Central European foreign exchange markets: a cross-spectral analysis of the 2007 financial crisis

This paper investigates co-movements between currency markets of Czech Republic, Poland, Hungary, Slovakia and the Euro in the year following the drying up of money markets in August 2007. The paper shows that assessing the degree of foreign currency co-movement by correlation can lead to concluding, erroneously, that financial contagion has not occurred. Using cross-spectral methods, the paper shows that defining contagion as changes in the structure of co-movements of asset prices encompasses more of the complex nature of exchange rate dynamics. What is shown is that, following August 2007, there is increased in the intensity of co-movements, but non-linearly. Focusing on the activities of a mix of banks and currency managers, it is suggested that changes in the structure of currency interaction present an unfavourable view of the contagion experienced by at least three of these currencies

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

OBJECTIVE: This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. METHODS: A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. RESULTS: In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. SIGNIFICANCE: There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning-based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories

Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices

The topological organization of the cerebral cortex provides hierarchical axes, namely gradients, which reveal systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axes remains unclear. We map the gradient of cortical morphometric similarity (MS) connectome, combining multiple features conceptualized as a “fingerprint” of an individual’s brain. The principal MS gradient is anchored by motor and sensory cortices at two extreme ends, which are reliable and reproducible. Notably, divergences between motor and sensory hierarchies are consistent with the laminar histological thickness gradient but contrary to the canonical functional connectivity (FC) gradient. Moreover, the MS dissociates with FC gradients in the higher-order association cortices. The MS gradient recapitulates fundamental properties of cortical organization, from gene expression and cyto- and myeloarchitecture to evolutionary expansion. Collectively, our findings provide a heuristic hierarchical organization of cortical morphological neuromarkers

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics

Ultra-high field MRI across the depth of the cortex has the potential to provide ana- tomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 m whole brain acquisition to (a) layer-specific cell measures from the von Economo his- tology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expres- sion across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease

Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics

Ultra‐high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer‐selective vulnerability. Here we compare multi‐parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer‐specific cell measures from the von Economo histology atlas, (b) layer‐specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high‐fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer‐specific cell number and layer‐specific gene expression. R1‐ and R2*‐weighted connectivity strength of cortico‐striatal and intra‐hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer‐specific relationships demonstrated are at least in part related to the high cross‐correlations of von Economo atlas cell counts and layer‐specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning

Objective Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface‐based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. Methods Sixty‐one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1‐volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k‐fold cross‐validation. Results The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. Significance Automated surface‐based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy

Author Correction: Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism.

Differences in cortical morphology-in particular, cortical volume, thickness and surface area-have been reported in individuals with autism. However, it is unclear what aspects of genetic and transcriptomic variation are associated with these differences. Here we investigate the genetic correlates of global cortical thickness differences (ΔCT) in children with autism. We used Partial Least Squares Regression (PLSR) on structural MRI data from 548 children (166 with autism, 295 neurotypical children and 87 children with ADHD) and cortical gene expression data from the Allen Institute for Brain Science to identify genetic correlates of ΔCT in autism. We identify that these genes are enriched for synaptic transmission pathways and explain significant variation in ΔCT. These genes are also significantly enriched for genes dysregulated in the autism post-mortem cortex (Odd Ratio (OR) = 1.11, Pcorrected  10-14), driven entirely by downregulated genes (OR = 1.87, Pcorrected  10-15). We validated the enrichment for downregulated genes in two independent data sets: Validation 1 (OR = 1.44, Pcorrected = 0.004) and Validation 2 (OR = 1.30; Pcorrected = 0.001). We conclude that transcriptionally downregulated genes implicated in autism are robustly associated with global changes in cortical thickness variability in children with autism

Shifts in myeloarchitecture characterise adolescent development of cortical gradients.

Funder: MQ: Transforming Mental Health; Grant(s): MQF17/24We studied an accelerated longitudinal cohort of adolescents and young adults (n = 234, two time points) to investigate dynamic reconfigurations in myeloarchitecture. Intracortical profiles were generated using magnetization transfer (MT) data, a myelin-sensitive magnetic resonance imaging contrast. Mixed-effect models of depth specific intracortical profiles demonstrated two separate processes i) overall increases in MT, and ii) flattening of the MT profile related to enhanced signal in mid-to-deeper layers, especially in heteromodal and unimodal association cortices. This development was independent of morphological changes. Enhanced MT in mid-to-deeper layers was found to spatially co-localise specifically with gene expression markers of oligodendrocytes. Interregional covariance analysis revealed that these intracortical changes contributed to a gradual differentiation of higher-order from lower-order systems. Depth-dependent trajectories of intracortical myeloarchitectural development contribute to the maturation of structural hierarchies in the human neocortex, providing a model for adolescent development that bridges microstructural and macroscopic scales of brain organisation

Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study

Objective: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. Methods: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. Results: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. Significance: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy