Machine learning evaluates changes in functional connectivity under a prolonged cognitive load

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Machine learning evaluates changes in functional connectivity under a prolonged cognitive load. / Frolov, Nikita ; Kabir, Muhammad Salman ; Максименко, Владимир ; Hramov, Alexander .

In: Chaos, Vol. 31, No. 10, 101106, 01.10.2021.

Research output: Contribution to journal › Article › peer-review

Author

Frolov, Nikita ; Kabir, Muhammad Salman ; Максименко, Владимир ; Hramov, Alexander . / Machine learning evaluates changes in functional connectivity under a prolonged cognitive load. In: Chaos. 2021 ; Vol. 31, No. 10.

BibTeX

@article{5aa74165fbe944f5976cc08b9f6653aa,

title = "Machine learning evaluates changes in functional connectivity under a prolonged cognitive load",

abstract = "One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.",

author = "Nikita Frolov and Kabir, {Muhammad Salman} and Владимир Максименко and Alexander Hramov",

note = "Publisher Copyright: {\textcopyright} 2021 Author(s).",

year = "2021",

month = oct,

day = "1",

doi = "10.1063/5.0070493",

language = "English",

volume = "31",

journal = "Chaos",

issn = "1054-1500",

publisher = "American Institute of Physics",

number = "10",

}

RIS

TY - JOUR

T1 - Machine learning evaluates changes in functional connectivity under a prolonged cognitive load

AU - Frolov, Nikita

AU - Kabir, Muhammad Salman

AU - Максименко, Владимир

AU - Hramov, Alexander

PY - 2021/10/1

Y1 - 2021/10/1

N2 - One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.

AB - One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.

UR - https://aip.scitation.org/doi/abs/10.1063/5.0070493?journalCode=cha

UR - https://pubmed.ncbi.nlm.nih.gov/34717312/

UR - http://www.scopus.com/inward/record.url?scp=85118826045&partnerID=8YFLogxK

U2 - 10.1063/5.0070493

DO - 10.1063/5.0070493

M3 - Article

VL - 31

JO - Chaos

JF - Chaos

SN - 1054-1500

IS - 10

M1 - 101106

ER -

ID: 88675832