Objectives: To determine the frequency high-convexity tight sulci (HCTS) in a population based sample and whether the presence of HCTS and related features influenced participants’ cognitive status and classification within the new Alzheimer-biomarker framework. Methods: We analyzed 684 participants, ≥50, enrolled into the prospective population-based Mayo Clinic Study of Aging, who underwent structural MRI, amyloid PET imaging, and tau- PET imaging. A fully automated machine-learning algorithm that had been developed previously in-house was used to detect neuroimaging features of HCTS. Based on PET and MRI measures, participants were classified as having normal (A−) or abnormal (A+) amyloid, normal (T−) or abnormal (T+) tau, and normal (N−) or abnormal (N+) neurodegeneration. The neuropsychological battery assessed domain-specific and global cognitive scores. Gait speed also was assessed. Analyses were adjusted for age and sex. Results: 45/684 participants (6.6%) were classified with HCTS based on the automated algorithm. Patients with HCTS were older than patients without HCTS (mean [SD], 78.0 [8.3] vs 71.9 [10.8] years; P<.001). More were cognitively impaired after age and sex adjustment (27% vs 9%; P=.005). Amyloid PET status was similar with and without HCTS, but tau PET SUVR was lower for those with HCTS after age and sex adjustment (P<.001). Despite a lower tau SUVR, HCTS patients had lower Alzheimer disease (AD) signature cortical thickness. Using the AT(N) framework, HCTS was overrepresented in the T−(N)+ group, regardless of amyloid status. Conclusions: The HCTS pattern represents a definable subgroup of non-AD pathophysiology (i.e., T−[N]+) that is associated with cognitive impairment. HCTS may confound clinical and biomarker interpretation in AD clinical trials.