Biology subjects

Rajabli, R.

Publications and source records attributed to Rajabli, R..

4 recordsLinked to original sources

bioRxiv2025

MRI-based classifier to identify close-to-onset cases in C9orf72 genetic frontotemporal dementia

Predicting symptom onset in genetic frontotemporal dementia (FTD) is crucial for advancing targeted interventions and clinical trial design. Brain changes begin years before clinical symptoms emerge, making neuroimaging a strong candidate for onset prediction. However, FTD is highly heterogeneous, encompassing diverse molecular pathologies, affected brain networks, and symptom trajectories. This variability limits the predictive power of any single imaging biomarker and underscores the need for an integrative, multimodal approach to improve prediction accuracy and generalizability. We used machine learning to integrate diverse neuroimaging features, identifying a robust signature for risk stratification. We analyzed T1-weighted and T2-weighted MRI scans from 71 symptomatic C9orf72 carriers, 90 presymptomatic carriers, and 69 healthy controls from the GENFI cohort. We used FreeSurfer to measure cortical thickness and subcortical volumes, and BISON to quantify white matter hyperintensities (WMH). We applied Principal Component Analysis for dimensionality reduction and trained a random forest classifier to distinguish symptomatic carriers from controls. The model was subsequently applied to the presymptomatic cohort to identify individuals whose brain patterns resembled those of symptomatic cases, under the hypothesis that greater similarity indicated a higher risk of conversion. We validated the model with neuropsychological data and a two-year longitudinal follow-up. The classifier distinguished symptomatic C9orf72 carriers from controls with 87.0% accuracy. When applied to presymptomatic carriers, the model identified 21.1% of the cohort as having brain features comparable to those of symptomatic cases. This "high-risk group" showed significant neuropsychological weaknesses in executive function, language and social cognition compared to the non high-risk group. The model accurately predicted clinical conversion within a two-year period with 84.5% accuracy, a 70% sensitivity and a 93.3% negative predictive value. Our findings demonstrate the utility of a machine learning approach using multi-modal MRI to identify presymptomatic C9orf72 carriers at high risk of disease onset within the next two years. By capturing subtle neuroanatomical patterns associated with disease processes, this approach offers a promising method for stratifying genetic FTD carriers prior to symptom onset. Such predictive models could optimize patient selection in future clinical trials.

neuroscience↗

bioRxiv2025

White Matter Hyperintensities Precede other Biomarkers in GRN Frontotemporal Dementia

INTRODUCTIONIncreased white matter hyperintensities (WMHs) have been reported in genetic frontotemporal dementia (FTD) in small studies, but the sequence of WMH abnormalities relative to other biomarkers is unclear. METHODSUsing a large dataset (n=763 GENFI2 participants), we measured WMHs and examined them across genetic FTD variants and stages. Cortical and subcortical volumes were parcellated, and serum neurofilament light chain (NfL) levels were measured. Biomarker progression was assessed with discriminative event-based and regression modeling. RESULTSSymptomatic GRN carriers showed elevated WMHs, primarily in the frontal lobe, while no significant increase was observed in C9orf72 or MAPT carriers. WMH abnormalities preceded NfL elevation, ventricular enlargement, and cortical atrophy. Longitudinally, baseline WMHs predicted subcortical changes, while subcortical volumes did not predict WMH changes, suggesting WMHs may precede neurodegeneration. DISCUSSIONWMHs are elevated in a subset of GRN-related FTD. When present, they appear early and should be considered in disease progression models. HighlightsO_LIElevated WMH volumes in symptomatic GRN carriers, but not in other mutations. C_LIO_LIWMH accumulation is mostly observed in the frontal lobe. C_LIO_LIWMH abnormalities appear early in GRN-FTD, before NfL, atrophy, and ventriculomegaly. C_LIO_LILongitudinally, WMH volumes can predict subcortical changes, but not vice versa. C_LIO_LIWMHs are key early markers in GRN-FTD and should be included in progression models. C_LI RESEARCH IN CONTEXTO_ST_ABSSystematic reviewC_ST_ABSWe systematically reviewed the literature on white matter hyperintensities (WMHs) in frontotemporal dementia (FTD) using PubMed. While a few small studies reported increased WMHs in GRN mutation carriers, their sample sizes were limited, and they did not assess the timing of WMHs within disease progression or their temporal relationship to other biomarkers. InterpretationWe identified a sequence of key biomarkers in GRN-related FTD and demonstrated that WMHs are among the earliest biomarkers, preceding cortical and subcortical atrophy as well as blood biomarkers. This aligns with neuropathological evidence of early white matter involvement in FTLD-GRN. Additionally, using a larger dataset, we validated previous reports of elevated WMHs in GRN carriers, confirming their reliability. Future directionsFuture studies should integrate WMHs into FTD progression models to enhance early diagnosis. Understanding why only a subset of GRN carriers exhibit high WMH volumes remains a key research priority.

neuroscience↗

bioRxiv2024

Brain Age Prediction: Deep Models Need a Hand to Generalize

Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the Mean Absolute Error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contribute to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN-reg, based on the VGG-16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 44% (from 5.25 to 2.96 years) in the Alzheimers Disease Neuroimaging Initiative dataset and by 22% (from 4.35 to 3.40 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve a 29% reduction in scan-rescan error (from 0.86 to 0.61 years) while enhancing the models robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high-quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps towards the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models.

neuroscience↗

bioRxiv2022

QRATER: a collaborative and centralized imaging quality control web-based application.

Quality control (QC) is an important part of all scientific analysis, including neuroscience. With manual curation considered the gold standard, there remains a lack of available tools that make manual neuroimaging QC accessible, fast, and easy. In this article we present Qrater, a containerized web-based python application that enables viewing and rating of previously generated QC images. A group of raters with varying amounts of experience in QC evaluated Qrater in three different tasks: QC of MRI raw acquisition (10,196 images), QC of non-linear registration to a standard template (10,196 images) and QC of skull segmentation (6,968 images). We measured the proportion of failed images, timing and intra- and inter-rater agreement. Raters spent vastly different amounts of time on each image depending on their experience and the task at hand. QC of MRI raw acquisition was the slowest. While an expert rater needed approximately one minute, trained raters spent 2-6 minutes evaluating an image. The fastest was the curation of a skull segmentation image, where expert raters spent on average 3 seconds per image before assigning a rating. Rating agreement also varied depending on the experience of the raters and the task at hand: trained raters inter-rater agreement with the experts gold standard ranged from fair to substantial in raw acquisition (Cohens chance corrected kappa agreement scores up to 0.72) and from fair to excellent in linear registration (kappa scores up to 0.82), while the experts inter-rater agreement of the skull segmentation task was excellent (kappa = 0.83). These results demonstrate that Qrater is a useful asset for QC tasks that rely on manual curation of images.

bioinformatics↗