1 code implementation • 25 May 2020 • Seyed Mostafa Kia, Hester Huijsdens, Richard Dinga, Thomas Wolfers, Maarten Mennes, Ole A. Andreassen, Lars T. Westlye, Christian F. Beckmann, Andre F. Marquand
Clinical neuroimaging has recently witnessed explosive growth in data availability which brings studying heterogeneity in clinical cohorts to the spotlight.
1 code implementation • 12 Dec 2018 • Seyed Mostafa Kia, Andre F. Marquand
Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders.
no code implementations • 4 Jun 2018 • Seyed Mostafa Kia, Andre Marquand
Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical cohorts.
no code implementations • 14 Sep 2017 • Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello
Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors.
no code implementations • 17 Jun 2016 • Seyed Mostafa Kia, Andrea Passerini
Despite extensive studies of this type, at present, there is no formal definition for interpretability of brain decoding models.
no code implementations • 5 Nov 2015 • Nastaran Mohammadian Rad, Andrea Bizzego, Seyed Mostafa Kia, Giuseppe Jurman, Paola Venuti, Cesare Furlanello
Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility.
1 code implementation • 29 Mar 2016 • Seyed Mostafa Kia
In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness.
no code implementations • 24 Feb 2014 • Seyed Mostafa Kia, Hossein Rahmani, Reza Mortezaei, Mohsen Ebrahimi Moghaddam, Amer Namazi
To test the proposed method, performance of system was evaluated over 18354 download images from internet.
no code implementations • 25 Jun 2014 • Seyed Mostafa Kia
Recent advances in statistical theory, together with advances in the computational power of computers, provide alternative methods to do mass-univariate hypothesis testing in which a large number of univariate tests, can be properly used to compare MEEG data at a large number of time-frequency points and scalp locations.
no code implementations • 16 Apr 2014 • Emanuele Olivetti, Seyed Mostafa Kia, Paolo Avesani
On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning.
no code implementations • 31 Jul 2018 • Seyed Mostafa Kia, Christian F. Beckmann, Andre F. Marquand
Most brain disorders are very heterogeneous in terms of their underlying biology and developing analysis methods to model such heterogeneity is a major challenge.
no code implementations • 3 Jun 2022 • Seyed Mostafa Kia, Nastaran Mohammadian Rad, Daniel van Opstal, Bart van Schie, Andre F. Marquand, Josien Pluim, Wiepke Cahn, Hugo G. Schnack
In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know).