Details

Autor(en) / Beteiligte

• Lteif, Diala
• Sreerama, Sandeep
• Bargal, Sarah A.
• Plummer, Bryan A.
• Au, Rhoda
• Kolachalama, Vijaya B.

Titel

Disease‐driven domain generalization for neuroimaging‐based assessment of Alzheimer's disease

Ist Teil von

• Human brain mapping, 2024-06, Vol.45 (8), p.e26707-n/a

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class‐wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state‐of‐the‐art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field. By utilizing model interpretability, our deep learning framework enhances assessment of cognitive impairment from MRI scans, paving the way for improved generalizability in machine learning models.