Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors

IntroductionThe difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG.MethodsT1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction.ResultsThe combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers.DiscussionIn conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging

Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data

Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain regions triggering seizures. Spike detection is often performed manually. However, it is a burdensome and error prone task due to the complexity of MEG data. To address this problem, we propose a 1D temporal convolutional neural network (Time CNN) coupled with a graph convolutional network (GCN) to classify short time frames of MEG recording as containing a spike or not. Compared to other recent approaches, our models have fewer parameters to train and we propose to use a GCN to account for MEG sensors spatial relationships. Our models produce clinically relevant results and outperform deep learning-based state-of-the-art methods reaching a classification f1-score of 76.7% on a balanced dataset and of 25.5% on a realistic, highly imbalanced dataset, for the spike class.Comment: This work has been submitted to IEEE ISBI 2024 for possible publicatio

Age-dependent analysis of cerebral structures and arteries in a large database

Aging of the population is expected to lead to a rapid increase of neurological diseases. Such diseases can progress quickly and detrimentally affect the daily life of patients. Prognosis improves with early diagnosis, but early detection is diffcult. It is crucial to be able to differentiate early stage pathological alteration from normal age-related changes. Thus, there is a need for a better understanding of brain aging and reliable biomarkers. Within that context, the overarching aim of this work is to study normal aging patterns in brain tissues and arteries using a large database of magnetic resonance imaging and angiography data, as well as cardiovascular risk factors from the whole adult life span. To do so, the objectives of this thesis are: (1) to quantify artery morphology variability among adults and identify the impact of age, sex and cardiovascular risk factors on cerebrovascular structures; (2) to combine brain tissue and artery information for biological brain age prediction; (3) to explore the impact of cardiovascular risk factors on the brain age gap, which is a biomarker representing the difference between the biological brain age and chronological age. To achieve these objectives, first, a statistical cerebrovascular atlas is generated from multi-centre adult data. Image analyses and multivariate regression methods are then employed to find associations between brain artery morphology and aging. Second, multi-modal explainable deep learning models are used to accurately estimate the biological brain age and identify predictive brain regions. Third, an exploratory causal analysis is performed to isolate the effects of individual factors on the brain age gap. The results of this work offer a novel insight on brain tissue and artery aging patterns. An in-depth analysis of the brain age gap biomarker is carried out. Novel approaches are proposed to improve brain age prediction models in terms of accuracy and explainability. Finally, innovative methods are used to study cause and effects relationships between brain aging and cardiovascular risk factors. This work aims to uncover clinically relevant findings and represents valuable methodological advancements that could be used in other neuroimaging clinical applications, for instance, to ameliorate predictive models for decision-support

Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data

Magnetoencephalography (MEG) recordings of patients with epilepsy exhibit spikes, a typical biomarker of the pathology. Detecting those spikes allows accurate localization of brain regions triggering seizures. Spike detection is often performed manually. However, it is a burdensome and error prone task due to the complexity of MEG data. To address this problem, we propose a 1D temporal convolutional neural network (Time CNN) coupled with a graph convolutional network (GCN) to classify short time frames of MEG recording as containing a spike or not. Compared to other recent approaches, our models have fewer parameters to train and we propose to use a GCN to account for MEG sensors spatial relationships. Our models produce clinically relevant results and outperform deep learning-based state-of-the-art methods reaching a classification f1-score of 76.7% on a balanced dataset and of 25.5% on a realistic, highly imbalanced dataset, for the spike class

Respiratory motion model derived from CBCT projection data

International audienc

Multimodal imaging measures in the prediction of clinical response to deep brain stimulation for refractory depression: A machine learning approach

This study compared machine learning models using unimodal imaging measures and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome prediction in treatment resistant depression (TRD). Regional brain glucose metabolism (CMRGlu), cerebral blood flow (CBF), and grey matter volume (GMV) were measured at baseline using 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography (PET), arterial spin labelling (ASL) magnetic resonance imaging (MRI), and T1-weighted MRI, respectively, in 19 patients with TRD receiving subcallosal cingulate (SCC)-DBS. Responders (n = 9) were defined by a 50% reduction in HAMD-17 at 6 months from the baseline. Using an atlas-based approach, values of each measure were determined for pre-selected brain regions. OneR feature selection algorithm and the naïve Bayes model was used for classification. Leave-out-one cross validation was used for classifier evaluation. The performance accuracy of the CMRGlu classification model (84%) was greater than CBF (74%) or GMV (74%) models. The classification model using the three image modalities together led to a similar accuracy (84%0 compared to the CMRGlu classification model. CMRGlu imaging measures may be useful for the development of multivariate prediction models for SCC-DBS studies for TRD. The future of multivariate methods for multimodal imaging may rest on the selection of complementing features and the developing better models.Clinical Trial Registration: ClinicalTrials.gov (#NCT 01983904)</p

Aerobic exercise increases brain vessel lumen size and blood flow in young adults with elevated blood pressure. Secondary analysis of the TEPHRA randomized clinical trial

Importance: Cerebrovascular changes are already evident in young adults with hypertension and exercise is recommended to reduce cardiovascular risk. To what extent exercise benefits the cerebrovasculature at an early stage of the disease remains unclear. Objective: To investigate whether structured aerobic exercise increases brain vessel lumen diameter or cerebral blood flow (CBF) and whether lumen diameter is associated with CBF. Design: Open, parallel, two-arm superiority randomized controlled (1:1) trial in the TEPHRA study on an intention-to-treat basis. The MRI sub-study was an optional part of the protocol. The outcome assessors remained blinded until the data lock. Setting: Single-centre trial in Oxford, UK. Participants: Participants were physically inactive (37 weeks). Intervention: Study participants were randomised to a 16 week aerobic exercise intervention targeting 3×60 min sessions per week at 60 to 80 % peak heart rate. Main outcomes and Measures: cerebral blood flow (CBF) maps from ASL MRI scans, internal carotid artery (ICA), middle cerebral artery (MCA) M1 and M2 segments, anterior cerebral artery (ACA), basilar artery (BA), and posterior cerebral artery (PCA) diameters extracted from TOF MRI scans. Results: Of the 135 randomized participants (median age 28 years, 58 % women) who had high quality baseline MRI data available, 93 participants also had high quality follow-up data available. The exercise group showed an increase in ICA (0.1 cm, 95 % CI 0.01 to 0.18, p =.03) and MCA M1 (0.05 cm, 95 % CI 0.01 to 0.10, p =.03) vessel diameter compared to the control group. Differences in the MCA M2 (0.03 cm, 95 % CI 0.0 to 0.06, p =.08), ACA (0.04 cm, 95 % CI 0.0 to 0.08, p =.06), BA (0.02 cm, 95 % CI −0.04 to 0.09, p =.48), and PCA (0.03 cm, 95 % CI −0.01 to 0.06, p =.17) diameters or CBF were not statistically significant. The increase in ICA vessel diameter in the exercise group was associated with local increases in CBF. Conclusions and Relevance: Aerobic exercise induces positive cerebrovascular remodelling in young people with early hypertension, independent of blood pressure. The long-term benefit of these changes requires further study. Trial Registration: Clinicaltrials.gov NCT02723552, 30 March 201