16 research outputs found
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
We predicted residual fluid intelligence scores from T1-weighted MRI data
available as part of the ABCD NP Challenge 2019, using morphological similarity
of grey-matter regions across the cortex. Individual structural covariance
networks (SCN) were abstracted into graph-theory metrics averaged over nodes
across the brain and in data-driven communities/modules. Metrics included
degree, path length, clustering coefficient, centrality, rich club coefficient,
and small-worldness. These features derived from the training set were used to
build various regression models for predicting residual fluid intelligence
scores, with performance evaluated both using cross-validation within the
training set and using the held-out validation set. Our predictions on the test
set were generated with a support vector regression model trained on the
training set. We found minimal improvement over predicting a zero residual
fluid intelligence score across the sample population, implying that structural
covariance networks calculated from T1-weighted MR imaging data provide little
information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD
Neurocognitive Prediction Challenge at MICCAI 201
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
We applied several regression and deep learning methods to predict fluid
intelligence scores from T1-weighted MRI scans as part of the ABCD
Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel
intensities and probabilistic tissue-type labels derived from these as features
to train the models. The best predictive performance (lowest mean-squared
error) came from Kernel Ridge Regression (KRR; ), which produced a
mean-squared error of 69.7204 on the validation set and 92.1298 on the test
set. This placed our group in the fifth position on the validation leader board
and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at
MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl
The location of the axon initial segment affects the bandwidth of spike initiation dynamics
The dynamics and the sharp onset of action potential (AP) generation have recently been the subject of intense experimental and theoretical investigations. According to the resistive coupling theory, an electrotonic interplay between the site of AP initiation in the axon and the somato-dendritic load determines the AP waveform. This phenomenon not only alters the shape of AP recorded at the soma, but also determines the dynamics of excitability across a variety of time scales. Supporting this statement, here we generalize a previous numerical study and extend it to the quantification of the input-output gain of the neuronal dynamical response. We consider three classes of multicompartmental mathematical models, ranging from ball-and-stick simplified descriptions of neuronal excitability to 3D-reconstructed biophysical models of excitatory neurons of rodent and human cortical tissue. For each model, we demonstrate that increasing the distance between the axonal site of AP initiation and the soma markedly increases the bandwidth of neuronal response properties. We finally consider the Liquid State Machine paradigm, exploring the impact of altering the site of AP initiation at the level of a neuronal population, and demonstrate that an optimal distance exists to boost the computational performance of the network in a simple classification task. Copyright
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and Kernel ridge regression
We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from kernel ridge regression (), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board