Pal Vakli
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Explore the profile of Pal Vakli including associated specialties, affiliations and a list of published articles.
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17
Citations
58
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Recent Articles
1.
Vakli P, Weiss B, Rozmann D, Eross G, Narai A, Hermann P, et al.
Neuroimage
. 2024 May;
294:120646.
PMID: 38750907
Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and...
2.
Narai A, Hermann P, Radosi A, Vakli P, Weiss B, Rethelyi J, et al.
Res Child Adolesc Psychopathol
. 2024 Mar;
52(7):1063-1074.
PMID: 38483760
Methods: Aim was to examine, whether brain charts can be applied in a sample of adolescents (N = 140, 38% female) to determine whether atypical brain subcortical and total volumes...
3.
Banko E, Weiss B, Hevesi I, Manga A, Vakli P, Havadi-Nagy M, et al.
Ideggyogy Sz
. 2024 Feb;
77(1-2):51-59.
PMID: 38321854
Background And Purpose: Neurocognitive aging and the associated brain diseases impose a major social and economic burden. Therefore, substantial efforts have been put into revealing the lifestyle, the neurobiological and...
4.
Vakli P, Weiss B, Szalma J, Barsi P, Gyuricza I, Kemenczky P, et al.
Med Image Anal
. 2023 Jun;
88:102850.
PMID: 37263108
Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. For this reason, several machine learning-based methods have been...
5.
Narai A, Hermann P, Auer T, Kemenczky P, Szalma J, Homolya I, et al.
Sci Data
. 2022 Oct;
9(1):630.
PMID: 36253426
Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing,...
6.
Kemenczky P, Vakli P, Somogyi E, Homolya I, Hermann P, Gal V, et al.
Sci Rep
. 2022 Feb;
12(1):1618.
PMID: 35102199
Due to their robustness and speed, recently developed deep learning-based methods have the potential to provide a faster and hence more scalable alternative to more conventional neuroimaging analysis pipelines in...
7.
Manga A, Madurka P, Vakli P, Kirwan C, Vidnyanszky Z
Learn Mem
. 2021 Mar;
28(4):109-113.
PMID: 33723030
Binding visual features into coherent object representations is essential both in short- and long-term memory. However, the relationship between feature binding processes at different memory delays remains unexplored. Here, we...
8.
Manga A, Vakli P, Vidnyanszky Z
Sci Rep
. 2020 Jun;
10(1):8817.
PMID: 32483177
Motivation exerts substantial control over cognitive functions, including working memory. Although it is well known that both motivational control and working memory processes undergo a progressive decline with ageing, whether...
9.
Vakli P, Deak-Meszlenyi R, Auer T, Vidnyanszky Z
Front Neuroinform
. 2020 Apr;
14:10.
PMID: 32265681
In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient...
10.
Vakli P, Deak-Meszlenyi R, Hermann P, Vidnyanszky Z
Gigascience
. 2018 Nov;
7(12).
PMID: 30395218
Background: Deep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive...