Pattern Recognition in NeuroImaging: What can machine learning classifiers bring to the analysis of functional brain imaging?

Doctoral thesis (2013)

The study of the brain development and functioning raises many question that are tracked using neuroimaging techniques such as positron emission tomography or (functional) magnetic resonance imaging ... [more ▼]

The study of the brain development and functioning raises many question that are tracked using neuroimaging techniques such as positron emission tomography or (functional) magnetic resonance imaging. During the last decades, various techniques have been developed to analyse neuroimaging data. These techniques brought valuable insight on neuroscientific questions, but encounter limitations which make them unsuitable to tackle more complex problems. More recently, machine learning based models, coming from the field of pattern recognition, have been promisingly applied to neuroimaging data. In this work, the assets and limitations of machine learning based models were investigated and compared to previously developed techniques. To this end, two applications involving challenging datasets were defined and the results from widespread methods were compared to the results obtained using machine learning based modelling. More specifically, the first application addressed a research question: Is it possible to detect and characterize mnemonic traces? The fMRI experiment comprised a learning and a control tasks, both flanked by rest sessions. From previous studies, patterns of brain activity generated during the learning task should be spontaneously repeated during the following rest session, while no difference should be observed between the pre- and post-task rest session in the control condition. Using univariate and multivariate feature selection steps before a Gaussian Processes classification, mnemonic traces could be detected and their spatio-temporal evolution characterized. On the contrary, an analysis of the rest sessions based on the detection of independent networks did not provide any results supporting the theory of memory consolidation. The second application tackled a clinical issue: Can a pattern of brain activation characteristic to idiopathic Parkinson’s disease be detected and localized? The dataset considered to address this question comprised the fMRI images of aged healthy subjects and Parkinsonian patients while they were performing a task of mental imagery of gait at three different paces. The signal comprised in a priori selected regions of interest allowed for the support vector machines classification of healthy and diseased volunteers with an accuracy of 86%. To localize the discriminating pattern, a methodology based on the weight in labelled regions (e.g. from the anatomical automatic labelling or Brodmann atlases) was developed, which enabled the comparison between univariate and multivariate results and showed a nice overlap between them. Furthermore, models could then be compared quantitatively in terms of pattern localization, using a specifically defined measure of distance. This measure could then be used to compare the patterns generated from different folds of a same model, from different feature sets, or from different modelling techniques. The present study concluded that machine learning models can clearly and fruitfully complement other analysis techniques to tackle challenging questions in neuroscience. On the other hand, more work is needed in order to render the methodology fully accessible to the neuroscientific community. [less ▲]

Decoding spontaneous brain activity from fMRI using Gaussian Processes: tracking brain reactivation

in 2012 Second International Workshop on Pattern Recognition in NeuroImaging (PRNI 2012): proceedings (2012, July 03)

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled ... [more ▼]

While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled experimental paradigms. 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. Moreover, in the case of spontaneous brain activity, the mental states can not be linked to any external or internal stimulation, which makes it a highly difficult condition to decode. This study tests the classification of brain activity, acquired on 14 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. Application of the obtained model on rest sessions allowed classifying spontaneous brain activity linked to the task which, overall, correlated with their behavioural performance to the task. [less ▲]

Automatic multiclass classification of 18FDG-PET scans for the distinction between Parkinson’s disease and atypical parkinsonian syndromes

Poster (2012, June 10)

Part of the difficulty in the early diagnosis of Parkinson’s disease (PD) is in differentiating it from atypical parkinsonian disorders (APS) that have a poorer prognosis such as multiple system atrophy ... [more ▼]

Part of the difficulty in the early diagnosis of Parkinson’s disease (PD) is in differentiating it from atypical parkinsonian disorders (APS) that have a poorer prognosis such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). 18flurodeoxyglucose (FDG) positron emission tomography (PET) has been recommended for the early differentiation between PD and APS [1]. Here, 120 FDG PET scans (42, 31, 26 and 21 for the PD, MSA, PSP and CBS resp.) were acquired on average 3.5 years after symptoms onset (because the initial clinical features were outside the prevailing perception for PD) to look, without any a priori assumption, for cerebral FDG uptake patterns that discriminate either between the PD and APS classes, or between the four PD, MSA, PSP and CBS classes. The diagnostic used to label the scans was defined by clinical criteria on average 4.5 years after PET assessment. [less ▲]

Multivariate pattern analysis: brain decoding

in Schnakers, Caroline; Laureys, Steven (Eds.) Coma and altered states of consciousness (2012)

Two of the most fundamental questions in the field of neurosciences are how information is represented in different brain structures, and how this information evolves over time. Various tools, such as ... [more ▼]

Two of the most fundamental questions in the field of neurosciences are how information is represented in different brain structures, and how this information evolves over time. Various tools, such as Magnetic Resonance (MRI) and Positron Emission Tomography (PET) have been developed over the last few decades to record brain activity and investigate these questions. In particular, functional MRI (fMRI) tracks changes of the Blood Oxygenation Level-Dependent (BOLD) signal, which is a good indicator of brain activity, with a spatial resolution of a few cubic millimeters and a typical temporal resolution in the order of 1 or 2 seconds. Until recently, the methods used to analyze such data focused on characterizing the individual relationship between a cognitive or perceptual state and each image voxel, i.e. following a massively univariate approach. A well-known univariate technique is Statistical Parametric Mapping (SPM) . SPM relies on the General Linear Model to detect which voxels show a statistically significant response to the (combination of) experimental conditions of interest. However, there are limitations on what can be learned about the representation of information by examining voxels in a univariate fashion. For instance, spatially distributed sets of voxels considered as non-significant by a SPM analysis of one experimental condition might still carry information about the presence or absence of that condition. Furthermore, classic voxel-based analytic techniques are agnostic of any a priori information, for example disease-specific information. They are also mainly designed to perform group-wise comparisons and would therefore be unsuitable to evaluate the state of the disease of each individual. On the other hand, Multi-Voxel Pattern Analyses (MVPA) allow an increased sensitivity to detect the presence of a particular mental representation. These multivariate methods, also known as brain decoding or mind reading, attempt to link a particular cognitive, behavioral or perceptual state to specific patterns of voxels’ activity. Application of these methods made it possible to decode the category of a seen object or the orientation of a stripped pattern seen by the subject from the brain activation of the imaged subject. Advances in pattern-classification algorithms also allowed the decoding of less-controlled conditions such as memory retrieval tasks. Advanced mathematical tools are still under development to allow the classification of more complicated experimental data sets, such as examining the content of mind wandering or detecting the state of consciousness of a patient showing no response to a command. [less ▲]

FASST- a FMRI Artefact rejection and Sleep Scoring Toolbox

Poster (2011, June 28)

We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FASST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional ... [more ▼]

We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FASST”, 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 handwritten 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 GNU-GPL. [less ▲]

Brain functional integration decreases during propofol-induced loss of consciousness.

in NeuroImage (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 ... [more ▼]

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. [less ▲]

Experience-dependent induction of hypnagogic images during daytime naps: a combined behavioural and EEG study.

in Journal of Sleep Research (2011)

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 ... [more ▼]

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. [less ▲]

Changes in functional interactions during anaesthesia-induced loss of consciousness

Poster (2010, December 12)

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 ... [more ▼]

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. [less ▲]