Biology subjects

Hariri, A.

Publications and source records attributed to Hariri, A..

5 recordsLinked to original sources

bioRxiv2024

Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking

Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning "stacking" approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old). For predictability, stacked models led to out-of-sample r[~].5-.6 when predicting cognitive abilities at the time of scanning, primarily driven by task-fMRI contrasts. Notably, using the Dunedin Study, we were able to predict participants cognitive abilities at ages 7, 9, and 11 using their multimodal MRI at age 45, with an out-of-sample r of 0.52. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities. Significance statementScientists have had limited success in predicting cognitive abilities from brain MRI. We proposed a machine learning method, called stacking, to draw information across different types of brain MRI. Using three large databases (n=2,131, 22-100 years old), we found stacking to make the prediction of cognitive abilities 1) closer to actual cognitive scores when applied to a new individual, not part of the modelling process, 2) reliable over times and 3) applicable to the data collected from different age groups and MRI scanners. Indeed, stacking, especially with fMRI task contrasts, allowed us to use MRI of people aged 45 to predict their childhood cognitive abilities reasonably well. Accordingly, stacking may help MRI realise its potential to predict cognitive abilities.

neuroscience↗

bioRxiv2022

Characterization of a miniaturized ultrasound transducer customized for oral health applications

ObjectiveTo customize a miniaturized ultrasound transducer to access full-mouth B-mode, color Doppler and spectral Doppler imaging for monitoring oral health. MethodsA customized periodontal ultrasound transducer SS-19-128 (19 MHz, 128 channels) with 1.8 cm wide and 1 cm thick was developed and connected to a data acquisition (DAQ) system. B-mode, color Doppler, and spectral Doppler data could all be collected with SS-19-128. The imaging resolution and penetration capacity of SS-19-128 were characterized on phantoms. Five human subjects were recruited to demonstrate B-mode and Doppler imaging by SS-19-128. Gingival thickness was measured on 11 swine teeth by SS-19-128 for comparison to conventional transgingival probing via Bland-Altman analysis and Pearson correlation. ResultsThe axial and lateral spatial resolution at 5.5 mm depth is 102.1 m and 142.9 m, respectively. The penetration depth in a tissue-mimicking phantom is over 30 mm. In vivo B-mode imaging of all 28 teeth was demonstrated on one human subject, and imaging of tooth #18 was accessed on five human subjects. Gingival thickness measurement compared with transgingival probing showed a bias of -0.015 mm and SD of 0.031 mm, and a r = 0.9235 (P<0.0001) correlation. In vivo color and spectral Doppler imaging of the supraperiosteal artery in human gingiva was performed to generate hemodynamic information. ConclusionsThe small size of SS-19-128 offers important advantages over existing technology--more specifically, whole-mouth scanning/charting reminiscent of radiography. This is nearly a two-fold increase in the number of teeth that can be assessed versus existing transducers.

bioengineering↗

bioRxiv2022

Full-mouth photoacoustic/ultrasound imaging of the periodontal pocket with a compact intraoral transducer

Periodontitis is a public issue and imaging periodontal pocket is important to evaluate periodontitis. Regular linear transducers have limitations in imaging the posterior teeth due to their geometry restrictions. Here we characterized a transducer that can image the entire human mouth including assessment of periodontal pockets via a combination of photoacoustic and ultrasound imaging. Unlike conventional transducer design, this device has a toothbrush-shaped form factor with a side-view transducer to image molars (total size: 1 x 1.9 cm). A laser diode was integrated as the light source to reduce the cost and size and facilitates clinical transition. The in vivo imaging of a molar of a periodontal patient demonstrated that the transducer could image in the posterior area of gum in vivo; the value determined by imaging was within 7% of the value measured clinically.

bioengineering↗

bioRxiv2019

Poor test-retest reliability of task-fMRI: New empirical evidence and a meta-analysis

Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task-fMRI is a major focus of biomarker development; however, the reliability of task-fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task-fMRI measures. First, a meta-analysis of 90 experiments (N=1,008) revealed poor overall reliability (mean ICC=.397). Second, the test-retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected in the context of the Human Connectome Project (N=45) and the Dunedin Study (N=20) were poor (ICCs=.067-.485). Collectively, these findings demonstrate that common task-fMRI measures are not currently suitable for brain biomarker discovery or individual differences research. We review how this state of affairs came to be and highlight avenues for improving task-fMRI reliability.

neuroscience↗

bioRxiv2017

Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

AbstractSusceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3x10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

neuroscience↗