Biology subjects

Ardekani, B. A.

Publications and source records attributed to Ardekani, B. A..

4 recordsLinked to original sources

bioRxiv2025

Neuroanatomical features reveal accelerated brain age in alcohol use disorder

Background/ObjectivesBrain age is a novel measure to characterize the integrity of neurocognitive functioning and brain health in various psychiatric and neurological disorders. Although there is a literature suggesting premature aging of the brain in individuals with alcohol use disorder (AUD), studies directly examining brain age are rare. Therefore, the current study was designed to estimate brain age in AUD individuals using brain morphological features, such as cortical thickness and brain volume. MethodsThe sample included a group of 30 adult males with a history of AUD but maintaining abstinence and a group of 30 male controls without any history of AUD. Brain age was computed using an XGBoost regression model with 187 brain morphological features of cortical thickness and brain volume as predictors. An exploratory correlational analysis between brain age measures and features of neuropsychological performance, impulsivity, and alcohol consumption was also performed. ResultsFindings revealed that AUD individuals showed an increase of 1.70 years in their brain age relative to their chronological age. Further, in the AUD group, higher brain age was significantly associated with poor executive functioning, while a larger gap between brain age and actual age was associated with lower non-planning impulsivity and later age of onset for regular drinking in those with AUD. ConclusionsAUD individuals manifested accelerated brain aging, possibly representing compromised brain health. Brain age measures were found to be associated with some of the measures of neurocognition, impulsivity, and alcohol consumption. These findings may have important implications for the early identification, prevention, and treatment of AUD. However, future studies with larger sample sizes are needed to confirm these preliminary findings.

neuroscience↗

bioRxiv2024

A comparative study of rigid-body registration algorithms for the alignment of longitudinal structural MRI of the brain

Longitudinal structural MRI (sMRI) may be used to characterize brain morphological changes over time. A key requirement for this approach is accurate rigid-body alignment of longitudinal sMRI. We have recently developed the automatic temporal registration algorithm (ATRA) for this purpose. ATRA is a landmark-based approach capable of registering dozens of serial sMRI simultaneously in an unbiased manner. The aim of the research presented in this paper was to evaluate the accuracy and inverse-consistency of ATRA in comparison to three commonly used sMRI alignment methods: FSL, FreeSurfer, and ANTS. In the absence of a ground truth, it is only possible to quantitatively determine the degree of discrepancy between two algorithms. We propose that if the discrepancy exceeds a certain threshold, the relative accuracy of the two algorithms could be determined visually. We computed the discrepancy between ATRA and each of the three other methods for the alignment of 150 pairs of sMRI taken roughly one year apart. We visually rated the accuracy of alignments in cases where the discrepancy was greater than .5 mm while the rater was agnostic to the registration method. In those instances, ATRA was considered more accurate than FSL in 46 out of 48 cases, more accurate than FreeSurfer in 6 out of 7 cases, and more accurate than ANTS in all 6 cases. ATRA was also the most inverse-consistent method. In addition to being capable of performing unbiased group-wise registration, ATRA is the most accurate algorithm in comparison to several commonly used rigid-body alignment methods.

neuroscience↗

bioRxiv2024

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.

neuroscience↗

bioRxiv2018

A New Approach to Symmetric Registration of Longitudinal Structural MRI of the Human Brain

This technical report presents the Automatic Temporal Registration Algorithm (ATRA) for symmetric rigid-body affine registration of longitudinal T1-weighted three-dimensional magnetic resonance imaging (MRI) scans of the human brain. This is a fundamental processing step in computational neuroimaging. The notion of leave-one-out consistent (LOOC) landmarks with respect to a supervised landmark detection algorithm is introduced. An automatic algorithm is presented for identification of LOOC landmarks on MRI scans. Using this technique, multiple sets of LOOC landmarks (around 150) are identified on each of the volumes being registered. Then, a Generalized Orthogonal Procrustes Analysis of the identified landmarks is used to find a rigid-body transformation of each volume into a common space where the transformed volumes are precisely aligned. In addition, a new approach is introduced for quantitative assessment of registration accuracy in the absence of a gold standard. Qualitative and quantitative evaluations of ATRA registration accuracy are performed using 2012 volumes from 503 subjects (4 longitudinal volumes/subject) from the Alzheimers Disease Neuroimaging Initiative database, and on a further 120 volumes acquired from 3 normal subjects (40 longitudinal volumes/subject). The algorithm is symmetric, in the sense that any permutation of the input volumes does not change the resulting transformation matrices, and unbiased, in the sense that all volumes undergo one and only one interpolation operation, which precisely aligns them in a common space. There is no interpolation bias and no reference volume. All volumes are treated exactly the same. The algorithm is fast and highly accurate. The software is publicly available.

bioinformatics↗