hypothesis Testing for Brain Decoding, Pattern Recognition (under review) E. Olivetti, S. Veeramachaneni, P.Avesani,Testing for Information with Brain Decoding, Pattern Recognition in Neuroimaging, (under review)
13 Brain Mapping A protocol of stimuli induces a mental state in a subject. The challenge is to identify the region of the brain related to the mental process.
14 Brain Mapping Random Subsampling Methods Dealing with the curse of dimensionality by reducing the ratio between features and examples. To perform the relevance voxel assessment by preserving the redundancy. KEYWORDS Feature Selection, Ensemble Methods DATASET [fmri] MVPA Testbed recorded at CIMeC PARTNER CIMeC REFERENCE D. Sona, P. Avesani, Multivariate Brain Mapping by Random Subsampling, ICPR. 2010
![KEYWORDS Liquid State Machine, Reservoir Computing DATASET [fmri] MVPA Testbed recorded at CIMeC 2009.](/docs-images/96/127310625/images/15-3.jpg "PARTNER University of Haifa REFERENCE P. Avesani, H. Hazan, E. Koilis, L. Manevitz, D.")
15 Brain Mapping Supervised Learning Haemodynamic Response Given the protocol of stimuli to predict the BOLD response of a given voxel. Working hypothesis: relevant voxels should allow for accurate BOLD prediction. KEYWORDS Liquid State Machine, Reservoir Computing DATASET [fmri] MVPA Testbed recorded at CIMeC PARTNER University of Haifa REFERENCE P. Avesani, H. Hazan, E. Koilis, L. Manevitz, D. Sona, Learning BOLD response in fmri by Reservoir Computing., Pattern Recognition in Neurimaging (under review)
16 Brain Mapping Longitudinal Studies To detect the portion of the brain affected by a rehabilitation theraphy interleaving two subsequent sessions of fmri recordings. Untangling neuroplasticity recovery from systemic variance of data sampling. DATASET [fmri] Language Rehabilitation 2010 PARTNER CERIN REFERENCE D. Sona, G. Basso, P. Avesani, S. Magon, G. Miceli, Pairwise Brain Mapping for Longitudinal Studies (ongoing)
17 Brain Connectivity Recent diffusion MRI techniques allow to reconstruct the structure of nearly fibers in the brain. Specific bundles of reconstructed fibers can be identified for their role in connecting related brain areas.
![DATASET [fmri, DSI] Pittsburgh Brain Connectivity Contest 2009 PARTNER CIMeC](/docs-images/96/127310625/images/18-4.jpg "REFERENCE E.")
18 Brain Connectivity Joint Functionl-Structural Data Analysis To investigate brain connectivity by combining functional and diffusion MRI data. Setting an homogeneous pairwise encoding both for voxel timecourses and tractography streamlines. DATASET [fmri, DSI] Pittsburgh Brain Connectivity Contest 2009 PARTNER CIMeC REFERENCE E.Olivetti, Sriharsha Veeramachaneni, Susanne Greiner, Paolo Avesani, Brain Connectivity Analysis by Reduction to Pair Classification, Cognitive Information Processing
19 Brain Connectivity Supervised Fiber Tract Segmentation Learning from a tract of a source subject manually annotated by a human expert to segment the same kind of tract from a subsample of tractography of a target subject. AWARD Honorable Mention PBC-2009 DATASET [DSI] Pittsburgh Brain Connectivity Contest 2009 PARTNER University of Cambridge REFERENCE E. Olivetti, S. Veeramachaneni, E. Garyfallidis, P. Avesani, Supervised Fiber Bundle Segmentation from Tractographies (ongoing)
20
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