BACKGROUND: A number of epilepsy syndromes are characterized by sleep-activated epileptiform discharges, however drivers of this process are not well understood. Previous research has found that thalamic injury in early life may increase the odds of sleep-activated spikes. Benign childhood epilepsy with centrotemporal spikes (BECTS) is among the most common pediatric-onset epilepsy syndromes, characterized by sleep-potentiated spike activity, a focal sensorimotor seizure semiology, and deficits in language, attention, and behavioral functioning. Though ictal and interictal electro-clinical activity resolves during mid-adolescence, adverse psychosocial outcomes may persist. Previous findings from monozygotic twin and neuroimaging studies suggest a multifactorial pattern of disease and raise suspicion for structural changes in thalamocortical connectivity focal to the seizure onset zone, though this has not been explored. OBJECTIVE: This research aims to (1) assess white matter differences in focal thalamocortical connectivity between BECTS cases and healthy controls using validated probabilistic tractography methods, (2) assess the association between spike burden and white matter connectivity focal to the seizure onset zone, and (3) evaluate longitudinal changes in thalamocortical connectivity across four cases. METHODS: 42 subjects ages 6-15 years were recruited between November 2015 and February 2018, including 23 BECTS cases and 19 healthy controls. Subjects underwent 3 Tesla structural and diffusion-weighted magnetic resonance imaging (2mm x 2mm x 2mm) with 64 gradient directions (b-value=2000) and 72 electrode sleep-deprived electroencephalographic (EEG) recordings. Seed and target regions of interest (ROIs) were created within each hemisphere using the Desikan-Killiany atlas, with the thalamus set as a seed ROI, and SOZ cortex and non-SOZ (NSOZ) cortex as target ROIs. Probabilistic tractography was executed using PROBTRACKX2 with 500 streamlines per seed voxel, 0.5 millimeter steps, and a curvature threshold of 0.2. All streamlines reaching the target ROI were summed and normalized by seed voxel count. Results for BECTS and healthy controls were plotted by age. The slope of thalamocortical connectivity versus age was computed for each group and compared between groups using nonparametric bootstrap analysis. Additionally, the association between SOZ connectivity and spike burden was assessed in a subgroup analysis using a linear regression model, controlling for age. RESULTS: A significant difference in the developmental trajectory of thalamocortical connectivity to the SOZ in BECTS cases compared to healthy controls was found (p=0.014), where the increase in connectivity with age observed in healthy controls was not present in BECTS children. These results did not extend to NSOZ thalamocortical connections (p=0.192). Longitudinal results support these observations, where all BECTS cases who underwent repeat imaging (N=4) showed a decrease in thalamocortical connectivity to the SOZ over the follow-up period. No relationship was found between thalamocortical connectivity and spike burden (p=0.840). CONCLUSIONS: These findings suggest that children with BECTS show subtle alterations in thalamocortical white matter development focal to the seizure onset zone. Thalamocortical connectivity to the SOZ does not appear to directly mediate non-REM sleep spike potentiation in BECTS. Limitations of this study include the potential for selection bias and limited power to detect sample differences. Additional research is needed to further characterize thalamocortical network changes and electrographic and neuropsychological correlates.

No description available.

Der menschliche inferiore Parietallappen (IPC) gehört zum Assoziationskortex und spielt eine wichtige Rolle bei der Integration von somatosensorischen (taktilen), visuellen und akustischen Reizen. Bisher gibt es keine eindeutigen Informationen über den strukturellen Aufbau dieser Hirnregion. Parzellierungen anhand der Zytoarchitektur reichen von zwei (Brodmann 1909) bis sieben Subareale (Caspers et al. 2006). Homologien zwischen dem IPC des Menschen und Makaken-Affen sind weitestgehend unbekannt. In der vorliegenden Arbeit wurden der Aufbau und die Konnektivitäten des menschlichen IPC genauer untersucht. Dazu führte man eine konnektivitätsbasierte Parzellierung des IPC an 20 Probanden durch. Als Methode kam Diffusions-Tensor-Imaging (DTI) kombiniert mit probabilistischer Traktogra-phie zum Einsatz. Der IPC konnte anhand der Konnektivitäten in drei Subareale (IPCa, IPCm, IPCp) parzelliert werden. Diese besitzen in beiden Hemisphären eine ähnliche Größe und eine rostro-kaudale Anordnung. Die Parzellierung ist vergleichbar mit der des Makaken-IPC, bei dem ebenfalls eine Unterteilung in drei Areale (PF, PFG, PG) und eine rostro-kaudale Anordnung nachgewiesen werden konnte. Jedes Subareal des menschlichen IPC besitzt ein individuelles Konnektivitätsmuster. Beim Menschen als auch beim Makaken gibt es starke Verbindungen zum lateralen prämotorischen Kortex und zum superioren Parietallappen. Diese Gemeinsamkeiten lassen darauf schließen, dass strukturelle Eigenschaften im Laufe der Evolution erhalten geblieben sind. Allerdings sind beim Menschen auch Neuentwicklungen nachweisbar. Dazu gehören die deutlich hervortretenden Verbindungen zum Temporallappen. Möglicherweise haben sich diese erst während der Evolution entwickelt und sind beim Menschen als Teil des perisylvischen Sprachnetzwerkes an der Sprachbildung beteiligt. / The human inferior parietal cortex convexity (IPCC) is an important association area, which integrates auditory, visual and somatosensory information. However, the structural organization of the IPCC is a controversial issue. For example, cytoarchitectonic parcellations reported in the literature range from two to seven areas. Moreover, anatomical descriptions of the human IPCC are often based on experiments in the macaque monkey. In this study we used diffusion-weighted magnetic resonance imaging (dMRI) combined with probabilistic tractography to quantify the connectivity of the human IPCC, and used this information to parcellate this cortex area. This provides a new structural map of the human IPCC, comprising three sub-areas (IPCa, IPCm, IPCp) of comparable size, in a rostro-caudal arrangement in the left and right hemisphere. Each sub-area is characterized by a connectivity fingerprint and the parcellation is similar to the subdivision reported for the macaque IPCC (rostro-caudal areas areas PF, PFG, and PG). However, the present study also reliably demonstrates new structural features in the connectivity pattern of the human IPCC, which are not known to exist in the macaque. This study quantifies inter-subject variability by providing a population representation of the sub-area arrangement, and demonstrates substantial lateralization of the connectivity patterns of IPCC.

The primate visual system is organised and wired in a topological manner. From the eye well into extrastriate visual cortex, a preserved spatial representation of the vi- sual world is maintained across many levels of processing. Diffusion-weighted imaging (DWI), together with probabilistic tractography, is a non-invasive technique for map- ping connectivity within the brain. In this thesis I probed the sensitivity and accuracy of DWI and probabilistic tractography by quantifying its capacity to detect topolog- ical connectivity in the post mortem macaque brain, between the lateral geniculate nucleus (LGN) and primary visual cortex (V1). The results were validated against electrophysiological and histological data from previous studies. Using the methodol- ogy developed in this thesis, it was possible to segment the LGN reliably into distinct subregions based on its structural connectivity to different parts of the visual field represented in V1. Quantitative differences in connectivity from magno- and parvo- cellular subcomponents of the LGN to different parts of V1 could be replicated with this method in post mortem brains. The topological corticocortical connectivity be- tween extrastriate visual area V5/MT and V1 could also be mapped in the post mortem macaque. In vivo DWI scans previously obtained from the same brains have lower resolution and signal-to-noise because of the shorter scan times. Nevertheless, in many cases, these yielded topological maps similar to the post mortem maps. These results indicate that the preserved topology of connection between LGN to V1, and V5/MT to V1, can be revealed using non-invasive measures of diffusion-weighted imaging and tractography in vivo. In a preliminary investigation using Human Connectome data obtained in vivo, I was not able to segment the retinotopic map in LGN based on con- nections to V1. This may be because information about the topological connectivity is not carried in the much lower resolution human diffusion data, or because of other methodological limitations. I also investigated the mechanisms of perceptual learning by developing a novel task-irrelevant perceptual learning paradigm designed to adapt neuronal elements early on in visual processing in a certain region of the visual field. There is evidence, although not clear-cut, to suggest that the paradigm elicits task- irrelevant perceptual learning, but that these effects only emerge when practice-related effects are accounted for. When orientation and location specific effects on perceptual performance are examined, the largest improvement occurs at the trained location, however, there is also significant improvement at one other 'untrained' location, and there is also a significant improvement in performance for a control group that did not receive any training at any location. The work highlights inherent difficulties in inves- tigating perceptual learning, which relate to the fact that learning likely takes place at both lower and higher levels of processing, however, the paradigm provides a good starting point for comprehensively investigating the complex mechanisms underlying perceptual learning.

Odorous sensations are based on trigeminal and olfactory perceptions. Both trigeminal and olfactory stimuli generate overlapping as well as distinctive activations in the olfactory cortex including the piriform cortex. Orbitofrontal cortex (OFC), an integrative center for all senses, is directly activated in the presence of olfactory stimulations. In contrast, the thalamus, a very important midbrain structure, is not directly activated in the presence of odors, but rather acts as a relay for portions of olfactory information between primary olfactory cortex and higher-order processing centers. The aims of the study were (1) to examine the number of streamlines between the piriform cortex and the OFC and also between the piriform cortex and the thalamus and (2) to explore potential correlations between these streamlines and trigeminal and olfactory chemosensory perceptions. Thirty-eight healthy subjects were recruited for the study and underwent diffusion MRI using a 3T MRI scanner with 67 diffusion directions. ROIs were adapted from two studies looking into olfaction in terms of functional and structural properties of the olfactory system. The “waytotal number” was used which corresponds to number of streamlines between two regions of interests. We found the number of streamlines between the piriform cortex and the thalamus to be higher in the left hemisphere, whereas the number of streamlines between the piriform cortex and the OFC were higher in the right hemisphere. We also found streamlines between the piriform cortex and the thalamus to be positively correlated with the intensity of irritating (trigeminal) odors. On the other hand, streamlines between the piriform cortex and the OFC were correlated with the threshold scores for these trigeminal odors. This is the first studying the correlations between streamlines and olfactory scores using tractography. Results suggest that different chemosensory stimuli are processed through different networks in the chemosensory system involving the thalamus.

Der menschliche inferiore Parietallappen (IPC) gehört zum Assoziationskortex und spielt eine wichtige Rolle bei der Integration von somatosensorischen (taktilen), visuellen und akustischen Reizen. Bisher gibt es keine eindeutigen Informationen über den strukturellen Aufbau dieser Hirnregion. Parzellierungen anhand der Zytoarchitektur reichen von zwei (Brodmann 1909) bis sieben Subareale (Caspers et al. 2006). Homologien zwischen dem IPC des Menschen und Makaken-Affen sind weitestgehend unbekannt. In der vorliegenden Arbeit wurden der Aufbau und die Konnektivitäten des menschlichen IPC genauer untersucht. Dazu führte man eine konnektivitätsbasierte Parzellierung des IPC an 20 Probanden durch. Als Methode kam Diffusions-Tensor-Imaging (DTI) kombiniert mit probabilistischer Traktogra-phie zum Einsatz. Der IPC konnte anhand der Konnektivitäten in drei Subareale (IPCa, IPCm, IPCp) parzelliert werden. Diese besitzen in beiden Hemisphären eine ähnliche Größe und eine rostro-kaudale Anordnung. Die Parzellierung ist vergleichbar mit der des Makaken-IPC, bei dem ebenfalls eine Unterteilung in drei Areale (PF, PFG, PG) und eine rostro-kaudale Anordnung nachgewiesen werden konnte. Jedes Subareal des menschlichen IPC besitzt ein individuelles Konnektivitätsmuster. Beim Menschen als auch beim Makaken gibt es starke Verbindungen zum lateralen prämotorischen Kortex und zum superioren Parietallappen. Diese Gemeinsamkeiten lassen darauf schließen, dass strukturelle Eigenschaften im Laufe der Evolution erhalten geblieben sind. Allerdings sind beim Menschen auch Neuentwicklungen nachweisbar. Dazu gehören die deutlich hervortretenden Verbindungen zum Temporallappen. Möglicherweise haben sich diese erst während der Evolution entwickelt und sind beim Menschen als Teil des perisylvischen Sprachnetzwerkes an der Sprachbildung beteiligt.:1. Einleitung 1.1. Der inferiore Parietalkortex 1.2. Konnektivitätsbasierte-Parzellierung durch Diffusions-Tensor-Bildgebung 1.3. Motivation 1.4. Überblick 2. Methoden 2.1. Theoretische Grundlagen 2.1.1. Magnet-Resonanz-Bildgebung 2.1.2. Diffusionsgewichtete Magnet-Resonanz-Tomographie 2.1.3. Diffusions-Tensor-Bildgebung 2.1.4. Traktographie in der weißen Substanz 2.1.5. Parzellierungsmethoden 2.2. Datenerfassung 2.3. Datenverarbeitung 2.4. Parzellierung des IPC 2.4.1. Definition der Analyseregion 2.4.2. Bestimmung der Startvoxel 2.4.3. Probabilistische Traktographie 2.4.4. Clustering 2.4.5. Populationskarte 2.4.6. Statistische Auswertung der Parzellierungsergebnisse 2.5. Analyse der Konnektivitäten des IPC 2.5.1. Berechnung der Konnektivitäten 2.5.2. Statistische Auswertung der Konnektivitäten 3. Ergebnis 3.1. Definition der Analyseregion 3.2. Analyse der Parzellierung 3.3. Statistische Auswertung der Parzellierung 3.4. Zusammenfassung der Parzellierungsergebnisse 3.5. Populationskarte aller Probanden 3.6. Statistische Auswertung weiterer Eigenschaften 3.6.1. Schwerpunkte der Areale 3.6.2. Größe der Areale 3.7. Analyse der Konnektivitäten 3.8. Statistische Auswertung der Konnektivitäten 3.9. Vergleich der linken und rechten Hemisphäre 4. Diskussion 4.1. Zwei oder drei Regionen: Welche Parzellierung ist am geeignetsten für den IPC? 4.2. Welche Konnektivitäten charakterisieren den IPC? 4.3. Vergleich von Mensch und Makaken 4.3.1. Homologien in der Parzellierung des IPC 4.3.2. Homologien in den Konnektivitäten des IPC 4.4. Funktionelle Bedeutung der IPC Parzellierung 4.4.1. Der IPC des Makaken 4.4.2. Der IPC des Menschen 4.5. Anmerkung zu den Methoden 4.5.1. Definition der Analyseregion 4.5.2. Auflösung der Diffusions-Tensor-Bildgebung 4.5.3. Traktographie Artefakte 4.6. Zusammenfassung 5. Anhang 5.1. Glossar 5.2. Abkürzungsverzeichnis 5.3. Detaillierte Abbildung der Ergebnisse 6. Danksagung 7. Zusammenfassung der Arbeit 8. Literaturverzeichnis 9. Publikation 10. Eigenständigkeitserklärung 11. Lebenslauf / The human inferior parietal cortex convexity (IPCC) is an important association area, which integrates auditory, visual and somatosensory information. However, the structural organization of the IPCC is a controversial issue. For example, cytoarchitectonic parcellations reported in the literature range from two to seven areas. Moreover, anatomical descriptions of the human IPCC are often based on experiments in the macaque monkey. In this study we used diffusion-weighted magnetic resonance imaging (dMRI) combined with probabilistic tractography to quantify the connectivity of the human IPCC, and used this information to parcellate this cortex area. This provides a new structural map of the human IPCC, comprising three sub-areas (IPCa, IPCm, IPCp) of comparable size, in a rostro-caudal arrangement in the left and right hemisphere. Each sub-area is characterized by a connectivity fingerprint and the parcellation is similar to the subdivision reported for the macaque IPCC (rostro-caudal areas areas PF, PFG, and PG). However, the present study also reliably demonstrates new structural features in the connectivity pattern of the human IPCC, which are not known to exist in the macaque. This study quantifies inter-subject variability by providing a population representation of the sub-area arrangement, and demonstrates substantial lateralization of the connectivity patterns of IPCC.:1. Einleitung 1.1. Der inferiore Parietalkortex 1.2. Konnektivitätsbasierte-Parzellierung durch Diffusions-Tensor-Bildgebung 1.3. Motivation 1.4. Überblick 2. Methoden 2.1. Theoretische Grundlagen 2.1.1. Magnet-Resonanz-Bildgebung 2.1.2. Diffusionsgewichtete Magnet-Resonanz-Tomographie 2.1.3. Diffusions-Tensor-Bildgebung 2.1.4. Traktographie in der weißen Substanz 2.1.5. Parzellierungsmethoden 2.2. Datenerfassung 2.3. Datenverarbeitung 2.4. Parzellierung des IPC 2.4.1. Definition der Analyseregion 2.4.2. Bestimmung der Startvoxel 2.4.3. Probabilistische Traktographie 2.4.4. Clustering 2.4.5. Populationskarte 2.4.6. Statistische Auswertung der Parzellierungsergebnisse 2.5. Analyse der Konnektivitäten des IPC 2.5.1. Berechnung der Konnektivitäten 2.5.2. Statistische Auswertung der Konnektivitäten 3. Ergebnis 3.1. Definition der Analyseregion 3.2. Analyse der Parzellierung 3.3. Statistische Auswertung der Parzellierung 3.4. Zusammenfassung der Parzellierungsergebnisse 3.5. Populationskarte aller Probanden 3.6. Statistische Auswertung weiterer Eigenschaften 3.6.1. Schwerpunkte der Areale 3.6.2. Größe der Areale 3.7. Analyse der Konnektivitäten 3.8. Statistische Auswertung der Konnektivitäten 3.9. Vergleich der linken und rechten Hemisphäre 4. Diskussion 4.1. Zwei oder drei Regionen: Welche Parzellierung ist am geeignetsten für den IPC? 4.2. Welche Konnektivitäten charakterisieren den IPC? 4.3. Vergleich von Mensch und Makaken 4.3.1. Homologien in der Parzellierung des IPC 4.3.2. Homologien in den Konnektivitäten des IPC 4.4. Funktionelle Bedeutung der IPC Parzellierung 4.4.1. Der IPC des Makaken 4.4.2. Der IPC des Menschen 4.5. Anmerkung zu den Methoden 4.5.1. Definition der Analyseregion 4.5.2. Auflösung der Diffusions-Tensor-Bildgebung 4.5.3. Traktographie Artefakte 4.6. Zusammenfassung 5. Anhang 5.1. Glossar 5.2. Abkürzungsverzeichnis 5.3. Detaillierte Abbildung der Ergebnisse 6. Danksagung 7. Zusammenfassung der Arbeit 8. Literaturverzeichnis 9. Publikation 10. Eigenständigkeitserklärung 11. Lebenslauf

Within the prefrontal cortex, the frontal eye field (FEF) and the inferior frontal junction (IFJ) are crucial regions that mediate attention, working memory, and cognitive control functions. I comprehensively reviewed the neuroimaging evidence on these regions, suggesting that they are specialized in the control of spatial versus non-spatial visual processing, respectively, and hypothesized that their connectivity fingerprints might underlie these roles. To accurately infer the localization of these regions in standard space, I carried out an activation likelihood estimation (ALE) fMRI meta-analysis using tasks that reliably engage these regions. Prosaccade and antisaccade tasks were included in the FEF sample, whereas oddball/attention, n-back, Stroop and task-switching experiments were included in the IFJ sample (n = 35 and 32, respectively). The ALE technique revealed the strongest convergence of activations at the junctions of the superior precentral sulcus and superior frontal sulcus for the FEF, and the inferior precentral sulcus and inferior frontal sulcus for the IFJ. I employed the resulting ALE peaks to perform a meta-analytic connectivity modeling analysis to uncover their whole-brain fMRI coactivations and decoded these patterns to infer significant associations with behavioral domains. The ALE peaks from a subsample of the previous meta-analysis were used to analyze 3T diffusion MRI data released by the Human Connectome Project from 56 unrelated subjects. Using a surface-based probabilistic tractography approach, I tracked streamlines ipsilaterally from the FEF and IFJ to regions of the dorsal and ventral visual streams on the native white matter surface parcellated using the atlas by Glasser et al. (2016). By contrasting their connectivity likelihood, I found predominant structural connectivity from FEF to regions of the dorsal visual stream (particularly in the left hemisphere) compared to the IFJ, and conversely, predominant structural connectivity from the IFJ to regions of the ventral visual stream compared to the FEF. These results were replicated when accounting for the distance between FEF, IFJ, and the target regions. The connectivity fingerprints of the FEF and IFJ provide converging evidence of their specialization in the control of spatial vs non-spatial processing, which is mediated by long-range white matter association pathways. The results presented in this dissertation overall support the view that the two-visual stream architecture extends to the human prefrontal cortex, as was originally hypothesized in the macaque based on tract tracing and neurophysiological evidence.