Preprocessing FMRI Data In SPM12

Bazay, Fatima Ez-Zahraa; Benabdallah, Fatima Zahra; Maliani, Ahmed Drissi El

doi:10.1109/wincom55661.2022.9966463

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…After data preprocessing, the fMRI data were analyzed using the SPM package [12] , a voxel-based statistical method, and the brain was divided into 90 brain regions according to the AAL template [13] , and the activation intensity and clump size were accurately calculated for each brain region, and the results were presented as a trilinear table. The results were visualized using MRIcron, BrainNet to visualize the brain activation regions and the size of the activated regions more visually.…”

Section: Fmri Data Analysismentioning

confidence: 99%

The neural mechanisms tone recognition in deaf children: an fMRI study

Li,

Meng

2023

International Workshop on Signal Processing and Machine Learning (WSPML 2023)

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Section: Fmri Data Analysismentioning

confidence: 99%

The neural mechanisms tone recognition in deaf children: an fMRI study

Li,

Meng

2023

International Workshop on Signal Processing and Machine Learning (WSPML 2023)

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“…After data preprocessing, the fMRI data were analyzed using the SPM package [8] , a voxel-based statistical method, and the brain was divided into 90 brain regions according to the AAL template [9] , and the activation intensity and clump size were accurately calculated for each brain region, and the results were presented as a trilinear table. The results were visualized using MRIcron, BrainNet to visualize the brain activation regions and the size of the activated regions more visually.…”

Section: Fmri Data Analysismentioning

confidence: 99%

An fMRI study of the neural mechanisms of second and fourth tone recognition in deaf children

Meng¹,

Li²

2023

AEHSSR

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Tone is an important component of language and it plays a key role in language comprehension and communication. However, children with hearing loss face challenges in vocal tone recognition due to hearing impairment. In this study, five deaf children and two children with normal hearing were recruited to compare the differences in second and fourth tone recognition tasks between deaf and normal children. The results found that (1) some of the deaf children's brain regions for processing vocal tones did not work properly due to hearing loss; (2) deaf children may rely on different neural networks when processing vocal tone information. (3) Deaf children process vocal tone information with hemispheric characteristics.

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Section: Fmri Data Analysismentioning

confidence: 99%

An fMRI study of the neural mechanisms of third and fourth tone recognition in deaf children

Meng,

2023

AEHSSR

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Vocal tone is an important component of language and it plays a key role in language comprehension and communication. However, children with hearing loss face challenges in vocal tone recognition due to hearing impairment. In this study, five deaf children and two children with normal hearing were recruited to compare the differences in third and fourth tone recognition tasks between deaf and normal children. The results revealed that (1) some of the deaf children's brain regions that process vocal tones did not work properly due to hearing loss; (2) deaf children may rely on different neural networks when processing vocal tone information. (3) Deaf children process vocal tone information with hemispheric characteristics.

show abstract

Preprocessing FMRI Data In SPM12

Cited by 4 publications

References 18 publications

The neural mechanisms tone recognition in deaf children: an fMRI study

The neural mechanisms tone recognition in deaf children: an fMRI study

An fMRI study of the neural mechanisms of second and fourth tone recognition in deaf children

An fMRI study of the neural mechanisms of third and fourth tone recognition in deaf children

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