Schizophrenia—a severe psychiatric condition characterized by hallucinations, delusions, loss of initiative and cognitive function—is hypothesized to result from abnormal anatomical neural connectivity and a consequent decoupling of the brain’s integrative thought processes. The rise of in vivo neuroimaging techniques has refueled the formulation of dysconnectivity hypotheses, linking schizophrenia to abnormal structural and functional connectivity in the brain at both microscopic and macroscopic levels. Over the past few years, advances in high-field structural and functional neuroimaging techniques have made it increasingly feasible to reconstruct comprehensive maps of the macroscopic neural wiring system of the human brain, know as the connectome. In parallel, advances in network science and graph theory have improved our ability to study the spatial and topological organizational layout of such neural connectivity maps in detail. Combined, the field of neural connectomics has created a novel platform that provides a deeper understanding of the overall organization of brain wiring, its relation to healthy brain function and human cognition, and conversely, how brain disorders such as schizophrenia arise from abnormal brain network wiring and dynamics. In this review we discuss recent findings of connectomic studies in schizophrenia that examine how the disorder relates to disruptions of brain connectivity.

Brain networks in schizophrenia

Investigating white matter fibre density and morphology using fixel-based analysis.

Keywords: LTS5 Reference EPFL-CONF-138670 Record created on 2009-05-29, modified on 2017-05-10

https://serval.unil.ch/resource/serval:BIB_E467E3E2F917.P001/REF.pdf

Mapping the human connectome at multiple scales with diffusion spectrum MRI

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived “connection strengths” between distant brain regions is becoming increasingly popular However, this practice is often frowned upon by fiber-tracking experts In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them We also highlight the ongoing challenges related to fiber tracking While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural “connectomes” remain unresolved

Diffusion MRI fiber tractography of the brain

Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.

Evaluation and statistical inference for human connectomes

https://pure.mpg.de/pubman/item/item_1841047_8/component/file_2149374/Assaf_2013_Connect.pdf

Albero Bizzi

Papers

The CONNECT project: Combining macro- and micro-structure