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1. Regional Brain Volumes Associated With Depression in the National Alzheimer’s Coordinating Center Uniform Data Set

   https://doi.org/10.1093/geroni/igaa057.1196  

   Burke, Shanna; Li, Tan; Grudzien, Adrienne; Barnes, Christopher; Hanson, Kevin; DeKosky, Steven (December 2020, Innovation in Aging)

   null (Ed.)

   Abstract Depression has been associated with greater risk of Alzheimer’s disease (AD), and existing research has identified structural differences in brain regions in depressed subjects compared to healthy samples, but results have been heterogeneous. We sought to determine the effect of depression on regional brain volumes by cognitive and APOE e4 status. Secondary analysis of the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set was conducted using complete MRI data from 1,371 participants (mean age: 70.5; SD: 11.7). Multiple linear regression was used to estimate the adjusted effect of depression (via the Neuropsychiatric Inventory Questionnaire) on regional brain volumes through measurement of 30 structural MRIs. Depression in the prior two years was associated with lower total brain, cerebrum,, and gray matter volumes and greater total brain white matter hyperintensities (p<.05). Greater volumes were also observed in all ventricular volume measures. Lower mean volumes were observed in six additional frontal lobe and parietal lobe cortical regions. Alternately, depression antecedent to the past 2 years correlated only with occipital lobe gray matter volumes (right, left, total). Our findings suggest that depression in the prior two years is associated with atrophy across multiple brain regions and related ventricular enlargement, even after controlling for intracranial volume and demographic covariates. The duration of depression influences results, however, as depression prior to 2 years before assessment was correlated with significantly fewer and different regional brain volume changes. 

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2. The Impact of Sleep Disturbance on Regional Brain Volumes

   https://doi.org/10.1093/geroni/igaa057.1521  

   Burke, Shanna; Li, Tan; Grudzien, Adrienne; Barnes, Christopher; Hanson, Kevin; DeKosky, Steven (December 2020, Innovation in Aging)

   null (Ed.)

   Abstract Sleep disruption has been associated with increased beta-amyloid deposition and greater risk for later development of Alzheimer’s disease. Studies indicate that sleep disturbance correlates with regional brain volumes, but data are limited. We sought to determine the effect of sleep disturbance on regional brain volumes by cognitive and apolipoprotein e (APOE) e4 status. We conducted a secondary analysis of the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set using complete structural imaging data from 1,371 participants (mean age: 70.5; SD: 11.7). Multiple linear regression was used to estimate the adjusted effect of sleep disturbance (via Neuropsychiatric Inventory Questionnaire) on regional brain volumes through measurement of 30 structural MRI biomarkers. Sleep disruption was associated with greater volumes in the right and left lateral ventricles and greater volume of total white matter hyperintensities (p<.05). Lower mean volumes in total brain, total gray matter, and total cerebrum grey matter volumes, and in 12 hippocampal, frontal, parietal, and temporal lobe volumes were observed among participants who reported sleep disturbance. Males, Hispanic participants, and those with less education were more likely to report sleep disruption. Cognitive status moderated the relationship between sleep disturbance and lateral ventricular volumes, while APOE e4 moderated the effect between sleep disturbance and parietal lobe volumes. These findings suggest that disrupted sleep is associated with atrophy across multiple brain regions and ventricular hydrocephalus ex vacuo, after controlling for intracranial volume and demographic covariates. The influence of cognition and APOE e4 status indicates that this relationship is affected by co-occurring physiological processes. 

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3. Brain Structures Associated with Depression in Older Adults in the National Alzheimer’s Coordinating Center Uniform Data Set

   https://doi.org/10.1177/08919887251361101  

   Burke, Shanna L; Grudzien, Adrienne; Li, Tan; Goulett, Natalie; Barnes, Christopher P; Hanson, Kevin; DeKosky, Steven T (August 2025, Journal of Geriatric Psychiatry and Neurology)

   PurposeThis study examined relations between four late-life depression subgroups (recent, >2 years ago, chronic, no depression) and regional brain volumes using structural MRI data from the National Alzheimer’s Coordinating Center (n=1,551). Data AnalysisMultiple linear regressions evaluated the effects of depression on 30 MRI biomarkers, while moderation analyses assessed how APOE ε4 and depression shape the connections between cognitive status and brain structure volumes. ResultsAfter adjusting for covariates and applying Hochberg’s method, recent depression (< 2 years) was associated with reduced total cerebrum cranial volume and left frontal lobe cortical gray matter volume. Chronic depression correlated with larger right lateral ventricle volume. ConclusionThese findings suggest that recent depression is linked to brain atrophy across specific regions and ventricular enlargement. Future research should investigate age-related impacts on these associations and whether restoration of brain volume occurs after depressive symptoms subside. 

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4. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

   https://doi.org/10.1073/pnas.2214634120  

   Yin, Chenzhong; Imms, Phoebe; Cheng, Mingxi; Amgalan, Anar; Chowdhury, Nahian F.; Massett, Roy J.; Chaudhari, Nikhil N.; Chen, Xinghe; Thompson, Paul M.; Bogdan, Paul; et al (January 2023, Proceedings of the National Academy of Sciences)

   The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

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5. Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

   https://doi.org/10.1186/s12859-022-04946-x  

   Feng, Yixue; Kim, Mansu; Yao, Xiaohui; Liu, Kefei; Long, Qi; Shen, Li; for the Alzheimer’s Disease Neuroimaging Initiative (September 2022, BMC Bioinformatics)

   Abstract BackgroundIn Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. ResultsDGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. ConclusionOverall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. 

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