Analysis of brain age gap across subject cohorts and prediction model architectures

Dular, Lara (avtor), Špiclin, Žiga (avtor)

Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)—the difference between predicted brain age and chronological age—is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson’s disease, mild cognitive impairment, and Alzheimer’s disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer’s dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson’s disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (−0.34 to −0.2), mild cognitive impairment (−0.37 to −0.26), and Alzheimer’s dementia (−0.66 to −0.47), compared to healthy controls (−0.18 to −0.1). Conclusions: Consistent with previous research, Alzheimer’s dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum.

• Jezik: Angleški jezik
• Ključne besede: magnetnoresonančno slikanje, napovedovanje možganske starosti, globoki regresijski modeli, analiza starostnega primanjkljaja, primerjalna študija, kvantitativno vrednotenje
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: 17 str.
• Številčenje: Vol. 12, iss. 9, art. 2139
• PID: 20.500.12556/RUL-162565
• UDK: 004.93:616.8
• ISSN pri članku: 2227-9059
• DOI: 10.3390/biomedicines12092139
• COBISS.SI-ID: 208778243
• Datum objave v RUL: 25.09.2024

Gradivo je del revije

• Naslov: Biomedicines
• Skrajšan naslov: Biomedicines
• Založnik: MDPI AG
• ISSN: 2227-9059
• COBISS.SI-ID: 523006745

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: magnetic resonance imaging, brain age prediction, deep model regression, brain age gap analysis, comparative study, quantitative evaluation

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0232
• Naslov: Analiza biomedicinskih slik in signalov

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: J2-3059
• Naslov: Sprotno prilagajanje načrta protonske in radioterapije