Whole-brain computation of cognitive versus acoustic errors in music : A mismatch negativity study

Previous studies have evidenced how the local prediction of physical stimulus features may affect the neural processing of incoming stimuli. Less known are the effects of cognitive priors on predictive processes, and how the brain computes local versus cognitive predictions and their errors. Here, we determined the differential brain mechanisms underlying prediction errors related to high-level, cognitive priors for melody (rhythm, contour) versus low-level, local acoustic priors (tuning, timbre). We measured with magnetoencephalography the mismatch negativity (MMN) prediction error signal in 104 adults having varying levels of musical expertise. We discovered that the brain regions involved in early predictive processes for local priors were primary and secondary auditory cortex and insula, whereas cognitive brain regions such as cingulate and orbitofrontal cortices were recruited for early melodic errors in cognitive priors. The involvement of higher-level brain regions for computing early cognitive errors was enhanced in musicians, especially in cingulate cortex, inferior frontal gyrus, and supplementary motor area. Overall, the findings expand knowledge on whole-brain mechanisms of predictive processing and the related MMN generators, previously mainly confined to the auditory cortex, to a frontal network that strictly depends on the type of priors that are to be computed by the brain.Peer reviewe

The neural representation of events Is dominated by elements that are most reliably present

An episodic memory is specific to an event that occurred at a particular time and place. However, the elements that constitute the event—the location, the people present, and their actions and goals—might be shared with numerous other similar events. Does the brain preferentially represent certain elements of a remembered event? If so, which elements dominate its neural representation: those that are shared across similar events, or the novel elements that define a specific event? We addressed these questions by using a novel experimental paradigm combined with fMRI. Multiple events were created involving conversations between two individuals using the format of a television chat show. Chat show “hosts” occurred repeatedly across multiple events, whereas the “guests” were unique to only one event. Before learning the conversations, participants were scanned while viewing images or names of the (famous) individuals to be used in the study to obtain person-specific activity patterns. After learning all the conversations over a week, participants were scanned for a second time while they recalled each event multiple times. We found that during recall, person-specific activity patterns within the posterior midline network were reinstated for the hosts of the shows but not the guests, and that reinstatement of the hosts was significantly stronger than the reinstatement of the guests. These findings demonstrate that it is the more generic, familiar, and predictable elements of an event that dominate its neural representation compared with the more idiosyncratic, event-defining, elements

Development of an open source software module for enhanced visualization during MR-guided interstitial gynecologic brachytherapy

In 2010, gynecologic malignancies were the 4th leading cause of death in U.S. women and for patients with extensive primary or recurrent disease, treatment with interstitial brachytherapy may be an option. However, brachytherapy requires precise insertion of hollow catheters with introducers into the tumor in order to eradicate the cancer. In this study, a software solution to assist interstitial gynecologic brachytherapy has been investigated and the software has been realized as an own module under (3D) Slicer, which is a free open source software platform for (translational) biomedical research. The developed research module allows on-time processing of intra-operative magnetic resonance imaging (iMRI) data over a direct DICOM connection to a MR scanner. Afterwards follows a multi-stage registration of CAD models of the medical brachytherapy devices (template, obturator) to the patient's MR images, enabling the virtual placement of interstitial needles to assist the physician during the intervention.Comment: 9 pages, 6 figure

Quantitative analysis of human brain MR images at ultrahigh field strength

T2*-weighted imaging provides a non-invasive means to study susceptibility changes of substances such as myelin and iron in the brain. Particularly, phase images show an increased sensitivity to magnetic susceptibility differences with increased field strengths. The primary goal of the thesis was to develop methods for quantitative analysis of human brain T2*-weighted images at ultrahigh field strength. Additionally, it was also aimed to investigate the use of textural features derived from whole-brain deformation field for classification of Alzheimer__s disease (AD). A framework for the detection of between-group textural differences in 7T T2*-weighted magnitude and phase images of subcortical structures was presented, and its application was demonstrated in Huntington__s disease. A novel algorithm for segmentation of the cerebral cortex from 7T T2*-weighted images was proposed and extensively validated. Subsequently, a highly automated method was proposed for quantification of regional changes in these images in terms of gray matter/white matter contrast and cortical profile. In addition to an analysis of aging effect using data of young and elderly healthy subjects, this method was also applied to compare early- and late- onset AD patients. The analysis techniques presented in this thesis can be useful tools for susceptibility studies using ultrahigh field MR imagesUBL - phd migration 201

Insights from neuropsychology: pinpointing the role of the posterior parietal cortex in episodic and working memory

The role of posterior parietal cortex (PPC) in various forms of memory is a current topic of interest in the broader field of cognitive neuroscience. This large cortical region has been linked with a wide range of mnemonic functions affecting each stage of memory processing: encoding, maintenance, and retrieval. Yet, the precise role of the PPC in memory remains mysterious and controversial. Progress in understanding PPC function will require researchers to incorporate findings in a convergent manner from multiple experimental techniques rather than emphasizing a particular type of data. To facilitate this process, here, we review findings from the human neuropsychological research and examine the consequences to memory following PPC damage. Recent patient-based research findings have investigated two typically disconnected fields: working memory (WM) and episodic memory. The findings from patient participants with unilateral and bilateral PPC lesions performing diverse experimental paradigms are summarized. These findings are then related to findings from other techniques including neurostimulation (TMS and tDCS) and the influential and more abundant functional neuroimaging literature. We then review the strengths and weaknesses of hypotheses proposed to account for PPC function in these forms of memory. Finally, we address what missing evidence is needed to clarify the role(s) of the PPC in memory

Meta-analytic evidence for a novel hierarchical model of conceptual processing

Conceptual knowledge plays a pivotal role in human cognition. Grounded cognition theories propose that concepts consist of perceptual-motor features represented in modality-specific perceptual-motor cortices. However, it is unclear whether conceptual processing consistently engages modality-specific areas. Here, we performed an activation likelihood estimation (ALE) meta-analysis across 212 neuroimaging experiments on conceptual processing related to 7 perceptual-motor modalities (action, sound, visual shape, motion, color, olfaction-gustation, and emotion). We found that conceptual processing consistently engages brain regions also activated during real perceptual-motor experience of the same modalities. In addition, we identified multimodal convergence zones that are recruited for multiple modalities. In particular, the left inferior parietal lobe (IPL) and posterior middle temporal gyrus (pMTG) are engaged for three modalities: action, motion, and sound. These “trimodal” regions are surrounded by “bimodal” regions engaged for two modalities. Our findings support a novel model of the conceptual system, according to which conceptual processing relies on a hierarchical neural architecture from modality-specific to multimodal areas up to an amodal hub

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation

Flexible recruitment of cortical networks in visual and auditory attention

Our senses, while limited, shape our perception of the world and contribute to the functional architecture of the brain. This dissertation investigates the role of sensory modality and task demands in the cortical organization of healthy human adults using functional magnetic resonance imaging (fMRI). This research provides evidence for sensory modality bias in frontal cortical regions by directly contrasting auditory and visual sustained attention. This contrast revealed two distinct visual-biased regions in lateral frontal cortex - superior and inferior precentral sulcus (sPCS, iPCS) - anatomically interleaved with two auditory-biased regions - transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic (resting-state) functional connectivity analysis demonstrated that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Unisensory (auditory or visual) short-term memory (STM) tasks assessed the flexible recruitment of these sensory-biased cortical regions by varying information domain demands (e.g., spatial, temporal). While both modalities provide spatial and temporal information, vision has greater spatial resolution than audition, and audition has excellent temporal precision relative to vision. A visual temporal, but not a spatial, STM task flexibly recruited frontal auditory-biased regions; conversely, an auditory spatial task more strongly recruited frontal visual-biased regions compared to an auditory temporal task. This flexible recruitment extended to an auditory-biased superior temporal lobe region and to a subset of visual-biased parietal regions. A demanding auditory spatial STM task recruited anterior/superior visuotopic maps (IPS2-4, SPL1) along the intraparietal sulcus, but neither spatial nor temporal auditory tasks recruited posterior/interior maps. Finally, a comparison of visual spatial attention and STM under varied cognitive load demands attempted to further elucidate the organization of posterior parietal cortex. Parietal visuotopic maps were recruited for both visual spatial attention and working memory but demonstrated a graded response to task demands. Posterior/inferior maps (IPS0-1) demonstrated a linear relationship with the number of items attended to or remembered in the visual spatial tasks. Anterior/superior maps (IPS2-4, SPL1) demonstrated a general recruitment in visual spatial cognitive tasks, with a stronger response for visual spatial attention compared to STM

Investigation of neural activity in Schizophrenia during resting-state MEG : using non-linear dynamics and machine-learning to shed light on information disruption in the brain

Environ 25% de la population mondiale est atteinte de troubles psychiatriques qui sont typiquement associés à des problèmes comportementaux, fonctionnels et/ou cognitifs et dont les corrélats neurophysiologiques sont encore très mal compris. Non seulement ces dysfonctionnements réduisent la qualité de vie des individus touchés, mais ils peuvent aussi devenir un fardeau pour les proches et peser lourd dans l’économie d’une société. Cibler les mécanismes responsables du fonctionnement atypique du cerveau en identifiant des biomarqueurs plus robustes permettrait le développement de traitements plus efficaces. Ainsi, le premier objectif de cette thèse est de contribuer à une meilleure caractérisation des changements dynamiques cérébraux impliqués dans les troubles mentaux, plus précisément dans la schizophrénie et les troubles d’humeur. Pour ce faire, les premiers chapitres de cette thèse présentent, en intégral, deux revues de littératures systématiques que nous avons menées sur les altérations de connectivité cérébrale, au repos, chez les patients schizophrènes, dépressifs et bipolaires. Ces revues révèlent que, malgré des avancées scientifiques considérables dans l’étude de l’altération de la connectivité cérébrale fonctionnelle, la dimension temporelle des mécanismes cérébraux à l’origine de l’atteinte de l’intégration de l’information dans ces maladies, particulièrement de la schizophrénie, est encore mal comprise. Par conséquent, le deuxième objectif de cette thèse est de caractériser les changements cérébraux associés à la schizophrénie dans le domaine temporel. Nous présentons deux études dans lesquelles nous testons l’hypothèse que la « disconnectivité temporelle » serait un biomarqueur important en schizophrénie. Ces études explorent les déficits d’intégration temporelle en schizophrénie, en quantifiant les changements de la dynamique neuronale dite invariante d’échelle à partir des données magnétoencéphalographiques (MEG) enregistrés au repos chez des patients et des sujets contrôles. En particulier, nous utilisons (1) la LRTCs (long-range temporal correlation, ou corrélation temporelle à longue-distance) calculée à partir des oscillations neuronales et (2) des analyses multifractales pour caractériser des modifications de l’activité cérébrale arythmique. Par ailleurs, nous développons des modèles de classification (en apprentissage-machine supervisé) pour mieux cerner les attributs corticaux et sous-corticaux permettant une distinction robuste entre les patients et les sujets sains. Vu que ces études se basent sur des données MEG spontanées enregistrées au repos soit avec les yeux ouvert, ou les yeux fermées, nous nous sommes par la suite intéressés à la possibilité de trouver un marqueur qui combinerait ces enregistrements. La troisième étude originale explore donc l’utilité des modulations de l’amplitude spectrale entre yeux ouverts et fermées comme prédicteur de schizophrénie. Les résultats de ces études démontrent des changements cérébraux importants chez les patients schizophrènes au niveau de la dynamique d’invariance d’échelle. Elles suggèrent une dégradation du traitement temporel de l’information chez les patients, qui pourrait être liée à leurs symptômes cognitifs et comportementaux. L’approche multimodale de cette thèse, combinant la magétoencéphalographie, analyses non-linéaires et apprentissage machine, permet de mieux caractériser l’organisation spatio-temporelle du signal cérébrale au repos chez les patients atteints de schizophrénie et chez des individus sains. Les résultats fournissent de nouvelles preuves supportant l’hypothèse d’une « disconnectivité temporelle » en schizophrénie, et étendent les recherches antérieures, en explorant la contribution des structures cérébrales profondes et en employant des mesures non-linéaires avancées encore sous-exploitées dans ce domaine. L’ensemble des résultats de cette thèse apporte une contribution significative à la quête de nouveaux biomarqueurs de la schizophrénie et démontre l’importance d’élucider les altérations des propriétés temporelles de l’activité cérébrales intrinsèque en psychiatrie. Les études présentées offrent également un cadre méthodologique pouvant être étendu à d’autres psychopathologie, telles que la dépression.Psychiatric disorders affect nearly a quarter of the world’s population. These typically bring about debilitating behavioural, functional and/or cognitive problems, for which the underlying neural mechanisms are poorly understood. These symptoms can significantly reduce the quality of life of affected individuals, impact those close to them, and bring on an economic burden on society. Hence, targeting the baseline neurophysiology associated with psychopathologies, by identifying more robust biomarkers, would improve the development of effective treatments. The first goal of this thesis is thus to contribute to a better characterization of neural dynamic alterations in mental health illnesses, specifically in schizophrenia and mood disorders. Accordingly, the first chapter of this thesis presents two systematic literature reviews, which investigate the resting-state changes in brain connectivity in schizophrenia, depression and bipolar disorder patients. Great strides have been made in neuroimaging research in identifying alterations in functional connectivity. However, these two reviews reveal a gap in the knowledge about the temporal basis of the neural mechanisms involved in the disruption of information integration in these pathologies, particularly in schizophrenia. Therefore, the second goal of this thesis is to characterize the baseline temporal neural alterations of schizophrenia. We present two studies for which we hypothesize that the resting temporal dysconnectivity could serve as a key biomarker in schizophrenia. These studies explore temporal integration deficits in schizophrenia by quantifying neural alterations of scale-free dynamics using resting-state magnetoencephalography (MEG) data. Specifically, we use (1) long-range temporal correlation (LRTC) analysis on oscillatory activity and (2) multifractal analysis on arrhythmic brain activity. In addition, we develop classification models (based on supervised machine-learning) to detect the cortical and sub-cortical features that allow for a robust division of patients and healthy controls. Given that these studies are based on MEG spontaneous brain activity, recorded at rest with either eyes-open or eyes-closed, we then explored the possibility of finding a distinctive feature that would combine both types of resting-state recordings. Thus, the third study investigates whether alterations in spectral amplitude between eyes-open and eyes-closed conditions can be used as a possible marker for schizophrenia. Overall, the three studies show changes in the scale-free dynamics of schizophrenia patients at rest that suggest a deterioration of the temporal processing of information in patients, which might relate to their cognitive and behavioural symptoms. The multimodal approach of this thesis, combining MEG, non-linear analyses and machine-learning, improves the characterization of the resting spatiotemporal neural organization of schizophrenia patients and healthy controls. Our findings provide new evidence for the temporal dysconnectivity hypothesis in schizophrenia. The results extend on previous studies by characterizing scale-free properties of deep brain structures and applying advanced non-linear metrics that are underused in the field of psychiatry. The results of this thesis contribute significantly to the identification of novel biomarkers in schizophrenia and show the importance of clarifying the temporal properties of altered intrinsic neural dynamics. Moreover, the presented studies offer a methodological framework that can be extended to other psychopathologies, such as depression

Registration of brain MR images in large-scale populations

Non-rigid image registration is fundamentally important in analyzing large-scale population of medical images, e.g., T1-weighted brain MRI data. Conventional pairwise registration methods involve only two images, as the moving subject image is deformed towards the space of the template for the maximization of their in-between similarity. The population information, however, is mostly ignored, with individual images in the population registered independently with the arbitrarily selected template. By contrast, this dissertation investigates the contributions of the entire population to image registration. First, the population can provide guidance to the pairwise registration between a certain subject and the template. If the subject and an intermediate image in the same population are similar in appearances, the subject shares a similar deformation field with the intermediate image. Thus, the guidance from the intermediate image can be beneficial to the subject, in that the pre-estimated deformation field of the intermediate image initiates the estimation of the subject deformation field when the two images are registered with the identical template. Second, all images in the population can be registered towards the common space of the population using the groupwise technique. Groupwise registration differs from the traditional design of pairwise registration in that no template is pre-determined. Instead, all images agglomerate to the common space of the population simultaneously. Moreover, the common space is revealed spontaneously during image registration, without introducing any bias towards the subsequent analyses and applications. This dissertation shows that population information can contribute to both pairwise registration and groupwise registration. In particular, by utilizing the guidance from the intermediate images in the population, the pairwise registration is more robust and accurate compared to the direct pairwise registration between the subject and the template. Also, for groupwise registration, all images in the population can be aligned more accurately in the common space, although the complexity of groupwise registration increases substantially.Doctor of Philosoph