scholarly journals Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study

2020 ◽  
Vol 91 (11) ◽  
pp. 1154-1157
Author(s):  
Evan S. Lutkenhoff ◽  
Vikesh Shrestha ◽  
Jesus Ruiz Tejeda ◽  
Courtney Real ◽  
David L. McArthur ◽  
...  
Keyword(s):  
Temporal Cortex ◽  
Clinical Score ◽  
Inferior Temporal Cortex ◽  
Imaging Biomarkers ◽  
Imaging Data ◽  
Post Injury ◽  
Initial Report ◽  
Post Traumatic ◽  
Volume Deficit ◽  
Early Seizures

BackgroundTraumatic brain injury (TBI) causes early seizures and is the leading cause of post-traumatic epilepsy. We prospectively assessed structural imaging biomarkers differentiating patients who develop seizures secondary to TBI from patients who do not.DesignMulticentre prospective cohort study starting in 2018. Imaging data are acquired around day 14 post-injury, detection of seizure events occurred early (within 1 week) and late (up to 90 days post-TBI).ResultsFrom a sample of 96 patients surviving moderate-to-severe TBI, we performed shape analysis of local volume deficits in subcortical areas (analysable sample: 57 patients; 35 no seizure, 14 early, 8 late) and cortical ribbon thinning (analysable sample: 46 patients; 29 no seizure, 10 early, 7 late). Right hippocampal volume deficit and inferior temporal cortex thinning demonstrated a significant effect across groups. Additionally, the degree of left frontal and temporal pole thinning, and clinical score at the time of the MRI, could differentiate patients experiencing early seizures from patients not experiencing them with 89% accuracy.Conclusions and relevanceAlthough this is an initial report, these data show that specific areas of localised volume deficit, as visible on routine imaging data, are associated with the emergence of seizures after TBI.

scholarly journals Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengting Liu ◽  
Claude Lepage ◽  
Sharon Y. Kim ◽  
Seun Jeon ◽  
Sun Hyung Kim ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Age Groups ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Qualitative Assessment ◽  
Systematic Evaluation ◽  
Imaging Data ◽  
The Mean ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

scholarly journals Robust T1 MRI cortical surface pipeline for neonatal brain and systematic evaluation using multi-site MRI datasets

2021 ◽  
Author(s):  
Mengting Liu ◽  
Claude Lepage ◽  
Sharon Y. Kim ◽  
Seun Jeon ◽  
Sun Hyung Kim ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Age Groups ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Qualitative Assessment ◽  
Systematic Evaluation ◽  
Cortical Surface ◽  
Imaging Data ◽  
Postmenstrual Age ◽  
The Mean

ABSTRACTThe human brain grows the most dramatically during the perinatal and early postnatal periods, during which preterm birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal MRI pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the CSF/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 preterm and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532±0.035mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with postmenstrual age (PMA) at scan (p<0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3mm at 28 weeks of PMA to 1.8mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Artificial intelligence and the radiologist: the future in the Armed Forces Medical Services

2019 ◽  
pp. jramc-2018-001055
Author(s):  
Debraj Sen ◽  
R Chakrabarti ◽  
S Chatterjee ◽  
D S Grewal ◽  
K Manrai
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Cyber Security ◽  
Tertiary Care ◽  
Armed Forces ◽  
Medical Services ◽  
Imaging Biomarkers ◽  
Imaging Data ◽  
Adequate Funding ◽  
Trained Manpower

Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology will help in dealing with the tedious, repetitive, time-consuming job of detecting relevant findings in diagnostic imaging and segmenting the detected images into smaller data. It would also help in identifying details that are oblivious to the human eye. AI will have an immense impact in populations with deficiency of radiologists and in screening programmes. By correlating imaging data from millions of patients and their clinico-demographic-therapy-morbidity-mortality profiles, AI could lead to identification of new imaging biomarkers. This would change therapy and direct new research. However, issues of standardisation, transparency, ethics, regulations, training, accreditation and safety are the challenges ahead. The Armed Forces Medical Services has widely dispersed units, medical echelons and roles ranging from small field units to large static tertiary care centres. They can incorporate AI-enabled radiological services to subserve small remotely located hospitals and detachments without posted radiologists and ease the load of radiologists in larger hospitals. Early widespread incorporation of information technology and enabled services in our hospitals, adequate funding, regular upgradation of software and hardware, dedicated trained manpower to manage the information technology services and train staff, and cyber security are issues that need to be addressed.

Organization of local horizontal functional interactions between neurons in the inferior temporal cortex of macaque monkeys

2014 ◽  
Vol 111 (12) ◽  
pp. 2589-2602 ◽  
Author(s):  
Hiroshi Tamura ◽  
Yoshiya Mori ◽  
Hidekazu Kaneko
Keyword(s):  
Neuronal Activity ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Visual Object ◽  
Detailed Knowledge ◽  
Visual Response ◽  
Stimulus Preference ◽  
Macaque Monkeys ◽  
Functional Interactions ◽  
Cross Correlation Analysis

Detailed knowledge of neuronal circuitry is necessary for understanding the mechanisms underlying information processing in the brain. We investigated the organization of horizontal functional interactions in the inferior temporal cortex of macaque monkeys, which plays important roles in visual object recognition. Neuronal activity was recorded from the inferior temporal cortex using an array of eight tetrodes, with spatial separation between paired neurons up to 1.4 mm. We evaluated functional interactions on a time scale of milliseconds using cross-correlation analysis of neuronal activity of the paired neurons. Visual response properties of neurons were evaluated using responses to a set of 100 visual stimuli. Adjacent neuron pairs tended to show strong functional interactions compared with more distant neuron pairs, and neurons with similar stimulus preferences tended to show stronger functional interactions than neurons with different stimulus preferences. Thus horizontal functional interactions in the inferior temporal cortex appear to be organized according to both cortical distances and similarity in stimulus preference between neurons. Furthermore, the relationship between strength of functional interactions and similarity in stimulus preference observed in distant neuron pairs was more prominent than in adjacent pairs. The results suggest that functional circuitry is specifically organized, depending on the horizontal distances between neurons. Such specificity endows each circuit with unique functions.

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