The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Dynamic directed interictal connectivity in left and right temporal lobe epilepsy.

OBJECTIVE There is increasing evidence that epileptic activity involves widespread brain networks rather than single sources and that these networks contribute to interictal brain dysfunction. We investigated the fast-varying behavior of epileptic networks during interictal spikes in right and left temporal lobe epilepsy (RTLE and LTLE) at a whole-brain scale using directed connectivity. METHODS In 16 patients, 8 with LTLE and 8 with RTLE, we estimated the electrical source activity in 82 cortical regions of interest (ROIs) using high-density electroencephalography (EEG), individual head models, and a distributed linear inverse solution. A multivariate, time-varying, and frequency-resolved Granger-causal modeling (weighted Partial Directed Coherence) was applied to the source signal of all ROIs. A nonparametric statistical test assessed differences between spike and baseline epochs. Connectivity results between RTLE and LTLE were compared between RTLE and LTLE and with neuropsychological impairments. RESULTS Ipsilateral anterior temporal structures were identified as key drivers for both groups, concordant with the epileptogenic zone estimated invasively. We observed an increase in outflow from the key driver already before the spike. There were also important temporal and extratemporal ipsilateral drivers in both conditions, and contralateral only in RTLE. A different network pattern between LTLE and RTLE was found: in RTLE there was a much more prominent ipsilateral to contralateral pattern than in LTLE. Half of the RTLE patients but none of the LTLE patients had neuropsychological deficits consistent with contralateral temporal lobe dysfunction, suggesting a relationship between connectivity changes and cognitive deficits. SIGNIFICANCE The different patterns of time-varying connectivity in LTLE and RTLE suggest that they are not symmetrical entities, in line with our neuropsychological results. The highest outflow region was concordant with invasive validation of the epileptogenic zone. This enhanced characterization of dynamic connectivity patterns could better explain cognitive deficits and help the management of epilepsy surgery candidates.

A stereotaxic, population-averaged T1w ovine brain atlas including cerebral morphology and tissue volumes

Standard stereotaxic reference systems play a key role in human brain studies. Stereotaxic coordinate systems have also been developed for experimental animals including non-human primates, dogs, and rodents. However, they are lacking for other species being relevant in experimental neuroscience including sheep. Here, we present a spatial, unbiased ovine brain template with tissue probability maps (TPM) that offer a detailed stereotaxic reference frame for anatomical features and localization of brain areas, thereby enabling inter-individual and cross-study comparability. Three-dimensional data sets from healthy adult Merino sheep (Ovis orientalis aries, 12 ewes and 26 neutered rams) were acquired on a 1.5 T Philips MRI using a T1w sequence. Data were averaged by linear and non-linear registration algorithms. Moreover, animals were subjected to detailed brain volume analysis including examinations with respect to body weight (BW), age, and sex. The created T1w brain template provides an appropriate population-averaged ovine brain anatomy in a spatial standard coordinate system. Additionally, TPM for gray (GM) and white (WM) matter as well as cerebrospinal fluid (CSF) classification enabled automatic prior-based tissue segmentation using statistical parametric mapping (SPM). Overall, a positive correlation of GM volume and BW explained about 15% of the variance of GM while a positive correlation between WM and age was found. Absolute tissue volume differences were not detected, indeed ewes showed significantly more GM per bodyweight as compared to neutered rams. The created framework including spatial brain template and TPM represent a useful tool for unbiased automatic image preprocessing and morphological characterization in sheep. Therefore, the reported results may serve as a starting point for further experimental and/or translational research aiming at in vivo analysis in this species.

Studying functional brain networks with concurrent transcranical alternating current stimulation and EEG

Recently transcranial electric stimulation (tES) has been widely used as a mean to modulate brain activity. The modulatory effects of tES have been studied with the excitability of primary motor cortex. However, tES effects are not limited to the site of stimulation but extended to other brain areas, suggesting a need for the study of functional brain networks. Transcranial alternating current stimulation (tACS) applies sinusoidal current at a specified frequency, presumably modulating brain activity in a frequency-specific manner. At a behavioural level, tACS has been confirmed to modulate behaviour, but its neurophysiological effects are still elusive. In addition, neural oscillations are considered to reflect rhythmic changes in transmission efficacy across brain networks, suggesting that tACS would provide a mean to modulate brain networks. To study neurophysiological effects of tACS, we have been developing a methodological framework by combining transcranial magnetic stimulation (TMS), EEG and tACS. We have developed the optimized concurrent tACS-EEG recording protocol and powerful artefact removal method that allow us to study neurophysiological effects of tACS. We also established the concurrent tACS-TMS-EEG recording to study brain network connectivity while introducing extrinsic oscillatory activity by tACS. We show that tACS modulate brain activity in a phase-dependent manner. Our methodological advancement will open an opportunity to study causal role of oscillatory brain activity in neural transmissions in cortical brain networks.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.

Increased Resting State Network Connectivity in Synesthesia: Evidence for a Neural Basis of Synesthetic Consistency

Studying individual differences in conscious awareness can potentially lend fundamental insights into the neural bases of binding mechanisms and consciousness (Cohen Kadosh and Henik, 2007). Partly for this reason, considerable attention has been devoted to the neural mechanisms underlying grapheme–color synesthesia, a healthy condition involving atypical brain activation and the concurrent experience of color photisms in response to letters, numbers, and words. For instance, the letter C printed in black on a white background may elicit a yellow color photism that is perceived to be spatially colocalized with the inducing stimulus or internally in the “mind's eye” as, for instance, a visual image. Synesthetic experiences are involuntary, idiosyncratic, and consistent over time (Rouw et al., 2011). To date, neuroimaging research on synesthesia has focused on brain areas activated during the experience of synesthesia and associated structural brain differences. However, activity patterns of the synesthetic brain at rest remain largely unexplored. Moreover, the neural correlates of synesthetic consistency, the hallmark characteristic of synesthesia, remain elusive.

Magnetic resonance imaging and computer tomography of brain lesions in water buffaloes and cattle stunned with handguns or captive bolts

Owing to the demand for genuine mozzarella, some 330 water buffaloes are being slaughtered every year in Switzerland albeit a stunning procedure meeting animal welfare and occupational safety requirements remains to be established. To provide a basis for improvements, we sized anatomical specifics in water buffaloes and cattle and we assessed brain lesions after stunning with captive bolts or handguns by diagnostic imaging. In water buffaloes and cattle, the median distance from the frontal skin surface to the inner bone table was 74.0 mm (56.0–100.0 mm) vs 36.6 mm (29.3–44.3 mm) and from skin to the thalamus 144.8 mm (117.1–172.0 mm) vs 102.0 (101.0–121.0 mm), respectively. Consequently, customary captive bolt stunners may be inadequate. Free bullets are potentially suitable for stunning buffaloes but involve occupational safety hazards. The results of the present study shall be used to develop a device allowing effective and safe stunning of water buffaloes.

Cortical Abnormalities in Adults and Adolescents with Major Depression based on Brain Scans from 20 Cohorts Worldwide in the ENIGMA Major Depressive Disorder Working Group

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen’s d effect sizes: −0.10 to −0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: −0.26 to −0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.