Developing a disease staging biomarker based on micro density patterns in bvFTD patients
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 7:00 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
128
Submission Type:
Abstract Submission
Authors:
Behnaz Akbarian1, Kilian Hett2, Tony Phan2, Ryan Darby2
Institutions:
1Vanderbilt University, Nashville, TN, 2Vanderbilt university medical center, Nashville, TN
First Author:
Co-Author(s):
Introduction:
Frontotemporal dementia (FTD) is a neurodegenerative disorder affecting the frontal and temporal lobes (Grossman, 2023), with behavioral variant FTD (bvFTD) as the most common clinical presentation, characterized by changes in social behavior, personality, and executive function (Matarrubia, 2014). Staging dementia is critical for effective clinical management, as it helps tailor personalized care, and it provides a framework for documenting the impact of therapeutic interventions that may alter the course of the underlying disorder.
T1-weighted MRI is an important modality for diagnosis and clinical work-up, where visual inspection of cortical brain volume is part of the diagnostic criteria. Automated brain MRI segmentation methods are considered better than visual inspection alone, as they offer quantitative and repeatable measures of anatomical changes. However, volumetric analysis of structural MRI is limited to characterizing volume loss which is thought to occur later during the disease process. In contrast, texture analysis, which quantifies microstructural changes by examining relationships between signal intensities of neighboring voxels (Larroza, 2016), may detect physiological changes leading to neuronal loss.
We hypothesize that gray matter microstructural damage, quantified through texture analysis, can differentiate disease stages in bvFTD. Specifically, we focus on the sum average texture feature, which captures the relationship between radiolucent (dark, volume loss) and radiopaque (bright, dense) regions in MRI images. Neuron loss typically manifests as dark regions on T1-weighted MRI, with early changes often too subtle for visual detection. This study explores whether sum average can distinguish between mild and moderate dementia in bvFTD patients.
T1-weighted MRI is an important modality for diagnosis and clinical work-up, where visual inspection of cortical brain volume is part of the diagnostic criteria. Automated brain MRI segmentation methods are considered better than visual inspection alone, as they offer quantitative and repeatable measures of anatomical changes. However, volumetric analysis of structural MRI is limited to characterizing volume loss which is thought to occur later during the disease process. In contrast, texture analysis, which quantifies microstructural changes by examining relationships between signal intensities of neighboring voxels (Larroza, 2016), may detect physiological changes leading to neuronal loss.
We hypothesize that gray matter microstructural damage, quantified through texture analysis, can differentiate disease stages in bvFTD. Specifically, we focus on the sum average texture feature, which captures the relationship between radiolucent (dark, volume loss) and radiopaque (bright, dense) regions in MRI images. Neuron loss typically manifests as dark regions on T1-weighted MRI, with early changes often too subtle for visual detection. This study explores whether sum average can distinguish between mild and moderate dementia in bvFTD patients.
Methods:
Thirty-two bvFTD patients (3 females, age=62.58.7 years) and 33 age-matched controls (HC, 13 females, age=63.17.9 years) were included. Acquisition. T1-weighted MPRAGE MRI scans were acquired (TR=8.1ms, TE=3.7ms, flip angle=8°, FoV=256×256x150 mm, voxel size=1×1×1 mm). Clinical Dementia Rating (CDR) (Miyagawa, 2020) scale was used to classify patients into mild (CDR=0.5 or 1, n=21) and moderate (CDR=2, n=11) dementia groups. Processing. The Glasser atlas was used to segment T1-weighted images into 360 cortical regions of interest (ROI), (Huang, 2022). For each ROI, sum average was calculated using the gray-level co-occurrence method. Sum average quantifies the probability-weighted sum of all pixel intensity pair combinations, normalized by the total number of pairs (Fig. 1). Hypothesis testing. Sum average and volume were compared between mild and moderate dementia groups. ANCOVA was used to test group-level differences within each ROI, adjusting for age and sex. Brain volumes were additionally adjusted for intracranial volume. Reported p-values were corrected for family-wise error rate using the False Discovery Rate.
·Figure 1: Texture analysis pipeline.
Results:
Sum average progressively decreased across groups, from healthy controls to mild and moderate bvFTD patients (p<0.05, Fig. 2). Regions exhibiting abnormal texture feature in bvFTD included the frontal and temporal lobes, consistent with the expected pathology. Importantly, no significant volumetric differences were observed between mild and moderate dementia groups.
·Figure 2. Left: Sum average-based texture comparison between healthy controls (HC), bvFTD patients with mild and with moderate CDR scores. Right: Volume comparison across the same groups. * p < 0.05,
Conclusions:
This study indicates that texture analysis can distinguish between mild and moderate bvFTD patients, even when volumetric differences are not detectable. Key regions identified, such as the orbital frontal complex and anterior cingulate are linked to disease progression (Brambati, 2007, Van, 2022, Irish, 2017) and social behavior deficits (Hornberger, 2010, Séguin, 2004). Moreover, a higher sum average is associated with more dispersed density. With disease progression, this shift suggests the presence of more localized dark densities (Fig. 1). These findings imply that microstructural changes within specific regions may serve as sensitive markers of disease progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
MRI
Neurological
STRUCTURAL MRI
Other - Neurodegenerative, Dementia, Texture, Intensity, Volume
1|2Indicates the priority used for review
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Provide references using APA citation style.
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