Browsing by Autor "Nahian F. Chowdhury"

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Increases in regional brain volume across two native South American male populations
(Springer International Publishing, 2024) Nikhil N. Chaudhari; Phoebe Imms; Nahian F. Chowdhury; Margaret Gatz; Benjamin C. Trumble; Wendy J. Mack; Emma Law; M. Linda Sutherland; James D. Sutherland; Christopher J. Rowan
P2‐108: USING COMPUTED TOMOGRAPHY TO ASSESS BRAIN VOLUMETRICS IN AGING
(Wiley, 2019) Andrei Irimia; Hillard Kaplan; Ben C. Trumble; Juan J. Copajira Adrian; Alexander S. Maher; Kenneth A. Rostowsky; Nahian F. Chowdhury; M. Linda Sutherland; James D. Sutherland; Adel H. Allam
Although magnetic resonance imaging (MRI) remains the gold standard for the noninvasive evaluation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) volumes in the aging brain, CT continues to be used widely for brain imaging, particularly when MRI is unavailable or contraindicated. In developing countries, CT is often the only imaging modality available for evaluation of brain atrophy in patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD). This has renewed interest in the development of approaches to use head CT to estimate brain volumetrics. A brain segmentation approach was developed to delineate WM, GM and CSF from head CT using probabilistic, atlas-based classification. Feasibility and utility were evaluated by comparing MRI-only to CT-only segmentations in 10 older adults [mean (μ) ± standard deviation (σ) of age = 65 ± 7 yrs; 5 females] from whom both MRI and CT scans were acquired within an eight-week period. Segmentation similarity was quantified using the Dice coefficient (DC), a robust measure of inter-modality tissue classification agreement. Comparison of MRI vs. CT segmentations yielded normally-distributed DCs [μ ± σ across participants: 85.5% ± 4.6% (WM), 86.7% ± 5.6% (GM) and 91.3% ± 2.8% (CSF)], indicating satisfactory ability to calculate brain volumetrics from the CT scans of the participants, relative to MRI measurements. For this sample, bootstrapping suggests that the tissue classification method is sufficiently sensitive to estimate WM, GM and CSF volumes within ∼5%, ∼4% and ∼3% of their MRI-based values, respectively. Compared to MRI, volumes computed from CT displayed no evidence of systematic over- or under-estimation [t (9) = 0.89, p > 0.80]. Our contribution broadens the ability to integrate CT imaging findings with other research on brain aging in health and disease, and complements other methodologies for the study of brain volumetrics in neurodegenerative diseases, including AD.
Segmentation and morphometry of intracranial internal carotid artery calcification in relation to brain atrophy
(Springer Science+Business Media, 2026) Xiao Xu; Nikhil N. Chaudhari; Phoebe Imms; Nahian F. Chowdhury; Fangyun Liu; J.‐M. Galvan; Bavrina Bigjahan; Grant D. Schleifer; Maria Ashna; Blake Hannagan
Intracranial internal carotid artery calcification (iICAC) is a form of intracranial arteriosclerosis and is associated with an elevated risk of stroke and dementia. However, iICAC’s relationship with brain atrophy remains poorly understood. We aimed to automatically quantify iICAC morphometric characteristics and evaluate their associations with regional brain volumes (BVs). We developed an automated approach to compute iICAC surface area and thickness from CT brain scans in a sample of physically active South American subsistence farmers (n = 1,232, age range: 40 years to 92 years, 48.1% female, 794 Tsimane and 438 Moseten). Linear regression models were used to assess associations between two iICAC features and regional BVs, adjusted for age, sex, population, and total intracranial volume. Significant negative relationships were found between regional BVs and iICAC surface area, but not iICAC thickness. Frontal, parietal, temporal, and subcortical BVs exhibited significant negative associations with iICAC surface area (standardized $$\beta$$ range: -0.146 to -0.066, p ≤ 0.013), while the occipital BV did not (standardized $${\beta}_{left}$$ = -0.035, p = 0.249; $${\beta}_{right}$$ = 0.007, p = 0.810). Subcortical BVs demonstrated the strongest negative associations with iICAC surface area (standardized $${\beta}_{left}$$ = -0.146, p < 0.001; $${\beta}_{right}$$ = -0.139, p < 0.001). iICAC surface area—assumed to reflect arterial stiffness—shows a stronger relationship with regional BV loss than iICAC thickness—assumed to indicate arterial stenosis. The findings suggest that brain regions primarily supplied by the anterior circulation are more vulnerable to iICAC-related atrophy. Subcortical BVs showed the strongest negative associations with iICAC surface area, with region-specific analyses identifying significant effects in the putamen, thalamus, hippocampus, amygdala, pallidum, and ventral diencephalon, suggesting heightened vulnerability of deep gray-matter structures to iICAC-related atrophy.