Jessica obtained her PhD in Applied Sciences at the University of Liège – Belgium in 2013. Her thesis investigated the application of machine learning models to neuroimaging data, tackling the challenging issue of decoding spontaneous brain activity and evaluating the potential utility of multivariate methods as computer-aided diagnostic tools. More recently, she was involved in the design of PRoNTo (Pattern Recognition for Neuroimaging Toolbox), an open access Matlab toolbox to perform machine learning modeling of neuroimaging data. She is the recipient of Belgian American Educational Foundation - Henri Benedictus Fellowship, and is studying patterns of spontaneous intracranial brain activity.
Honors & Awards
FRIA PhD fellowship, Belgian National Research Funds (10/01/2009 - 09/30/2013)
Award for a project in translational medicine., Medical Foundation of the Liège Rotary Club (09/02/2013)
René Comoth fellowship promoting deprived students obtaining Honors., University of Liège (09/01/2008)
Marie Sklodowska-Curie Actions fellowship, European Commission (07/01/2015 - 11/10/2018)
Henri Benedictus award, Belgian American Educational Foundation (02/06/2013)
Bachelor of Engineering, Universite De L'Etat A Liege (2007)
Master of Engineering, Universite De L'Etat A Liege (2009)
Doctor of Philosophy, Universite De L'Etat A Liege (2013)
Josef Parvizi, Postdoctoral Faculty Sponsor
Current Research and Scholarly Interests
Jessica's research is aimed at investigating how the brain can maintain and store new information. Her project studies the three phases of human episodic memory: (1) the encoding, which is the processing of incoming information, (2) the consolidation, during which a permanent record of that information is created and maintained, (3) the retrieval, which consists in retrieving the information on purpose. To this end, she will develop novel machine learning approaches.
Josef Parvizi, Laboratory of Behavioral and Cognitive Neuroscience (9/23/2013 - 2/28/2017)
- Assessing Steady-State, Multivariate Experimental Data Using Gaussian Processes: The GPExp Open-Source Library ENERGIES 2016; 9 (6)
Decoding intracranial EEG data with multiple kernel learning method
JOURNAL OF NEUROSCIENCE METHODS
2016; 261: 19-28
Machine learning models have been successfully applied to neuroimaging data to make predictions about behavioral and cognitive states of interest. While these multivariate methods have greatly advanced the field of neuroimaging, their application to electrophysiological data has been less common especially in the analysis of human intracranial electroencephalography (iEEG, also known as electrocorticography or ECoG) data, which contains a rich spectrum of signals recorded from a relatively high number of recording sites.In the present work, we introduce a novel approach to determine the contribution of different bandwidths of EEG signal in different recording sites across different experimental conditions using the Multiple Kernel Learning (MKL) method.To validate and compare the usefulness of our approach, we applied this method to an ECoG dataset that was previously analysed and published with univariate methods.Our findings proved the usefulness of the MKL method in detecting changes in the power of various frequency bands during a given task and selecting automatically the most contributory signal in the most contributory site(s) of recording.With a single computation, the contribution of each frequency band in each recording site in the estimated multivariate model can be highlighted, which then allows formulation of hypotheses that can be tested a posteriori with univariate methods if needed.
View details for DOI 10.1016/j.jneumeth.2015.11.028
View details for Web of Science ID 000370894700003
View details for PubMedID 26692030
Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory
2016; 26 (1): 166-179
Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high-low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM.
View details for DOI 10.1093/cercor/bhu189
View details for Web of Science ID 000370972500017
View details for PubMedID 25146374
Decoding memory processing from electro-corticography in human posteromedial cortex
2014 INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION IN NEUROIMAGING
View details for Web of Science ID 000345837700039
Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions
2014; 4: 687-694
Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.
View details for DOI 10.1016/j.nicl.2014.04.004
View details for Web of Science ID 000349667600075
View details for PubMedID 24936420
PRoNTo: Pattern Recognition for Neuroimaging Toolbox
2013; 11 (3): 319-337
In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univariate techniques, they lack an established and accessible software framework. The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models. The "Pattern Recognition for Neuroimaging Toolbox" (PRoNTo) is open-source, cross-platform, MATLAB-based and SPM compatible, therefore being suitable for both cognitive and clinical neuroscience research. In addition, it is designed to facilitate novel contributions from developers, aiming to improve the interaction between the neuroimaging and machine learning communities. Here, we introduce PRoNTo by presenting examples of possible research questions that can be addressed with the machine learning framework implemented in PRoNTo, and cannot be easily investigated with mass univariate statistical analysis.
View details for DOI 10.1007/s12021-013-9178-1
View details for Web of Science ID 000322259300005
View details for PubMedID 23417655
Concurrent Synaptic and Systems Memory Consolidation during Sleep
JOURNAL OF NEUROSCIENCE
2013; 33 (24): 10182-?
Memories are consolidated during sleep by two apparently antagonistic processes: (1) reinforcement of memory-specific cortical interactions and (2) homeostatic reduction in synaptic efficiency. Using fMRI, we assessed whether episodic memories are processed during sleep by either or both mechanisms, by comparing recollection before and after sleep. We probed whether LTP influences these processes by contrasting two groups of individuals prospectively recruited based on BDNF rs6265 (Val66Met) polymorphism. Between immediate retrieval and delayed testing scheduled after sleep, responses to recollection increased significantly more in Val/Val individuals than in Met carriers in parietal and occipital areas not previously engaged in retrieval, consistent with "systems-level consolidation." Responses also increased differentially between allelic groups in regions already activated before sleep but only in proportion to slow oscillation power, in keeping with "synaptic downscaling." Episodic memories seem processed at both synaptic and systemic levels during sleep by mechanisms involving LTP.
View details for DOI 10.1523/JNEUROSCI.0284-13.2013
View details for Web of Science ID 000320235300032
View details for PubMedID 23761912
Multiclass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes.
2013; 2: 883-893
Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling ('bagging') for non-hierarchical multiclass classification. The method was tested on 120 cerebral (18)fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration.
View details for DOI 10.1016/j.nicl.2013.06.004
View details for PubMedID 24179839
Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes
2012; 7 (4)
Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.
View details for DOI 10.1371/journal.pone.0035860
View details for Web of Science ID 000305349100036
View details for PubMedID 22563410
Experience-dependent induction of hypnagogic images during daytime naps: a combined behavioural and EEG study
JOURNAL OF SLEEP RESEARCH
2012; 21 (1): 10-20
This study characterizes hypnagogic hallucinations reported during a polygraphically recorded 90-min daytime nap following or preceding practice of the computer game Tetris. In the experimental group (N?=?16), participants played Tetris in the morning for 2?h during three consecutive days, while in a first control group (N?=?13, controlling the effect of experience) participants did not play any game, and in a second control group (N?=?14, controlling the effect of anticipation) participants played Tetris after the nap. During afternoon naps, participants were repetitively awakened 15, 45, 75, 120 or 180?s after the onset of S1, and were asked to report their mental content. Reports content was scored by three judges (inter-rater reliability 85%). In the experimental group, 48 out of 485 (10%) sleep-onset reports were Tetris-related. They mostly consisted of images and sounds with very little emotional content. They exactly reproduced Tetris elements or mixed them with other mnemonic components. By contrast, in the first control group, only one report out of 107 was scored as Tetris-related (1%), and in the second control group only three reports out of 112 were scored as Tetris-related (3%; between-groups comparison; P?=?0.006). Hypnagogic hallucinations were more consistently induced by experience than by anticipation (P?=?0.039), and they were predominantly observed during the transition of wakefulness to sleep. The observed attributes of experience-related hypnagogic hallucinations are consistent with the particular organization of regional brain activity at sleep onset, characterized by high activity in sensory cortices and in the default-mode network.
View details for DOI 10.1111/j.1365-2869.2011.00939.x
View details for Web of Science ID 000299332300003
View details for PubMedID 21848802
Brain functional integration decreases during propofol-induced loss of consciousness
2011; 57 (1): 198-205
Consciousness has been related to the amount of integrated information that the brain is able to generate. In this paper, we tested the hypothesis that the loss of consciousness caused by propofol anesthesia is associated with a significant reduction in the capacity of the brain to integrate information. To assess the functional structure of the whole brain, functional integration and partial correlations were computed from fMRI data acquired from 18 healthy volunteers during resting wakefulness and propofol-induced deep sedation. Total integration was significantly reduced from wakefulness to deep sedation in the whole brain as well as within and between its constituent networks (or systems). Integration was systematically reduced within each system (i.e., brain or networks), as well as between networks. However, the ventral attentional network maintained interactions with most other networks during deep sedation. Partial correlations further suggested that functional connectivity was particularly affected between parietal areas and frontal or temporal regions during deep sedation. Our findings suggest that the breakdown in brain integration is the neural correlate of the loss of consciousness induced by propofol. They stress the important role played by parietal and frontal areas in the generation of consciousness.
View details for DOI 10.1016/j.neuroimage.2011.04.020
View details for Web of Science ID 000291624100023
View details for PubMedID 21524704
fMRI artefact rejection and sleep scoring toolbox.
Computational intelligence and neuroscience
2011; 2011: 598206-?
We started writing the "fMRI artefact rejection and sleep scoring toolbox", or "FAST", to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. "FAST" is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. "FAST" is available for free, under the
View details for DOI 10.1155/2011/598206
View details for PubMedID 21461381