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Proteomic studies have generated robust assessments of protein abundance changes in Alzheimer’s disease (AD); however, identifying how the protein abundance changes affect specific biological processes remains a challenge. To address these hurdles, we used a multi-network computational analysis approach that integrated dendritic spine morphometry data with mass spectrometry-based proteomics from the same individuals. The samples exhibited a range of AD neuropathology and were categorized into three groups: controls, asymptomatic AD, and AD cases. Multiplex tandem mass tag mass spectrometry proteomic data (N = 8,212 proteins) was generated on Brodmann area 46 (BA46) dorsolateral prefrontal cortex (DLPFC) human samples (N = 41, 23 males and 18 females), from which dendritic spine morphometry analysis existed. To integrate the multi-scale data types, two computational network analysis methods were performed, including WeiGhted co-expression network analysis (WGCNA) and SpeakEasy2 (SE2). Both WGCNA and SE2 revealed that the mitochondria protein modules were decreased in AsymAD and AD cases compared to controls, whereas the DNA repair modules were increased in AsymAD and AD compared to controls. Synaptic protein modules that correlated to multiple spine morphology traits were identified in both WGCNA and SE2. Pearson correlation analyses identified over a dozen individual proteins linked to multiple dendritic spine density and morphology traits. Collectively, these findings demonstrate how integration of spine morphometry data with proteomics can contextualize proteins for functional validation and identify synaptic alterations in AD progression. Significance Statement Cognitive decline in Alzheimer's disease associates more strongly with synapse and dendritic spine loss than amyloid-beta or tau pathology. However, one in three individuals harbor Alzheimer's disease neuropathology at death but were cognitively indistinguishable from baseline in life. Preservation of spines and synapses is hypothesized to prevent cognitive decline in these individuals. Identifying the molecular drivers of synaptic changes in Alzheimer's disease could yield deeper understanding of disease progression. Here, we utilized two computational network approaches that integrated multi-scale data, including proteomics and dendritic spine morphometry from the same humans, to identify proteins relevant to synapses in Alzheimer's disease. Hundreds of proteins related to mitochondria, DNA repair, and synaptic signaling were associated with alterations in synapse structure and function in Alzheimer's disease.