The concept of schizotypy — A computational anatomy perspective

AbstractDespite major progress in diagnostic accuracy and symptomatic treatment of mental disorders, there is an ongoing debate about their classification aiming to follow current advances in neurobiology. The main goal of this review is to provide a comprehensive summary of the put forward schizotypy concept that follows the needs for objective assessment of schizophrenia-like personality traits in the general population. We focus on major achievements in the field from the perspective of magnetic resonance imaging-based computational anatomy of the brain. Particular interest is devoted to overlapping brain structure findings in schizotypy and schizophrenia to promote a dimensional view on schizophrenia as extension of phenotype traits in the non-clinical general population

Grey matter alterations co-localize with functional abnormalities in developmental dyslexia : an ALE meta-analysis

The neural correlates of developmental dyslexia have been investigated intensively over the last two decades and reliable evidence for a dysfunction of left-hemispheric reading systems in dyslexic readers has been found in functional neuroimaging studies. In addition, structural imaging studies using voxel-based morphometry (VBM) demonstrated grey matter reductions in dyslexics in several brain regions. To objectively assess the consistency of these findings, we performed activation likelihood estimation (ALE) meta-analysis on nine published VBM studies reporting 62 foci of grey matter reduction in dyslexic readers. We found six significant clusters of convergence in bilateral temporo-parietal and left occipito-temporal cortical regions and in the cerebellum bilaterally. To identify possible overlaps between structural and functional deviations in dyslexic readers, we conducted additional ALE meta-analyses of imaging studies reporting functional underactivations (125 foci from 24 studies) or overactivations (95 foci from 11 studies ) in dyslexics. Subsequent conjunction analyses revealed overlaps between the results of the VBM meta-analysis and the meta-analysis of functional underactivations in the fusiform and supramarginal gyri of the left hemisphere. An overlap between VBM results and the meta-analysis of functional overactivations was found in the left cerebellum. The results of our study provide evidence for consistent grey matter variations bilaterally in the dyslexic brain and substantial overlap of these structural variations with functional abnormalities in left hemispheric regions

Computational anatomy for studying use-dependant brain plasticity.

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences

Explainable deep learning models for dementia identification via magnetic resonance imaging : Developing topics

Abstract Background Today, to diagnose dementia, clinicians evaluate cognitive tests performed by patients and briefly analyze brain imaging data to look for biomarkers. While valuable information is present in MRI scans, these latter remain challenging to analyze and interpret. Artificial intelligence models have shown promising results to improve the current practice by supporting practitioners in the evaluation of imaging data. Nonetheless, the majority of developed statistical models are more often than not black-box systems that issue predictions without any clear interpretability, hindering their practical applications. Methods We propose an interpretable method based on deep learning that works on minimally preprocessed T1-weighted 3D scans of the brain. Relying on FullGrad [1], we can dissect the predictions of the model given an input scan. Once the model is trained, it can not only give an automated diagnostic but also generate a heatmap highlighting the regions of the brain that our model points to be responsible for its prediction of dementia. To ensure practicality, we integrate our model in a convenient app that can smoothly be run from a browser, as shown in the attached screenshot. Results We trained and evaluated our model on the OASIS dataset [2]. The specific explanation obtained by our model points at well-known biomarkers, notably by highlighting the voxels of the hippocampus of patients with dementia. Interestingly, as it can be seen in the second annex, we notice that across individuals, our model focuses more on the voxels located in the right hippocampus. Conclusions In this study, we show how machine learning can identify dementia patients using MRI images while ensuring interpretable decisions of the models. Our tools, including the bespoke 'explainer' viewer overlaid on each patient's brain, will enable the development of better and more reliable machine-learning based diagnostics and nurture the trust of practitioners in computer-aided diagnostics. Furthermore, this will help to discover currently unknown biomarkers and thus lead to a better understanding of the disease. References: 1) Full-Gradient Representation for Neural Network Visualization, Suraj Srinivas, Francois Fleuret, 2) OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease, Pamela J LaMontagne

On the opto-electrical properties of ion-implanted single-crystal diamond in the visible and near-visible regime

Diamond is potentially the ultimate material for a vast range of optical and quantum-computing applications. The fabrication of diamond-based optical and photonic devices by ion implantation requires knowledge of the diamond’s modified optical properties. The purpose of this thesis is to determine how the optical properties of ion-implanted diamond depend on the ion implantation fluence. Ion-implanted diamond has been studied structurally, electrically and optically in the range 200 – 1700 nm.   Optical micro-waveguides are of fundamental importance to integrated optics for the transport of light into, out from and around diamond-based quantum devices and other photonic devices. A number of micro-waveguides have been modelled; the dimensions are designed to maintain single-mode propagation in the unimplanted core of the waveguide.   Disagreement in previous studies suggests qualitatively different mechanisms for ion-beam modification, at least at low fluences, between light-ion and heavy-ion implantation. This thesis supports the observation of the lowering of refractive index by low-fluence heavy-ion bombardment. There exists a region of implantation fluences in which the refractive index is lower than that of pristine diamond, while the absorption coefficient is still low enough to enable fabrication of efficient waveguides.   The achieved reduction in the refractive index, n , at 1.95 eV (637 nm vacuum wavelength) was ∆ n   ≈ -0.06; typically waveguides have  ∆ n   ≈ -0.003. The measured extinction coefficient, k , was 0.037 (α  ≈ 7x10 3 cm -1 ). The physical size of photonic components is largely influenced by the refractive index contrast between the two materials; a large contrast allows for smaller structures. However, diamond/air structures need to be very small to maintain single-mode propagation; they are inherently fragile. The smaller refractive index contrast achieved in this work permits the structures to be larger, and hence mechanically sound. Furthermore, the attenuation in the cladding region is inconsequential, due to the short operational lengths of the waveguides.   Refractive index determinations have been performed by spectral ellipsometry, white light reflectance and spectral transmittance, and compared with measurements of the electrical conductivity and the ion-induced surface swelling. The optical measurements all show quantitative agreement with each other. Furthermore, a consistent qualitative relation is shown between the optical measurements and the electrical conductivity measurements, which are comparable with previous measurements in diamond implanted with heavy ions.   It is a further claim of this thesis that the influence of the implanted atoms is negligible compared to the structural modifications that occur upon ion implantation. Furthermore, it is proposed that the lattice-induced pressure is the responsible mechanism that inhibits the decrease of the refractive index under high-energy light-ion implantation.   The conclusion reached is that the behaviour of the refractive index can be completely understood in terms of physical properties; namely material density, dangling-bond density, electric polarisability and electrical conductivity. The magnitude of these effects is highly dependent on the ion fluence; their individual contributions vary depending on the amount of lattice damage. The information contained within this thesis provides a feasible foundation for the production of waveguides and cavities; critical components in the realisation of a room-temperature scalable quantum computer

Brain tissue properties differentiate between motor and limbic basal ganglia circuits

Despite advances in understanding basic organizational principles of the human basal ganglia, accurate in vivo assessment of their anatomical properties is essential to improve early diagnosis in disorders with corticosubcortical pathology and optimize target planning in deep brain stimulation. Main goal of this study was the detailed topological characterization of limbic, associative, and motor subdivisions of the subthalamic nucleus (STN) in relation to corresponding corticosubcortical circuits. To this aim, we used magnetic resonance imaging and investigated independently anatomical connectivity via white matter tracts next to brain tissue properties. On the basis of probabilistic diffusion tractography we identified STN subregions with predominantly motor, associative, and limbic connectivity. We then computed for each of the nonoverlapping STN subregions the covariance between local brain tissue properties and the rest of the brain using high-resolution maps of magnetization transfer (MT) saturation and longitudinal (R1) and transverse relaxation rate (R2*). The demonstrated spatial distribution pattern of covariance between brain tissue properties linked to myelin (R1 and MT) and iron (R2*) content clearly segregates between motor and limbic basal ganglia circuits. We interpret the demonstrated covariance pattern as evidence for shared tissue properties within a functional circuit, which is closely linked to its function. Our findings open new possibilities for investigation of changes in the established covariance pattern aiming at accurate diagnosis of basal ganglia disorders and prediction of treatment outcom