hippocampal morphometry
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2021 ◽  
Author(s):  
Elly Htite ◽  
Niharika Gajawelli ◽  
Christine Saint-Martin ◽  
Miao Wei ◽  
Sarah Palmis ◽  
...  

2021 ◽  
Vol 15 ◽  
Author(s):  
Jianfeng Wu ◽  
Qunxi Dong ◽  
Jie Zhang ◽  
Yi Su ◽  
Teresa Wu ◽  
...  

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.


2021 ◽  
Author(s):  
Jianfeng Wu ◽  
Qunxi Dong ◽  
Jie Zhang ◽  
Yi Su ◽  
Teresa Wu ◽  
...  

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two ADNI cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI) and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89x speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.


NeuroImage ◽  
2021 ◽  
Vol 224 ◽  
pp. 117373
Author(s):  
Roy A.M. Haast ◽  
Jonathan C. Lau ◽  
Dimo Ivanov ◽  
Ravi S. Menon ◽  
Kâmil Uludağ ◽  
...  

Author(s):  
Xujun Duan ◽  
Changchun He ◽  
Jianjun Ou ◽  
Runshi Wang ◽  
Jinming Xiao ◽  
...  

Abstract Accumulating neuroimaging evidence has shown remarkable volume reductions in the hippocampi of patients with schizophrenia. However, the relationship among hippocampal morphometry, clinical symptoms, and cognitive impairments in schizophrenia is still unclear. In this study, high-resolution structural magnetic resonance imaging data were acquired in 36 patients with adolescent-onset schizophrenia (AOS, age range: 13–18 years) and 30 age-, gender-, and education-matched typically developing controls (TDCs). Hippocampal volume was assessed automatically through volumetric segmentation and measurement. After adjusting for total intracranial volume, we found reduced hippocampal volume in individuals with AOS compared with TDCs, and the hippocampal volume was positively correlated with verbal memory and negatively correlated with negative symptoms in AOS. In addition, mediation analysis revealed the indirect effect of hippocampal volume on negative symptoms via verbal memory impairment. When the negative symptoms were represented by 2 dimensions of deficits in emotional expression (EXP) and deficits in motivation and pleasure (MAP), the indirect effect was significant for EXP but not for MAP. Our findings provide further evidence of hippocampal volume reduction in AOS and highlight verbal memory impairment as a mediator to influence the relationship between hippocampal morphometry and negative symptoms, especially the EXP dimension of negative symptoms, in individuals with AOS.


2019 ◽  
Vol 15 ◽  
pp. P1097-P1097
Author(s):  
Qunxi Dong ◽  
Wen Zhang ◽  
Jianfeng Wu ◽  
Bolun Li ◽  
Emily H. Schron ◽  
...  

2019 ◽  
Vol 22 ◽  
pp. 101744 ◽  
Author(s):  
Qunxi Dong ◽  
Wen Zhang ◽  
Jianfeng Wu ◽  
Bolun Li ◽  
Emily H. Schron ◽  
...  

2017 ◽  
Vol 15 ◽  
pp. 56-61 ◽  
Author(s):  
Anna E. Blanken ◽  
Sona Hurtz ◽  
Chris Zarow ◽  
Kristina Biado ◽  
Hedieh Honarpisheh ◽  
...  

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