Morphometric Analysis of the Corpus Callosum According to Age and Sex in Middle Eastern Arabs: Racial Comparisons and Clinical Correlations to Autism Spectrum Disorder

Allouh, Mohammed Z.; Barbarawi, Mohammed M Al; Ali, Heba; Mustafa, Ayman G.; Alomari, Safwan

doi:10.3389/fnsys.2020.00030

Cited by 15 publications

(13 citation statements)

References 54 publications

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“…Initially the length and thickness were calculated in terms of number of pixels and further the pixel dimension was converted into millimeter scale multiplied by 0.9375 mm (1 pixel is equal to 0.9375 mm for the segmented images in our dataset). The converted length of the healthy CCs is around 68.03 mm on an average which is very similar to the range of actual CC length which is in older male and older female adults were 70.6 ± 3.4 mm and 68.5 ± 4.5 mm, respectively [21]. We have also observed increased perimeter and bending coefficient of CC in mild and moderate AD patients with respect to healthy samples leading to a reduced area of CC (Supplementary Figures 2 and 3).…”

Section: Reduction Of CC Length and Thicknesssupporting

confidence: 76%

“…Corpus callosum (CC) is the largest fiber bundle connecting the two cerebral hemispheres. Earlier, researchers have found structural changes in the rostrum and genu, rostral body, mid body, isthmus, and splenium of CC [20][21][22] may lead to functional disability and MCI. Zhu et al [23] confirmed CC atrophy occurs at an early stage of AD due to reduced inter-hemispheric integration.…”

Section: Introductionmentioning

confidence: 99%

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Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Das

Panigrahi

Chakrabarti

2021

ADR

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Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculate its morphological features to feed into a multivariate pattern analysis using support vector machine learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

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Section: Reduction Of CC Length and Thicknesssupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Das

Panigrahi

Chakrabarti

2021

ADR

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“…Race and ethnicity have both been associated with unique phenotypic traits. For example, studies have observed significant differences in the corpus callosum between sub-populations of the Middle East (Allouh et al 2020 ; Hosseini et al 2018 ). In a study comparing brain volumes from healthy White ( n = 44) and Black or African American ( n = 25) individuals, volumetric differences were observed in the left orbitofrontal cortex (Isamah et al 2010 ).…”

Section: Challenges In Reporting Race and Ethnicity In Biomedical Res...mentioning

confidence: 99%

“…Health disparities, disease presentation, and patient outcomes are influenced by a wide range of factors including sex, race, and ethnicity (Tang et al 2022 ; Shakeshaft et al 2022 ; Nahar et al 2022 ; Tseng, et al 2020 ). In biomedical and neuroimaging research, differences across demographic groups have been observed in brain structure, anatomy, morphometry, connectivity, development, and disease (Allouh et al 2020 ; Bonkhoff et al 2021 ; Chand et al 2017 ; Delvecchio et al 2021 ; Giedd et al 2012 ; Hosseini et al 2018 ; Liu et al 2020 ). Despite documented differences between demographic groups, historical underrepresentation of minority and female participants has resulted in clinical and biomedical best practices informed by conclusions made using a narrow subset of the population (Brady et al 2021 ; Sardar et al 2014 ; Clayton and Collins 2014 ).…”

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confidence: 99%

Demographic reporting across a decade of neuroimaging: a systematic review

Sterling

Pearl

Liu

et al. 2022

Brain Imaging and Behavior

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Diversity of participants in biomedical research with respect to race, ethnicity, and biological sex is crucial, particularly given differences in disease prevalence, recovery, and survival rates between demographic groups. The objective of this systematic review was to report on the demographics of neuroimaging studies using magnetic resonance imaging (MRI). The Web of Science database was used and data collection was performed between June 2021 to November 2021; all articles were reviewed independently by at least two researchers. Articles utilizing MR data acquired in the United States, with n ≥ 10 human subjects, and published between 2010–2020 were included. Non-primary research articles and those published in journals that did not meet a quality control check were excluded. Of the 408 studies meeting inclusion criteria, approximately 77% report sex, 10% report race, and 4% report ethnicity. Demographic reporting also varied as function of disease studied, participant age range, funding, and publisher. We anticipate quantitative data on the extent, or lack, of reporting will be necessary to ensure inclusion of diverse populations in biomedical research.

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“…In recent years, many morphometry studies have been published investigating the relationship between the appearance of clinical signs of various developmental disorders and pathological conditions and the morphology of the CC (5)(6)(7) . Different pathological processes such as mental disorders, dyslexia, autism, speech dysfunction, seizure, and Alzheimer's disease were shown to cause changes in the morphology of the CC (8)(9)(10) . Different measurement methods and studies published have shown that the shape and dimensions of the CC can vary according to age, gender, size of the brain and societies (11,12) .…”

Section: Introductionmentioning

confidence: 99%

The Utility of the Callosal/Supratentorial-Supracallosal Area Ratio to Evaluate Corpus Callosum Morphometry in Children

Öztürk¹,

Uysal²,

Duran³

et al. 2020

BMJ

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Objective: To perform morphometric analysis of corpus callosum (CC) by using callosal area (CA), supratentorial-supracallosal area (SSA) and CA/SSA parameters in a healthy pediatric population and to investigate changes according to age and gender. Method: Method: This retrospective study included a total of 313 children (154 boys, 159 girls) aged between 3-17 years. The cases were divided into three groups according to age: 3-6 years (Group 1) (pre-school), 7-12 years (Group 2) (preadolescent) and 13-17 years (Group 3) (adolescent). CA and SSA were measured on the mid-sagittal plane on T1-weighted images. CA/ SSA index was calculated. Differences in age, CA, SSA, and ratio parameters among the gender groups were compared using the Mann-Whitney U or the t-test. Results: Median values of CA (p= 0.002), mean values of SSA (p=0.001) and CA/SSA ratios (p= 0.04) were significantly higher in boys compared to girls. The median CA and mean CA/SSA ratios in Group 3 were significantly higher than Groups 1 and 2 (p= 0.001). Mean CA/SSA ratio values of boys and girls in Age Group 3 were significantly higher than Group 1 (p= 0.001) and significantly higher than Age Group 2 in girls. There were highly significant positive correlations of age with CA (p=0.001, r=0.47), SSA (p=0.028, r=0.12) and CA/SSA ratio (p=0.001, r=042). There was a highly significant and positive correlation between CA and SSA (p=0.001, r=0.25) and CA/SSA ratio (p=0.001, r=0.87). Conclusion: CA, SSA, and CA/ SSA ratio values in children are affected by age and gender. These parameters can be used as reference values for the diagnosis of congenital and acquired pathologies affecting the corpus callosum.

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Morphometric Analysis of the Corpus Callosum According to Age and Sex in Middle Eastern Arabs: Racial Comparisons and Clinical Correlations to Autism Spectrum Disorder

Cited by 15 publications

References 54 publications

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Demographic reporting across a decade of neuroimaging: a systematic review

The Utility of the Callosal/Supratentorial-Supracallosal Area Ratio to Evaluate Corpus Callosum Morphometry in Children

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