Dr. Daniel Kaufer is a neurologist specializing in memory and cognitive disorders. In a clinical setting he provides patients with comprehensive neurocognitive diagnostic evaluations of memory disorders, and also performs clinical research related to the subject matter. He received his medical degree from University of Wisconsin School of Medicine and Public Health and has been in practice for more than 20 years.
Areas of Expertise (9)
Mild Cognitive Impairment
Primary Progressive Aphasia
Lewy Body Disease
University of Wisconsin-Madison: M.D.
Montefiore University Hospital, Pittsburgh, PA: Internship
University of Pittsburgh: Residency, Neurology
University of California, Los Angeles: Fellowship, Behavioral/Geriatric Neurology
- American Board of Psychiatry and Neurology; 1995; re-certification, 2005
- United Council for Neurological Specialities (Behavioral Neurology and Neuropsychiatry), 2008
Morrison, R. L., Pei, H., Novak, G., Kaufer, D. I., Welsh-Bohmer, K. A., Ruhmel, S. & Narayan, V. A.
Introduction: Performance of “Revere” a novel iPad-administered word-list recall (WLR) test, in quantifying deficits in verbal episodic memory, was evaluated versus examiner-administered Rey Auditory Verbal Learning Test (RAVLT) in patients with mild cognitive impairment and cognitively normal participants. Methods: Elderly patients with clinically diagnosed mild cognitive impairment (Montreal Cognitive Assessment score 24–27) and cognitively normal (Montreal Cognitive Assessment score ≥28) were administered RAVLT or Revere in a randomized crossover design. Results: A total of 153/161 participants (Revere/RAVLT n = 75; RAVLT/Revere n = 78) were randomized; 148 (97%) completed study; 121 patients (mean [standard deviation] age: 70.4 [7.84] years) were included for analysis. Word-list recall scores (8 trials) were comparable between Revere and RAVLT (Pearson's correlation coefficients: 0.12–0.70; least square mean difference [Revere-RAVLT]: −0.84 [90% CI, −1.15; −0.54]). Model factor estimates indicated trial (P
Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Daniel Ian Kaufer
Recently hyper-graph learning gains increasing attention in medical imaging area since the hyper-graph, a generalization of a graph, opts to characterize the complex subject-wise relationship behind multi-modal neuroimaging data. However, current hyper-graph methods mainly have two limitations: (1) The data representation encoded in the hyper-graph is learned only from the observed imaging features for each modality separately. Therefore, the learned subject-wise relationships are neither consistent across modalities nor fully consensus with the clinical labels or clinical scores. (2) The learning procedure of data representation is completely independent to the subsequent classification step. Since the data representation optimized in the feature domain is not exactly aligned with the clinical labels, such independent step-by-step workflow might result in sub-optimal classification. To address these limitations, we propose a novel dynamic hyper-graph inference framework, working in a semi-supervised manner, which iteratively estimates and adjusts the subject-wise relationship from multi-modal neuroimaging data until the learned data representation (encoded in the hyper-graph) achieves largest consensus with the observed clinical labels and scores. It is worth noting our inference framework is also flexible to integrate classification (identifying individuals with neuro-disease) and regression (predicting the clinical scores). We have demonstrated the performance of our proposed dynamic hyper-graph inference framework in identifying MCI (Mild Cognition Impairment) subjects and the fine-grained recognition of different progression stage of MCI, where we achieve more accurate diagnosis result than conventional counterpart methods.
Geller EJ1, Dumond JB, Bowling JM, Khandelwal CM, Wu JM, Busby-Whitehead J, Kaufer DI.
This study aimed to investigate the effect of trospium chloride on cognitive function in postmenopausal women treated for overactive bladder (OAB).
Randomized double-blind placebo-controlled trial conducted from April 2013 to April 2015. Women aged 50 years or older seeking treatment for OAB were randomized to either trospium chloride XR 60 mg daily or placebo. Baseline cognitive function was assessed via Hopkins Verbal Learning Test-Revised (HVLT-R), Mini Mental Status Exam, Mini Mental Status X, Digit Span, Trails A, Trails B, and Epworth Sleepiness Scale. Cognitive function was reassessed at week 1 and week 4. A priori power analysis determined that 21 subjects were needed per group.
Although 59 women were enrolled and randomized (28 trospium and 31 placebo), 45 completed assessment (21 trospium and 24 placebo). Mean age was 68 years, 78% were white, and 44% had previously taken OAB medication. For the primary outcome, there was no difference in HVLT-R total score between trospium and placebo groups at week 4 (P = 0.29). There were also no differences based on the other cognitive tests. There was a correlation between age and the following week-4 tests: HVLT-R total score (r = -0.3, P = 0.02), HVLT-R total recall subscale (r = -0.4, P = 0.007), Trails A (r = 0.4, P = 0.002), and Trails B (r = 0.4, P = 0.004). A linear regression model found that HVLT-R total score decreased by 0.372 points for each increased year of age.
In women aged 50 years and older, there were no changes in cognitive function between those taking trospium and placebo. Cognitive function was correlated with age.