SVR-CLSM Code and Results for Wiesen, D., Bonilha, L., Rorden, C., Karnath, H.-O. Structural (Dis)Connectomics of spatial exploration and attention: a study of stroke patients with spatial neglect.

The article discusses the pathogenetic and clinical aspects of vestibular and cochlear disorders such as dizziness, tympanophony, and vestibular ataxia. It is emphasized that the vestibular system provides not only the relationship between motor and sensory processes but its functions are also much more significant. The uniqueness of the vestibular system consists of its multisensory cortical projections. The analysis of vestibular information is provided by a network of connections, which its epicenter is located in the depths of the Sylvian fissure and the surrounding parietal-temporal regions, and the retroinsular region. It has been suggested that the vestibular cortex can be considered a network of connections between all cortical areas receiving impulses from the vestibular system, including regions where vestibular information affects the analysis of other sensory (i.e., somatosensory and visual) and motor activity. The pathogenetic mechanisms of dizziness, tympanophony, and ataxia are discussed. The conclusion is made about the significance of connectome disorders in this patient category.

Synthetic connectomes at the interface

Across various psychiatric disorders, investigations using functional MRI (fMRI) have revealed neural circuit disruptions, largely within networks

Driving the connectome by-wire

Objective To explore the changes in brain structure network connection in children with attention deficit hyperactivity disorder(ADHD), and to provide novel markers for early identification of ADHD in clinical practice. Methods Deterministic diffusion-tensor tractography and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 25 children with ADHD and 23 healthy control children from May 2017 to May 2018, at Children′s Hospital of Xuzhou Medical University.Individual white matter networks were constructed for each participant, then the global properties, nodal properties and edge-wise distributions were compared between the two groups. Results (1)The global efficiency of the ADHD group (0.30±0.13) was significantly lower than that of the healthy control group (0.38±0.11), but the clustering coefficient (0.35±0.28) and the characteristic path length (2.94±0.38) were significantly higher than those of the healthy control group (0.28±0.10, 2.65±0.37), and the differences were statistically significant (t=-2.41, 2.31, 2.62, all P<0.05). (2)In the ADHD group, the nodal efficiency of the left inferior frontal gyrus, triangular part (0.13±0.06), left supramarginal gyrus (0.30±0.10), left inferior parietal, angular gyri (0.29±0.10), left precuneus (0.26±0.12)were significantly lower than the healthy control group(0.17±0.07, 0.38±0.10, 0.40±0.12, 0.35±0.12), while the nodal efficiency of the right superior frontal gyrus, orbital part and right paracentral lobule were significantly higher than the healthy control group(0.49±0.17, 0.43±0.14), and the differences were statistically significant[t=-2.52, -2.62, -3.11, -2.77, 2.34, 2.79, all P<0.05, false discovery rate(FDR) corrected]. (3)A disrupted subnetwork was observed that consisted of left frontoparietal areas, basal ganglia, thalamus and insular network (P<0.05, FDR corrected), which has the potential to discriminate individuals with ADHD from healthy control children(area under receiver operating characteristic curve was 0.78). (4)Diminished strength of the subnet work connections was correlated with the attention defect in patients with ADHD(r=-0.607, P=0.003). Conclusions Using magnetic resonance diffusion tensor imaging, with the help of graph theory analysis technology, ADHD children can be observed changes in brain structure network at multiple levels.The distribution pattern of brain network structure connection changes is expected to become a new marker for identifying ADHD. Key words: Attention deficit hyperactivity disorder; Diffusion tensor image; Graph theory; Structural network

Reconstructing worm connectomes by sex

Network theory has informed studies of brain circuitry by focusing on nodes, networks, and dynamic connections (the “connectome”). These authors posit

Tractography is the only non-invasive technique which is used to reconstruct the white mat- ter structural connectivity of the human brain. It relies on a specific Magnetic Resonance Imaging (MRI) acquisition, called diffusion MRI, which is sensitive to the displacement of water protons to varying magnetic fields, generating a signal that can be used to indirectly estimate microscopic tissue characteristics, e.g., composition and geometry. More specifically, tractography relies on two essential aspects: 1) orientations which indicates the direction of the white matter fibers in a typical 3D grid, and 2) principles of how to connect the voxels to reconstruct the white matter connections. Tractography is a relatively young technique since it was proposed only twenty years ago; however, it is already used in specific clinical applica- tions, e.g., partially in neurosurgery, and in research studies that involve reconstructions of well-known neuronal pathways. Hundreds of different tractography techniques have been proposed in the past years. In order to evaluate their performance, we participated in several international challenges. The outcomes of the challenges showed the advantages and limitations of modern tractography methods. In particular, one issue that was highlighted is the lack of a gold standard. Diffusion MRI tractography typically is validated with postmortem material and a tedious concatenation of classical 2D histological slices. In this thesis, we propose one of the first studies that use a novel 3D histological technique, named CLARITY, to validate fiber orientation in a large portion of tissue. At the typical spatial resolution of MRI, approximately 60-90% of voxels in the white matter contain multiple fiber populations. However, most of the microstructure imaging techniques proposed are not suitable to disentangle multiple populations in a voxel. In this thesis, we aimed to study the limitations of modern tractography approaches, and we proposed novel methods where tractography could play a crucial role. We propose to use microstructure informed tractography to regularize two important microstructural features, i.e., axon diameter and transversal relaxation time T2, showing the clear advantages of the use of global approaches and opening a new perspective for connectivity analysis. However, the price to pay for increasing the complexity of existing models is an increase in computational burden. In the appendix of the thesis, we propose a preliminary study which uses neural network approaches to accelerate global fitting of complex models.

A full fly connectome

Embryo Networks and Connectomes in Caenorhabditis elegans

To understand the fundamentals of brain functions and our mind, it is essential to elucidate working principles of neural circuit activity orchestrated by the activities of single neurons. To achieve this goal, several big projects are ongoing worldwide to decode brain connectomes at micro- through macro-scales, aiming at obtaining a whole picture of neural connectivity ranging from single neurons, group of neurons, functional brain areas, and connections between the areas, and to understand the structure and functions of our brain. We will briefly overview these ongoing efforts and discuss issues that need to be solved as we move forward.

The etiology and pathological mechanism of schizophrenia are very complicated, and the heterogeneity of clinical manifestation and the heterogeneity of treatment reaction are also distinct. The journal of World Journal of psychiatry reported that in the past 20 years, about the schizophrenic biomarker study, either the direction of our research is wrong, or the direction is right, but the method is wrong. It is urgent to adopt new technologies to carry out study about the biomarkers of schizophrenia from multiple perspectives. It can be seen that the precise exploration of the etiology and specific pathological mechanism and therapeutic targets of schizophrenia is still a common research task of the world researchers. Compared with the research results achieved by tumor and heart disease study and the rapid development of clinical transformation, psychiatric research needs to consider other disciplines' ideas, methods and techniques to promote the research of schizophrenia. In the past 2 years, many scholars have studied schizophrenia from different perspectives. This paper reviewed the previous studies about omics matrix biomarkers study during the past two years. We briefly summarized the previous findings from the brain connectomics, genomics, proteomics, and microbial genomics perspective, respectively. We generally described the progress of brain imaging networks, gene networks, protein networks and micro biological networks in schizophrenia to increase our understanding of knowledge in this field. Key words: Schizophrenia; Brain connectomes; Genomics; Proteomics; Microbial genomics

Astroglial anatomy in the times of connectomics

The human brain connectome is a new and rapidly developing project in neuroscience. The pattern of structural and functional connectivity in the brain is not fixed, but is continuously changing in response to experiences. Ex­ploring these phenomena opens a powerful arsenal of analyses and computational approaches that could provide important new insights into clinical and cognitive neuroscience. The aim of the present study was to investigate the structural plasticity of the cortical areas of the brain during a memory task performance using functional MRI. Fifteen right-handed male subjects and fourteen right-handed female subjects were scanned during memory par­adigm performance. The scanning of the participants was performed with a 3Т MRI system - GE Discovery 750 w with a protocol including structural scan - Sag 3D T1 F-BRAVO FSPGR, slice thickness 1 mm, matrix 256N…256, flip angle 10о and standard block design functional scan - 2D EPI, slice thickness 3 mm, matrix 64N…64, TR (rep­etition time) - 3000 msec, TE (echo time) -30, flip angle 90о. Data were analysed using the SPM 12 (Statistical Paramertic Mapping) software running on MATLAB R2015 for Windows. The level of statistical significance was set at P < 0.05. Statistical significance in brain cortical activation was not found between males and females. We found reliable occipital and temporal signal response across the block design contrasts with statistically significant differenc­es within the groups in both genders. The results highlighted several detailed differences between males and fe­males and potential future directions in brain activation studies.

Over the last two decades the field of network science has been evolving fast. Many useful applications in a wide variety of disciplines have been found. The application of network science to the brain initiated the interdisciplinary field of complex brain networks. On a macroscopic level, brain regions are taken as nodes in a network. The analysis of pairwise connections between the brain regions as links has provided a new perspective on many problems. The application of network science to neuroscience data helped, for example, to identify the disruptions due to different neurological disorders when comparing healthy and abnormal brain networks. In this dissertation, we focus on the macroscopic level of brain regions and analyze their pairwise connections from a network science perspective. We address different general research questions from network science and exploit their application possibilities towards brain networks. Due to different measurement techniques, one can construct many different representations of brain networks. We thereby distinguish between the structural and functional brain network. Structural brain networks map the anatomical connections between the regions, which we could interpret as the ’streets’ of the brain. On top of these streets, we can measure the traffic with techniques like e.g. magnetoencephalography (MEG) or functional Magnetic Resonance Imaging (fMRI) resulting in so-called functional brain networks. However, the relation between the structural and the functional brain networks is still insufficiently understood. The first main research question of this dissertation focuses on the functional network layer and tries to identify the most important links and motifs of these networks. For this purpose, we propose the union of shortest path trees (USPT) as a new sampling method extracting all the shortest paths of a network (Chapter 2 and 3). After constructing the USPT, we compare the individual functional brain networks of multiple sclerosis patients and healthy controls (Chapter 2). Furthermore, we generalize this sampling method and present a new ranking of all the links based on the USPT (Chapter 3). Regarding the higher-order building blocks of the functional brain networks, we analyze the so-called information flow motifs based on MEG data from different frequency bands (Chapter 4). After researching the local properties of the functional brain networks, we analyze the influence of the underlying structural connections on the emerging information flow. Thus, the second main research question concerns the relationship between the functional and the underlying structural connectivity. Specifically, we analyze which topological properties of the structural networks drive the functional interactions. First, this question is approached in a mathematical and straightforward manner by assuming that an analytic function between the two networks exists (Chapter 5). We investigate this mapping function and its reverse by evaluating empirical individual and group-averaged multimodal data sets. A second approach towards the structure-function relationship employs a simple model of activity spread. The epidemic spreading model is applied on the human connectome to investigate the global patterns of directional information flow in brain networks (Chapter 6). The main focus here lies on the pairwise measure of transfer entropy to investigate the influence of one brain region on another. We present the results for the local and global outcomes of the dynamic spreading process aiming to identify the driving structural properties behind the observed global patterns.

Connectomics 2.0: Connected or Not, Is This the Question?

Weight discordancy-dependent brain networks between twins: a human connectome project analysis

The human brain connectome is a topologically complex, spatially embedded network. One of the characteristic, basic, nonrandom rules on which brain topology relies on is the tendency of brain networks nodes to cluster into modules with high efficiency and short path length, thus reflecting an intrinsic small-world behavior, functionally segregated (local clustering) and integrated (global efficiency) [1]. Although network topology seems to be somehow connected to network geometry, one of the most challenging issues of the current network science is to infer the hidden geometry from the mere topology of a complex network. Here in, aiming at disclosing the latent geometry of the brain, we apply coalescent embedding – a novel advanced technique able to map a given network in the hyperbolic space inferring the node angular coordinates - on different structural brain networks [2]. Interestingly, we show that we can unsupervisedly reconstruct the intrinsic brain geometry with an incredible level of accuracy and that it strongly resembles the known brain anatomy. As a matter of fact, the first rule of organization of brain networks emerging in the hyperbolic space is their structural segregation into two distinct sections corresponding to the left and right hemispheres, which is a simple concept yet quite neglected in previous studies on brain connectomics. In addition, we demonstrate that the human structural brain networks exhibited a significant different geometry in two age range-specific groups. Finally, we show that the intrinsic geometry of Parkinson’s Disease patients is significantly altered compared to the healthy subjects as revealed by two novel latent geometry markers. The present study may bridge the gap between brain networks topology and geometry and may open a completely new scenario towards the realization of latent geometry network markers for the evaluation of brain disorders.

The periaqueductal gray (PAG) is a relevant neuronal station situated in the midbrain, which play a pivotal role in triggering behavioral responses to stressful stimuli, such as pain or threat. Current knowledge concerning PAG functions is based on several tract-tracing studies conducted on animals, which unveiled PAG connectivity to both cortical and subcortical areas [1]. Considering that descending projections to spinal cord reach the dorsal horn and connections to motor related cortical areas have never been described yet, the neural structure which best fits PAG modulation of motor behavior is the cerebellum. Direct connections between PAG and cerebellar cortex were firstly described in cats and neurophysiological studies conducted on animals, suggesting either direct or undirect PAG influence to cerebellar activity. In the last decades, the rise of diffusion weighted imaging and tractography have made possible to reliably reconstruct white matter pathways in the human brain. To the best of our knowledge, few tractography studies explored PAG connectivity in humans and the evidences concerning direct or undirect connections with the cerebellar cortex are still sparse. Aimed at investigating PAG connectivity with particular focus on PAG-cerebellum connections, we used high quality diffusion weighted imaging data of thirty healthy subjects from the Human Connectome Project. Fiber tracts have been reconstructed using Spherical Informed Filtering of Tractograms, a novel algorithm improving streamline reconstruction and selection [2]. Connectivity analysis revealed that the PAG is mainly connected with subcortical structures, such as the thalamus and the cerebellum. Taken together our results show a direct interplay between the PAG and the cerebellum, thus suggesting the cerebellum as a likely candidate to modulate complex features of motor behavior in stressful conditions, such as adaptation after social defeat and computing strategies to avoid threatening situations.