Vulnerable Brain Networks Predict Alzheimer’s Disease

The study covered in this summary was published on bioRxiv.org as a preprint and has not yet been peer reviewed.

Key Takeaway

• Identification of network-based biomarkers associated with Alzheimer’s disease (AD) can allow for early, effective interventions before major cognitive decline.

Why This Matters

• This study reports the identification of structural brain networks and associated risk factors for AD in preclinical stages, which may provide progress toward therapeutic or preventive interventions.

Study Design

• Three analysis methods were applied to a cohort of persons with normal cognition, mild cognitive impairment, and AD: sparse canonical correlation with bootstrap confidence interval estimation (SCCA), Tensor Network SCCA (TNSCCA), and predictive modeling.

• The investigators recruited 35 men and 37 women for the study (age range, 21–83 years; median age, 50.5; average age, 51.22 ± 15.3).

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• Cognitive testing was based on the Alzheimer’s Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS3).

• MRI was performed using a 3T GE Premier Performance scanner with Signa premier version 28; anatomic images were used for brain parcellation and connectome estimation.

• Canonical correlation analysis was used to investigate relationships among multiple sets of data with different types.

Key Results

• SCCA revealed relationships between brain subgraphs and AD risk; APOE genotype played an important role.

• Brain mapping identified vulnerable networks beyond the main nodes involved in auditory, visual, and olfactory memory.

• TNSCCA and sparse regression–based predictive models showed vulnerable networks associated with known risk factors for AD, including sex, age, genotype, and family risk.

• Brain connections predictive of APOE genotype included the middle and transverse temporal, paracentral and superior banks of temporal sulcus, amygdala, parahippocampal gyrus, and cerebellum.

Limitations

• The population sample was small.

• Network data may not accurately translate from one study to another.

• Different imaging methods are needed to discern the precise mechanism underlying observed changes and to refine future models.

Disclosures

• This study was funded by US government grants.

• The authors have disclosed no relevant financial relationships.

This is a summary of a preprint research study, “Vulnerable Brain Networks Associated With Risk for Alzheimer’s Disease,” written by Ali Mahzarnia from the Department of Radiology, Duke University Medical School, Durham, North Carolina, and colleagues, published on bioRxiv.org, and provided to you by Medscape. This study has not yet been peer reviewed. The full text of the study can be found bioRxiv.org.

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