Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours

A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex using Intrinsic Individual Brain Connectivity

Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome project. Results show that the intrinsic individual brain connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex

The neurobiology of cortical music representations

Music is undeniable one of humanity’s defining traits, as it has been documented since the earliest days of mankind, is present in all knowcultures and perceivable by all humans nearly alike. Intrigued by its omnipresence, researchers of all disciplines started the investigation of music’s mystical relationship and tremendous significance to humankind already several hundred years ago. Since comparably recently, the immense advancement of neuroscientific methods also enabled the examination of cognitive processes related to the processing of music. Within this neuroscience ofmusic, the vast majority of research work focused on how music, as an auditory stimulus, reaches the brain and howit is initially processed, aswell as on the tremendous effects it has on and can evoke through the human brain. However, intermediate steps, that is how the human brain achieves a transformation of incoming signals to a seemingly specialized and abstract representation of music have received less attention. Aiming to address this gap, the here presented thesis targeted these transformations, their possibly underlying processes and how both could potentially be explained through computational models. To this end, four projects were conducted. The first two comprised the creation and implementation of two open source toolboxes to first, tackle problems inherent to auditory neuroscience, thus also affecting neuroscientific music research and second, provide the basis for further advancements through standardization and automation. More precisely, this entailed deteriorated hearing thresholds and abilities in MRI settings and the aggravated localization and parcellation of the human auditory cortex as the core structure involved in auditory processing. The third project focused on the human’s brain apparent tuning to music by investigating functional and organizational principles of the auditory cortex and network with regard to the processing of different auditory categories of comparable social importance, more precisely if the perception of music evokes a is distinct and specialized pattern. In order to provide an in depth characterization of the respective patterns, both the segregation and integration of auditory cortex regions was examined. In the fourth and final project, a highly multimodal approach that included fMRI, EEG, behavior and models of varying complexity was utilized to evaluate how the aforementioned music representations are generated along the cortical hierarchy of auditory processing and how they are influenced by bottom-up and top-down processes. The results of project 1 and 2 demonstrated the necessity for the further advancement of MRI settings and definition of working models of the auditory cortex, as hearing thresholds and abilities seem to vary as a function of the used data acquisition protocol and the localization and parcellation of the human auditory cortex diverges drastically based on the approach it is based one. Project 3 revealed that the human brain apparently is indeed tuned for music by means of a specialized representation, as it evoked a bilateral network with a right hemispheric weight that was not observed for the other included categories. The result of this specialized and hierarchical recruitment of anterior and posterior auditory cortex regions was an abstract music component ix x SUMMARY that is situated in anterior regions of the superior temporal gyrus and preferably encodes music, regardless of sung or instrumental. The outcomes of project 4 indicated that even though the entire auditory cortex, again with a right hemispheric weight, is involved in the complex processing of music in particular, anterior regions yielded an abstract representation that varied excessively over time and could not sufficiently explained by any of the tested models. The specialized and abstract properties of this representation was furthermore underlined by the predictive ability of the tested models, as models that were either based on high level features such as behavioral representations and concepts or complex acoustic features always outperformed models based on single or simpler acoustic features. Additionally, factors know to influence auditory and thus music processing, like musical training apparently did not alter the observed representations. Together, the results of the projects suggest that the specialized and stable cortical representation of music is the outcome of sophisticated transformations of incoming sound signals along the cortical hierarchy of auditory processing that generate a music component in anterior regions of the superior temporal gyrus by means of top-down processes that interact with acoustic features, guiding their processing.Musik ist unbestreitbarer Weise eine der definierenden Eigenschaften des Menschen. Dokumentiert seit den frühesten Tagen der Menschheit und in allen bekannten Kulturen vorhanden, ist sie von allenMenschen nahezu gleichwahrnehmbar. Fasziniert von ihrerOmnipräsenz haben Wissenschaftler aller Disziplinen vor einigen hundert Jahren begonnen die mystische Beziehung zwischen Musik und Mensch, sowie ihre enorme Bedeutung für selbigen zu untersuchen. Seit einem vergleichsweise kurzem Zeitraum ist es durch den immensen Fortschritt neurowissenschafticher Methoden auch möglich die kognitiven Prozesse, welche an der Verarbeitung von Musik beteiligt, sind zu untersuchen. Innerhalb dieser Neurowissenschaft der Musik hat sich ein Großteil der Forschungsarbeit darauf konzentriert wie Musik, als auditorischer Stimulus, das menschliche Gehirn erreicht und wie sie initial verarbeitet wird, als auch welche kolossallen Effekte sie auf selbiges hat und auch dadurch bewirken kann. Jedoch haben die Zwischenschritte, also wie das menschliche Gehirn eintreffende Signale in eine scheinbar spezialisierte und abstrakte Repräsentation vonMusik umwandelt, vergleichsweise wenig Aufmerksamkeit erhalten. Um die dadurch entstandene Lücke zu adressieren, hat die hier vorliegende Dissertation diese Prozesse und wie selbige durch Modelle erklärt werden können in vier Projekten untersucht. Die ersten beiden Projekte beinhalteten die Herstellung und Implementierung von zwei Toolboxen um erstens, inhärente Probleme der auditorischen Neurowissenschaft, daher auch neurowissenschaftlicher Untersuchungen von Musik, zu verbessern und zweitens, eine Basis für weitere Fortschritte durch Standardisierung und Automatisierung zu schaffen. Im genaueren umfasste dies die stark beeinträchtigten Hörschwellen und –fähigkeiten in MRT-Untersuchungen und die erschwerte Lokalisation und Parzellierung des menschlichen auditorischen Kortex als Kernstruktur auditiver Verarbeitung. Das dritte Projekt befasste sich mit der augenscheinlichen Spezialisierung von Musik im menschlichen Gehirn durch die Untersuchung funktionaler und organisatorischer Prinzipien des auditorischen Kortex und Netzwerks bezüglich der Verarbeitung verschiedener auditorischer Kategorien vergleichbarer sozialer Bedeutung, im genaueren ob die Wahrnehmung von Musik ein distinktes und spezialisiertes neuronalenMuster hervorruft. Umeine ausführliche Charakterisierung der entsprechenden neuronalen Muster zu ermöglichen wurde die Segregation und Integration der Regionen des auditorischen Kortex untersucht. Im vierten und letzten Projekt wurde ein hochmultimodaler Ansatz,welcher fMRT, EEG, Verhalten undModelle verschiedener Komplexität beinhaltete, genutzt, umzu evaluieren, wie die zuvor genannten Repräsentationen von Musik entlang der kortikalen Hierarchie der auditorischen Verarbeitung generiert und wie sie möglicherweise durch Bottom-up- und Top-down-Ansätze beeinflusst werden. Die Ergebnisse von Projekt 1 und 2 demonstrierten die Notwendigkeit für weitere Verbesserungen von MRTUntersuchungen und die Definition eines Funktionsmodells des auditorischen Kortex, daHörxi xii ZUSAMMENFASSUNG schwellen und –fähigkeiten stark in Abhängigkeit der verwendeten Datenerwerbsprotokolle variierten und die Lokalisation, sowie Parzellierung des menschlichen auditorischen Kortex basierend auf den zugrundeliegenden Ansätzen drastisch divergiert. Projekt 3 zeigte, dass das menschliche Gehirn tatsächlich eine spezialisierte Repräsentation vonMusik enthält, da selbige als einzige auditorische Kategorie ein bilaterales Netzwerk mit rechtshemisphärischer Gewichtung evozierte. Aus diesemNetzwerk, welches die Rekrutierung anteriorer und posteriorer Teile des auditorischen Kortex beinhaltete, resultierte eine scheinbar abstrakte Repräsentation von Musik in anterioren Regionen des Gyrus temporalis superior, welche präferiert Musik enkodiert, ungeachtet ob gesungen oder instrumental. Die Resultate von Projekt 4 deuten darauf hin, dass der gesamte auditorische Kortex, erneut mit rechtshemisphärischer Gewichtung, an der komplexen Verarbeitung vonMusik beteiligt ist, besonders aber anteriore Regionen, die bereits genannten abstrakte Repräsentation hervorrufen, welche sich exzessiv über die Zeitdauer derWahrnehmung verändert und nicht hinreichend durch eines der getestetenModelle erklärt werden kann. Die spezialisierten und abstrakten Eigenschaften dieser Repräsentationen wurden weiterhin durch die prädiktiven Fähigkeiten der getestetenModelle unterstrichen, daModelle, welche entweder auf höheren Eigenschaften wie Verhaltensrepräsentationen und mentalen Konzepten oder komplexen akustischen Eigenschaften basierten, stets Modelle, welche auf niederen Attributen wie simplen akustischen Eigenschaften basierten, übertrafen. Zusätzlich konnte kein Effekt von Faktoren, wie z.B. musikalisches Training, welche bekanntermaßen auditorische und daherMusikverarbeitung beeinflussen, nachgewiesen werden. Zusammengefasst deuten die Ergebnisse der Projekte darauf, hin dass die spezialisierte und stabile kortikale Repräsentation vonMusik ein Resultat komplexer Prozesse ist, welche eintreffende Signale entlang der kortikalen Hierarchie auditorischer Verarbeitung in eine abstrakte Repräsentation vonMusik innerhalb anteriorer Regionen des Gyrus temporalis superior durch Top-Down-Prozesse, welche mit akustischen Eigenschaften interagieren und deren Verarbeitung steuern, umwandeln

Human thalamocortical connections and their involvement in language systems.

139 p.During evolution the expansion of the neocortex has been linked with the emergence of higher level cognitive functions, such as reasoning, abstract thinking, or language in human beings. Current research on cognitive neuroscience is mainly focused on the cerebral cortex. Whereas the thalamus is a structure that has extensive white-matter connections with the cerebral cortex, its expansion during evolution is parallel to the expansion of the neocortex. The thalamocortical connections are involved in communication between cortical areas. Thus, to fully understand the neural basis of cognition, a better understanding of the role of the thalamus in cortical function is necessary. The present doctoral dissertation is focused on the structure and function of the thalamus: the first study proposes a reproducible protocol to reconstruct the first-order thalamic white-matter tracts from diffusion-weighted imaging data; the second study investigates the higher-order thalamic white-matter tracts and a similar protocol is proposed to reconstruction those tracts; the third study uses task-based fMRI to examine the involvement of first-order thalamic nuclei in the main language systems.the current dissertation successfully reconstructed first-order and higher-order thalamic white-matter tracts from DWI data, and has proved high reproducibility of the reconstruction protocol. This protocol could benefit the tractography community to better understand the structural connectivity of the thalamus with cortical and subcortical structures and facilitate the research on thalamocortical pathways in humans. We also found evidence for differences in the processing of linguistic and nonlinguistic stimuli in first-order thalamic nuclei through a task-based fMRI study. These results suggest that the first-order thalamic nuclei play roles in human language that are beyond relaying sensory information from periphery to cerebral cortex. These findings are important to push forward our understanding on the role of subcortical structures, such as the thalamus, in human language functions, and to urge a revisitation of existing language models taking the thalamus into consideration

Evaluation of denoising strategies for task-based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

In-scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in-scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition-dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion-related artifacts between resting-state and task conditions. Denoising pipelines—including realignment/tissue-based regression, PCA/ICA-based methods (aCompCor and ICA-AROMA, respectively), global signal regression, and censoring of motion-contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance-dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance-dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task-based functional connectivity data, and more generally for resting-state data, are discussed