Gavin T Kress 1, Emily S Popa 2, Paul M Thompson 3, Susan Y Bookheimer 4, Sophia I Thomopoulos 3, Christopher R K Ching 3, Hong Zheng 3, Daniel A Hirsh 5, David A Merrill 6, Stella E Panos 2, Cyrus A Raji 7, Prabha Siddarth 8, Jennifer E Bramen 9 Affiliations Expand Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an “AD-NeuroScore,” that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, with a mean age of

Cyrus A Raji1, *, Somayeh Meysami2, 3, Sam Hashemi4, 5, Saurabh Garg4, 5, Nasrin Akbari4, 5, Ahmed Gouda4, 5, Yosef Gavriel Chodakiewitz4, Thanh Duc Nguyen4, 5, Kellyann Niotis6, 7, David A Merrill2, 8, 9, Rajpaul Attariwala4, 5, 10 Abstract Abdominal fat is increasingly linked to brain health. A total of 10,001 healthy participants were scanned on 1.5T MRI with a short whole-body MR imaging protocol. Deep learning with FastSurfer segmented 96 brain regions. Separate models segmented visceral and subcutaneous abdominal fat. Regression analyses of abdominal fat types and normalized brain volumes were evaluated, controlling for age and sex. Logistic regression models determined the risk of brain total gray and white matter volume loss from the highest quartile of visceral fat and lowest quartile of these brain volumes. This cohort had an average age of 52.9 ± 13.1 years with 52.8% men and 47.2% women. Segmented visceral abdominal fat predicted lower volumes in multiple regions including: total gray matter volume (r = -.44, p<.001), total white matter volume (r =-.41, p<.001), hippocampus (r = -.39, p< .001), frontal cortex (r = -.42, p<.001), temporal lobes (r = -.44, p<.001), parietal lobes (r = -.39, p<.001), occipital lobes (r

Cyrus A Raji1, *, Somayeh Meysami2, 3, Sam Hashemi4, 5, Saurabh Garg4, 5, Nasrin Akbari4, 5, Ahmed Gouda4, 5, Yosef Gavriel Chodakiewitz4, Thanh Duc Nguyen4, 5, Kellyann Niotis6, 7, David A Merrill2, 8, 9, Rajpaul Attariwala4, 5, 10 Abstract Abdominal fat is increasingly linked to brain health. A total of 10,001 healthy participants were scanned on 1.5T MRI with a short whole-body MR imaging protocol. Deep learning with FastSurfer segmented 96 brain regions. Separate models segmented visceral and subcutaneous abdominal fat. Regression analyses of abdominal fat types and normalized brain volumes were evaluated, controlling for age and sex. Logistic regression models determined the risk of brain total gray and white matter volume loss from the highest quartile of visceral fat and lowest quartile of these brain volumes. This cohort had an average age of 52.9 ± 13.1 years with 52.8% men and 47.2% women. Segmented visceral abdominal fat predicted lower volumes in multiple regions including: total gray matter volume (r = -.44, p<.001), total white matter volume (r =-.41, p<.001), hippocampus (r = -.39, p< .001), frontal cortex (r = -.42, p<.001), temporal lobes (r = -.44, p<.001), parietal lobes (r = -.39, p<.001), occipital lobes (r

Gavin T Kress 1, Emily S Popa 2, Paul M Thompson 3, Susan Y Bookheimer 4, Sophia I Thomopoulos 3, Christopher R K Ching 3, Hong Zheng 3, Daniel A Hirsh 5, David A Merrill 6, Stella E Panos 2, Cyrus A Raji 7, Prabha Siddarth 8, Jennifer E Bramen 9 Affiliations Expand Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an “AD-NeuroScore,” that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, with a mean age of

Gavin T Kress 1, Emily S Popa 2, Paul M Thompson 3, Susan Y Bookheimer 4, Sophia I Thomopoulos 3, Christopher R K Ching 3, Hong Zheng 3, Daniel A Hirsh 5, David A Merrill 6, Stella E Panos 2, Cyrus A Raji 7, Prabha Siddarth 8, Jennifer E Bramen 9 Affiliations Expand Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an “AD-NeuroScore,” that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, with a mean age of

Cyrus A Raji1, *, Somayeh Meysami2, 3, Sam Hashemi4, 5, Saurabh Garg4, 5, Nasrin Akbari4, 5, Ahmed Gouda4, 5, Yosef Gavriel Chodakiewitz4, Thanh Duc Nguyen4, 5, Kellyann Niotis6, 7, David A Merrill2, 8, 9, Rajpaul Attariwala4, 5, 10 Abstract Abdominal fat is increasingly linked to brain health. A total of 10,001 healthy participants were scanned on 1.5T MRI with a short whole-body MR imaging protocol. Deep learning with FastSurfer segmented 96 brain regions. Separate models segmented visceral and subcutaneous abdominal fat. Regression analyses of abdominal fat types and normalized brain volumes were evaluated, controlling for age and sex. Logistic regression models determined the risk of brain total gray and white matter volume loss from the highest quartile of visceral fat and lowest quartile of these brain volumes. This cohort had an average age of 52.9 ± 13.1 years with 52.8% men and 47.2% women. Segmented visceral abdominal fat predicted lower volumes in multiple regions including: total gray matter volume (r = -.44, p<.001), total white matter volume (r =-.41, p<.001), hippocampus (r = -.39, p< .001), frontal cortex (r = -.42, p<.001), temporal lobes (r = -.44, p<.001), parietal lobes (r = -.39, p<.001), occipital lobes (r