@article {795, title = {A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e95753}, abstract = {

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0095753}, author = {Sweeney, Elizabeth M and Vogelstein, Joshua T and Cuzzocreo, Jennifer L and Calabresi, Peter A and Reich, Daniel S and Crainiceanu, Ciprian M and Shinohara, Russell T} } @article {VP11_QAP, title = {Fast Inexact Graph Mathing with Applications in Statistical Connectomics}, journal = {under review}, year = {2011}, author = {Vogelstein, Joshua T and Conroy, John C and Podrazik, Louis J and Kratzer, Steven G and Fishkind, Donniell E and Vogelstein, Joshua T and Priebe, Carey E} } @article {VP11_sigsub, title = {Graph Classification using Signal Subgraphs: Applications in Statistical Connectomics}, journal = {under review}, year = {2011}, author = {Vogelstein, Joshua T and Gray, William R and Vogelstein, Joshua T and Priebe, Carey E} } @article {Yuste2011, title = {Imaging action potentials with calcium indicators.}, journal = {Cold Spring Harbor protocols}, volume = {2009}, number = {8}, year = {2011}, pages = {pdb.prot5316}, abstract = {

The understanding of neuronal circuits has been, and will continue to be, greatly advanced by the simultaneous imaging of action potentials in neuronal ensembles. This protocol describes "bulk" loading of brain slices with acetoxymethyl (AM) ester calcium indicators in order to monitor action potential activity in large populations of neurons simultaneously. The imaging of calcium influx into neurons provides an indirect, but accurate, measure of action potential generation in individual neurons. Single-cell resolution, and thus the easy identification of every active cell, is the key advantage of the technique.

}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21807854}, author = {Yuste, Rafael and MacLean, Jason and Vogelstein, Joshua T and Paninski, Liam} } @article {VP11_unlabeled, title = {Shuffled Graph Classification: Theory and Connectome Applications}, journal = {under review}, year = {2011}, author = {Vogelstein, Joshua T and Priebe, Carey E} } @article {Paninski2010, title = {A new look at state-space models for neural data.}, journal = {Journal of computational neuroscience}, volume = {29}, number = {1-2}, year = {2010}, pages = {107{\textendash}26}, abstract = {

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.

}, keywords = {1 introduction, Action Potentials, Action Potentials: physiology, Animals, Computer Simulation, for inference in state-space, forward-backward methods, hidden markov model, models, neural coding, Neurological, Neurons, Neurons: physiology, Retinal Ganglion Cells, Retinal Ganglion Cells: physiology, state-space models, Statistical, Synapses, Synapses: physiology, tridiagonal matrix}, issn = {1573-6873}, doi = {10.1007/s10827-009-0179-x}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19649698}, author = {Paninski, Liam and Ahmadian, Yashar and Gil, Daniel and Shinsuke, Ferreira and Rahnama, Kamiar and Michael, Rad and Vogelstein, Joshua T and Wu, Wei and Ferreira, Daniel Gil and Koyama, Shinsuke and {Rahnama Rad}, Kamiar and Vidne, Michael} } @article {MishchenkoPaninski09, title = {A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data}, journal = {Annals of Applied Statistics}, volume = {in press}, year = {2009}, author = {Mishchenko, Yuriy and Vogelstein, Joshua T and Paninski, Liam} } @article {VogelsteinPaninski09, title = {Spike inference from calcium imaging using sequential Monte Carlo methods.}, journal = {Biophys J}, volume = {97}, number = {2}, year = {2009}, pages = {636{\textendash}655}, publisher = {Department of Neuroscience, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA. joshuav@jhu.edu}, abstract = {

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest{\textendash}spike trains and/or time varying intracellular calcium concentrations{\textendash}are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

}, keywords = {Animals, Biological, Calcium, cytology/metabolism, Fluorescence, Inbred C57BL, Intracellular Space, metabolism, Mice, models, Monte Carlo Method, Neurons, Probability, Time Factors}, doi = {10.1016/j.bpj.2008.08.005}, url = {http://dx.doi.org/10.1016/j.bpj.2008.08.005}, author = {Vogelstein, Joshua T and Watson, Brendon O and Packer, Adam M and Yuste, Rafael and Jedynak, Bruno and Paninski, Liam} } @article {Huys2008, title = {Psychiatry : insights into depression through normative decision-making models}, journal = {Control}, year = {2008}, pages = {1{\textendash}8}, author = {Huys, Quentin J M and Vogelstein, Joshua T and Dayan, Peter} } @article {Vogelstein2007, title = {Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses.}, journal = {IEEE Transactions on Neural Networks}, volume = {18}, number = {1}, year = {2007}, pages = {253{\textendash}65}, publisher = {IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS}, abstract = {

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm x 3 mm chip fabricated in 0.5-microm complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus.

}, issn = {10459227}, doi = {10.1109/TNN.2006.883007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278476}, author = {Vogelstein, Joshua T and Mallik, Udayan and Vogelstein, Joshua T and Cauwenberghs, Gert} } @article {Greenspan1997, title = {Loss of FHIT expression in cervical carcinoma cell lines and primary tumors.}, journal = {Cancer Research}, volume = {57}, number = {21}, year = {1997}, pages = {4692{\textendash}4698}, publisher = {Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.}, abstract = {

Allelic deletions involving the short arm of chromosome 3 (3p13-21.1) have been observed frequently in cervical carcinomas. Recently, a candidate tumor suppressor gene, FHIT (Fragile Histidine Triad), was cloned and mapped to this chromosomal region (3p14.2). Abnormal FHIT transcripts have been identified previously in a variety of tumor cell lines and primary carcinomas, although their significance and the molecular mechanisms underlying their origin remain incompletely defined. In addition, integration of human papillomavirus DNA has been identified at a fragile site (FRA3B) within the FHIT locus in cervical cancer. These observations motivated us to evaluate FHIT mRNA and protein expression in cervical cancer cell lines, primary cervical carcinomas, and normal tissues. Transcripts of the expected size and sequence were the predominant species identified by reverse transcription (RT)-PCR in cultured keratinocytes and all normal tissues evaluated. In contrast, aberrant FHIT transcripts were readily demonstrated in 6 of 7 cervical carcinoma cell lines and 17 of 25 (68\%) primary cervical carcinomas. Northern blot analyses demonstrated reduced or absent FHIT expression in the cervical carcinoma cell lines, particularly those with aberrant RT-PCR products. Immunohistochemical analysis of Fhit expression in cervical tissues revealed strong immunoreactivity in nonneoplastic squamous and glandular cervical epithelium and marked reduction or loss of Fhit protein in 25 of 33 (76\%) primary cervical carcinomas. In those cervical cancer cell lines and primary tumors with exclusively aberrant or absent FHIT transcripts by RT-PCR, Fhit protein expression was always markedly reduced or absent. The frequent alterations in FHIT expression in many cervical carcinomas, but not in normal tissues, suggest that FHIT gene alterations may play an important role in cervical tumorigenesis.

}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9354423}, author = {Greenspan, D L and Connolly, D C and Wu, R and Lei, R Y and Vogelstein, Joshua T and Kim, Y T and Mok, J E and Mu{\~n}oz, N and Bosch, F X and Shah, K and Cho, K R} }