fMRI Pattern Classification using Neuroanatomically Constrained Boosting

Pattern classification in functional MRI (fMRI) is a novel methodology to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. Thus, most previous fMRI classification methods were applied in individual subjects. In this study, we developed a novel approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. Spatially normalized activation maps were segmented into functional areas using a neuroanatomical atlas and each map was classified separately using local classifiers. A single multiclass output was applied using a weighted aggregation of the classifier’s outputs. An Adaboost technique was applied, modified to find the optimal aggregation of a set of spatially distributed classifiers. This Adaboost combined the regionspecific classifiers to achieve improved classification accuracy with respect to conventional techniques. Multiclass classification accuracy was assessed in an fMRI group study with interleaved motor, visual, auditory, and cognitive task design. Data were acquired across 18 subjects at different field strengths (1.5 T, 4 T), with different pulse sequence parameters (voxel size and readout bandwidth). Misclassification rates of the boosted classifier were between 3.5% and 10%, whereas for the single classifier, these were between 15% and 23%, suggesting that the boosted classifier provides a better generalization ability together with better robustness. The high computational speed of boosting classification makes it attractive for real-time fMRI to facilitate online interpretation of dynamically changing activation patternsPublicad

Total variation regularization for fMRI-based prediction of behaviour.

International audienceWhile medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of taskrelated or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the '1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies

Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICAPublicad

A Comparative Assessment of Statistical Approaches for fMRI Data to Obtain Activation Maps

Functional Magnetic Resonance Imaging (fMRI) lets us peek into the human mind and try to identify which brain areas are associated with certain tasks without the need for an invasive procedure. However, the data collected during fMRI sessions is complex; this 4 dimensional sequence of 3 dimensional volumes as images of the brain does not allow for straightforward inference. Multiple models have been developed to analyze this data and each comes with its intricacies and problems. Two of the most common ones are 2-step General Linear Model (GLM) and Independent Component Analysis (ICA). We compare these approaches empirically by fitting the models to real fMRI data using packages developed and readily available in R. The real data, obtained from an open source database openneuro.org, is named BOLD5000. The task of interest for this thesis is image viewing versus fixation cross (resting state). We found that both the first-level GLM and ICA revealed significant activation located in the occipital lobe which is consistent with the literature on visual tasks. The second-level GLM results were consistent with the first level and found activation located in the occipital lobe as well. The Group ICA results however found activation located mainly in the temporal lobe.No embargoAcademic Major: Statistic

Functional organisation of behavioural inhibitory control mechanisms in cortico-basal ganglia circuitry: implications for stimulant use disorder.

The neural and psychological mechanisms of inhibitory control processes were investigated, focusing on the cortico-basal ganglia circuits in rats and humans. These included behavioural flexibility, ‘waiting’ and ‘stopping’ impulsivity and involved serial spatial reversal learning task in rodents, and in humans, premature responses in the Monetary Incentive Delay (MID) task and the stop-signal reaction time task. Chapter 2 and Chapter 3 focus on individual differences in behavioural flexibility in rats while Chapter 4, Chapter 5 and Chapter 6 consider how inhibitory control mechanisms are affected by the psychostimulant drug cocaine in both rats and humans. As reported in Chapter 2, systemic modulation of monoaminergic transmission by monoamine oxidase A (MAO-A) inhibitors enhanced reversal learning performance, selectively by decreasing the lose-shift probability, thereby implicating a role for dopamine, serotonin and noradrenaline in facilitating learning from negative feedback. Resting state functional magnetic resonance imaging (fMRI) revealed enhanced functional connectivity of the orbitofrontal and motor cortices as a correlate of flexible reversal learning performance, consistent with elevated levels of monoamines in these region (Chapter 3). Having clarified the mechanisms underlying behavioural flexibility in rats, Chapter 4 reports that escalation of intravenous cocaine self-administration induces behavioural inflexibility in rats even after a relatively short period of cocaine intake. Computational models, including a reinforced and Bayesian learner, revealed a lack of exploitation of the learned response-outcome relationships in cocaine-exposed rats. Chapter 5 focused on impulse control in human volunteers, identifying the striatal and cingulo-opercular networks as substrates of impulsive, premature responding in healthy 4 volunteers, stimulant-dependent individuals and their unaffected siblings. Loss of impulse control was elicited by different incentives for drug-free participants as opposed to drug users. Drug cues elicited striatal activation and increased premature responses in the stimulant-dependent group compared with the control group. In contrast, the ventral striatum was linked to incentive specific activation to reward anticipation. Task-based fMRI demonstrated that interactions between dorsal striatum and cingulo-opercular “cold cognition” networks underlie failures of impulse control in the control, at-risk and stimulant-dependent groups. However, whereas the cingulo-opercular networks were associated with premature responding in all groups, the reward system was activated specifically by the drug incentive cues in the stimulant group, and by monetary incentive cues in the drug-free groups. Chapter 6 presents evidence that corticostriatal functional and effective connectivity in an overlapping network that includes the anterior cingulate and inferior frontal cortices as well as motor cortex, the subthalamic nucleus and dorsal striatum, is critical to stopping impulse control in both control and cocaine individuals. No stopping efficiency impairments were observed in the cocaine-dependent group. Nevertheless, lower structural corticostriatal connectivity measured using diffusion MRI was associated with response execution impairments in cocaine participants performing a stop-signal reaction time task. Further, response execution was rescued by the selective noradrenaline reuptake inhibitor atomoxetine, which also increased corticostriatal effective connectivity. Finally, increased impulsivity and behavioural inflexibility seen in stimulant use disorder in Chapter 5 and Chapter 4, respectively, were not observed in the endophenotype at risk for developing stimulant abuse but were rather a consequence of stimulant abuse. These results further clarify the monoaminergic substrates of behavioural flexibility and specify the neural and computational impairments in inhibitory control induced by stimulant dependence.Pinsent Darwin Studentship from the Dept of Physiology, Development and Neuroscienc

Semantics in speech production

The semantic system contributes to the process of speech production in two major ways. The basic information is contained within semantic representations, and the semantic control system manipulates that knowledge as required by task and context. This thesis explored the evidence for interactivity between semantic and phonological stages of speech production, and examined the role of semantic control within speech production. The data chapters focussed on patients with semantic aphasia or SA, who all have frontal and/or temporoparietal lesions and are thought to have a specific impairment of semantic control. In a novel development, grammatical class and cueing effects in this patient group were compared with healthy participants under tempo naming conditions, a paradigm which is thought to impair normal semantic control by imposing dual task conditions. A basic picture naming paradigm was used throughout, with the addition of different grammatical classes, correct and misleading phonemic cues, and repetition and semantic priming: all these manipulations could be expected to place differing loads on a semantic control system with either permanent or experimentally induced impairment. It was found that stimuli requiring less controlled processing such as high imageability objects, pictures with simultaneous correct cues or repetition primed pictures were named significantly more accurately than items which needed more controlled processing, such as low imageability actions, pictures with misleading phonemic cues and unprimed pictures. The cueing evidence offered support to interactive models of speech production where phonological activation is able to influence semantic selection. The impairment in tasks such as the inhibition of task-irrelevant material seen in SA patients and tempo participants, and the overlap between cortical areas cited in studies looking at both semantic and wider executive control mechanisms suggest that semantic control may be part of a more generalised executive system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Influence of Emotional Content on Event-Related Brain Potentials during Spoken Word Processing

In unserem alltäglichen Leben ist Sprache ein unerlässliches Mittel für Kommunikation und die Umsetzung sozialer Interaktionen. Sprache kann in zwei verschiedene Modalitäten unterteilt werden, in die auditorische und die visuelle Modalität. Die auditorische Modalität umfasst gesprochene Sprache, wohingegen die visuelle Modalität vom geschriebenen Teil der Sprache gebildet wird. Auch wenn ein Tag ohne Sprechen für die meisten von uns unvorstellbar ist, hat die bisherige Forschung die Untersuchung von Effekten bei der Verarbeitung von emotionalem Bedeutungsinhalt in gesprochener Sprache, im Gegensatz zu der Verarbeitung von geschriebener Sprache, vernachlässigt. Die Verarbeitung des emotionalen Bedeutungsinhalts von geschriebenen Wörtern hat eine Vielzahl von Studien mit Hilfe von ereigniskorrelierten Potentialen (EKPs) ausführlich untersucht. Im Gegensatz dazu wurde der emotionale Bedeutungsinhalt bei der Verarbeitung von gesprochener Sprache nur gelegentlich und meist entweder in seiner Interaktion mit emotionaler Prosodie oder fokussiert auf die Existenz einer spezifischen EKP Komponente untersucht. Daher bleibt die Frage offen, wie und an welchen Verarbeitungsschritten der emotionale Inhalt gesprochener Sprache ereigniskorrelierte Potentiale beeinflusst, unabhängig von emotionaler Prosodie und der Frage, ob Gemeinsamkeiten mit der Verarbeitung von geschriebenen emotionalen Wörtern bestehen. In dieser Dissertation untersuche ich die Verarbeitung von gesprochenen Einzelwörtern mit positivem, neutralem und negativem Inhalt, mit der erkenntnisleitenden Fragestellung, ob der emotionale Inhalt von gesprochenen Wörtern Emotionseffekte in EKPs hervorruft und ob diese vergleichbar sind zu denen, die für geschriebene Wörter gezeigt wurden. In der ersten dieser Dissertation zugrundeliegenden Studie wurden gesprochene Wörter mit emotionalem und neutralem Inhalt den Versuchspersonen in zwei verschiedenen Lautstärken präsentiert, um mögliche Interaktionen mit bottom-up Aufmerksamkeitseffekten, geleitet durch die Größe des Stimulus, zu erklären. Für visuelle Stimuli mit emotionalem Inhalt, wie Bilder oder geschriebene Wörter, hat die Größe des Stimulus erhöhte emotions-bedingte EKPs hervorgerufen, zum Beispiel auf der Ebene der early posterior negativity (EPN). Es wurde untersucht, ob diese erhöhte Relevanz von größeren visuellen Stimuli auf die auditorische Modalität übertragbar sein könnte. Negativer emotionaler Bedeutungsinhalt führt zu einer erhöhten frontalen Positivierung und einer parieto-okzipitalen Negativierung zwischen 370 und 530 Millisekunden. Diese Komponente zeigt Ähnlichkeit mit der visuellen EPN, obwohl sich die Negativierung zu zentraleren Arealen der Kopfoberfläche ausweitet. Daher stellt sich die Frage, ob diese Komponente das auditorische Pendant zu einer visuellen EPN darstellen könnte. Entscheidend ist hier, dass keine Interaktion dieser emotions-bedingten EKP Komponente mit dem Lautstärkefaktor beobachtet werden kann. Die folgenden Vergleichsaspekte deuten auf umfassendere Unterschiede zwischen visueller und auditorischer Sprachverarbeitung hin: die fehlende Interaktion zwischen der Größe des Stimulus und der Emotionseffekte, die Unterschiede in den Topographien der Emotionseffekte sowie unterschiedliche Latenzen verglichen zu der visuellen EPN. Der zweite Teil dieser Dissertation ist auf einen direkteren Vergleich von Emotionseffekten in der visuellen und auditorischen Modalität ausgerichtet. Zu diesem Zweck wurde eine zweite Studie durchgeführt, in der Versuchspersonen dieselben Wörter in geschriebener und gesprochener Modalität präsentiert bekamen. Die gesprochenen Wörter wurden dabei sowohl von einer Computerstimme (Experiment 1) als auch von einer menschlichen Stimme (Experiment 2) produziert. Diese Studie wurde konzipiert, um die Existenz einer „auditorischen EPN“ und ihre Randbedingungen zu untersuchen. Darüber hinaus sollte die These überprüft werden, ob die höhere soziale Relevanz einer menschlichen Stimme die Emotionseffekte vergrößert. In beiden Experimenten zeigen sich Emotionseffekte. Für geschriebene Wörter zwischen 230 und 400 Millisekunden, im Zeitbereich der early posterior negativity, für gesprochene Wörter zwischen 460 und 510 Millisekunden. Wenn man die Verteilung der EKP Differenzen zwischen emotionalen und neutralen auditorischen Wörtern berücksichtigt, zeigen die Effekte interessanterweise sogar eine größere Ähnlichkeit mit der visuellen EPN als die Ergebnisse des ersten Teils dieser Dissertation. Eine Quellenlokalisierung ergab vergleichbare neuronale Generatoren im superioren parietalen Lobus (SPL) und im inferioren temporalen Lobus (IPL), sowohl im visuellen als auch im „auditorischen EPN“ Zeitfenster. Diese Befunde deuten auf Gemeinsamkeiten in der Verarbeitung emotionaler Inhalte über die Modalitäten hinweg hin, die – zumindest teilweise – durch das gleiche neuronale System gestützt werden. Trotzdem erscheinen diese Gemeinsamkeiten überraschend, da für die visuelle EPN angenommen wird, dass sie eine verstärkte sensorische Enkodierung für emotionale Stimuli in visuellen Arealen abbildet. Die oben beschriebenen und in diesen Studien gezeigten Emotionseffekte unterscheiden sich bezüglich ihrer Latenzen, Topographien und der Valenz, welche den Effekt hervorruft (positiv oder negativ). Im letzten Teil der Dissertation wurden daher systematisch Unterschiede zwischen den Studien untersucht um potenzielle Ursachen für die oben aufgeführten Unterschiede in den Emotionseffekten bestimmen zu können. Es zeigen sich Geschlechterunterschiede in den Topographien in Studie 2, die jedoch nicht die gefundenen Unterscheide in den Emotionseffekten zwischen den beiden Studien erklären können. Es wird angenommen, dass beide Studien die gleiche auditorische emotions-bedingte Komponente (AEK) in einem vergleichbaren Zeitfenster (Studie 1: 477 530 ms; Studie 2: 464 515 ms) hervorrufen, welcher in der ersten Studie eine N400-ähnlichen Verteilung vorausgegangen ist. Obwohl keine Interaktionen zwischen emotionalem Inhalt und Lautstärke aufgezeigt werden können, gehe ich davon aus, dass die Manipulation der Lautstärke in der ersten Studie den Kontext des Experiments verändert, und so den früheren Effekt ausgelöst hat. Auch wenn keine verifizierbaren Ursachen für die beschriebenen Unterschiede zwischen den Emotionseffekten aufgezeigt werden konnten, ist es mir mit dieser Dissertation gelungen, die Existenz einer auditorischen emotions-bedingten Komponente zu zeigen, die durch emotionalen (in Vergleich zu neutralem) Inhalt während der Verarbeitung von gesprochener Sprache hervorgerufen wird. Diese Komponente spiegelt sich in einer anterioren Positivierung und einer posterioren Negativierung zwischen 460 und 520 Millisekunden nach Wortbeginn wider. Diese zeigt sich gleichbleibend, unabhängig von der sozialen Signifikanz der Stimme des Sprechers oder der Manipulation der Lautstärke. Bezüglich eines Vergleich des zugrundeliegenden neuronalen Netzwerkes während der Verarbeitung des Inhalts von gesprochenen und geschriebenen Wörtern, kann man annehmen, dass die Verarbeitung Hirnareale aktiviert, die zumindest teilweise im SPL und IPL liegen. Obwohl die Verteilung der AEK eine hohe Ähnlichkeit zur visuellen EPN aufzeigt, kann man nicht annehmen, dass dieser Effekt ein auditorisches Pendant darstellt. Diese Schlussfolgerung beruht darauf, dass sich eine typische EPN-Verteilung nur bei der Berechnung der Differenzkurven von emotionalen und neutralen Stimuli zeigt. Die daraus resultierende posteriore Negativierung spiegelt eine erhöhte Aktivierung von visuellen Arealen - hervorgerufen durch emotionale Stimuli - wider. Die Analyse der zugrundeliegenden neuronalen Generatoren für den Unterschied zwischen auditorischen emotionalen und neutralen Stimuli liefert keine signifikanten Ergebnisse. Trotzdem zeigen die zugrundeliegenden Topographien der einzelnen Emotionskategorien, dass die Gemeinsamkeit auf der Ebene der Differenzkurven aus völlig unterschiedlichen Verteilungen resultiert. Zukünftige Forschung müsste das auditorische Stimulusmaterial bezüglich der Wortlänge oder des Worterkennungspunktes strikter kontrollieren, um den zeitlichen Jitter in den Daten zu reduzieren und somit die neuronalen Generatoren einer auditorischen emotions-bedingten Komponente besser bestimmen zu können