Embic Partners with Alzheimer’s Disease Neuroimaging Initiative (ADNI) to Make Digital Cognitive Biomarkers Available to Global Alzheimer’s Researchers

Read press release here.


NEWPORT BEACH, Calif., July 27, 2022 /PRNewswire/ — In a global effort to better understand and effectively treat Alzheimer’s disease, Embic and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have partnered to provide Embic’s digital cognitive biomarkers to the global research community. Qualified researchers can request access to the full ADNI dataset, including Embic’s seven digital biomarkers, each of which quantifies a particular cognitive process of encoding or retrieval.

Embic’s unique digital cognitive biomarkers are generated from a mathematical model of cognition that draws on the company’s proprietary dataset of two-million, normally aging individuals, collected over a 20-year period. The model, which was validated in an NIH-funded study (Grant No.: R44AG065126), quantifies the unobservable processes of information encoding and retrieval that allow individuals to perform a vast range of cognitive functions including memory, orientation, and verbal fluency. Isolating and quantifying these fundamental cognitive processes provides the research community with unprecedented insight into cognitive function and enables precise evaluation of overall brain health.  

Junko Hara, Ph.D., Embic’s chief scientific officer, commented, “Information encoding and retrieval are the primary cognitive functions that underlie cognitive ability. Digital biomarkers that quantify these processes are essential for detecting and understanding subtle but distinctive changes in brain health, including the early identification of emerging Alzheimer’s disease. Making these innovative measures available to the global research community is a step toward accelerating clinical trial enrollment, developing new therapeutics, and delivering timely care.”

By making these biomarkers available for researchers through ADNI, Embic will play a meaningful role in the worldwide effort to optimize cognitive health for an aging population.

About ADNI: ADNI is a global research study that actively supports the investigation and development of treatments that slow or stop the progression of Alzheimer’s disease (AD). In this multisite longitudinal study, researchers at 63 sites in the US and Canada track the progression of AD in the human brain with clinical, imaging, genetic and biospecimen biomarkers through the process of normal aging, early mild cognitive impairment (EMCI), and late mild cognitive impairment (LMCI) to dementia or AD. The overall goal of ADNI is to validate biomarkers for use in Alzheimer’s disease clinical treatment trials.

About Embic Corporation: Embic Corporation (www.embic.us) is a data analytics company that develops digital biomarkers for characterizing human cognition and brain health. The company’s intellectual property includes multiple patents in the cognitive health field, a registry of individuals monitoring their brain health, and a proprietary dataset of two million cognitive assessments that facilitates ongoing R&D efforts.

SOURCE Embic

New Publication: Optimizing Cognitive Assessment Outcome Measures for Alzheimer’s Disease

Optimizing Cognitive Assessment Outcome Measures for Alzheimer’s Disease by Matching Wordlist Memory Test Features to Scoring Methodology

Bock JR; Russell J; Hara J; Fortier D
Embic Corporation, Newport Beach, CA, USA

Front. Digit. Health, 03 November 2021. Read full article here.

Abstract
Cognitive assessment with wordlist memory tests is a cost-effective and non-invasive method of identifying cognitive changes due to Alzheimer’s disease and measuring clinical outcomes. However, with a rising need for more precise and granular measures of cognitive changes, especially in earlier or preclinical stages of Alzheimer’s disease, traditional scoring methods have failed to provide adequate accuracy and information. Well-validated and widely adopted wordlist memory tests vary in many ways, including list length, number of learning trials, order of word presentation across trials, and inclusion of semantic categories, and these differences meaningfully impact cognition. While many simple scoring methods fail to account for the information that these features provide, extensive effort has been made to develop scoring methodologies, including the use of latent models that enable capture of this information for preclinical differentiation and prediction of cognitive changes. In this perspective article, we discuss prominent wordlist memory tests in use, their features, how different scoring methods fail or successfully capture the information these features provide, and recommendations for emerging cognitive models that optimally account for wordlist memory test features. Matching the use of such scoring methods to wordlist memory tests with appropriate features is key to obtaining precise measurement of subtle cognitive changes.

Embic Shares New Study Outcome at the 14th Annual CTAD Conference

Embic’s newest finding was presented at the 14th annual Clinical Trials on Alzheimer’s Disease (CTAD) conference. The study further validates our mathematical model’s ability to detect pre-clinical AD with item response data from a standard wordlist memory test, and the additional signal detection theory parameters to further characterize cognitive processes. Read full presentation here.

Detection of Pre-clinical Alzheimer’s Disease with Simultaneous Modeling of Underlying Cognitive Processes in Recall and Recognition Tests
Bock JR [1]; Lee MD [2]; Shankle WRS [1-3]; Hara J [1,3]; Fortier D [1]; Mangrola T [1]
[1] Embic Corporation, Newport Beach, CA, USA
[2] Dept. of Cognitive Sciences, University of California at Irvine, Irvine, CA, USA
[3] Pickup Family Neuroscience Institute, Hoag Memorial Hospital, Newport Beach, CA, USA

Abstract
Background: Wordlist memory (WLM) tests are commonly used to detect and monitor cognitive impairment due to Alzheimer’s disease (AD). While traditional scoring methods and analyses of WLM tests (e.g., summary scores) are effective at identifying dementia, they are insufficient for detecting earlier stages of progressive decline, such as pre-clinical AD. This is partly due to the fact that summary scores of WLM test tasks (e.g., immediate and delayed free recall and delayed recognition tasks) do not contain sufficient information to make these more subtle distinctions in cognition. Our previous work demonstrated that using item response data from immediate and delayed free recall tasks, along with a hierarchical Bayesian cognitive processing (HBCP) model, enables greater information extraction from WLM tests, sufficient to characterize differences among subjects in varying stages of severity across the progression of AD. This was achieved by quantifying unobservable (latent) cognitive processes that underlie learning and recall, including encoding, storage, and retrieval of WLM test items. In further work, the HBCP model was applied to immediate and delayed free recall task data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects who were cognitively normal at baseline, a subgroup of which would remain normal, and another which would progress to impairment during follow-up. Using only baseline assessment data, the model successfully characterized and demonstrated statistically significant differences between these two subgroups.

Objective: To expand our HBCP model to incorporate delayed recognition task data in addition to immediate and delayed free recall data, and to identify meaningful differences or similarity in individual cognitive processes between healthy, normal subjects and pre-clinical AD subjects.

Methods: Nine hundred fifteen (915) cognitive assessments, performed on 254 subjects from a community memory clinic between 2002 and 2019, were included in this study. All subjects were cognitively normal by clinical diagnosis, and were given the MCI Screen (MCIS), a battery of cognitive tasks, including multi-trial free recall of a wordlist (with three immediate and one delayed free recall tasks) and a delayed recognition task of the same wordlist (with the addition of foil words). Subjects were classified into two groups: a decliner group (subject n = 92; assessment n = 234), if the subject declined to amnestic mild cognitive impairment or dementia within 2 years; and a non-decliner group (subject n = 162, assessment n = 681), if the subject remained cognitively normal or only subjectively cognitively impaired for at least 2 years. The HBCP model was expanded to include signal detection theory (SDT) parameters, discriminability and criterion, for measurement of recognition task data. This was done in such a way that the existing cognitive processing parameters benefit from both the free recall and recognition task information. We examined cognitive processing parameter posterior samples to characterize patterns in cognitive performance, and we performed Bayes factor analyses of parameter mean differences between decliner and non-decliner groups.

Results: decliner and non-decliner groups. Subjects in the decliner group demonstrated significantly lower encoding parameters for WLM task items in both early- and late-list positions, with unique patterns across specific encoding parameters. However, moderate evidence for statistical similarity between decliner and non-decliner groups was found for the SDT parameters of criterion and discriminability.

Discussion: ognitive processes. Lower encoding processes in the decliner group corroborates our previous findings, and similar levels in subject criterion and discriminability between groups aligns with existing literature pertaining to recognition task performance resilience to cognitive decline. Identifying specific cognitive processes which change before observable cognitive decline occurs, and differentiating them from processes that do not change until later in the progression timeline, is of great value in clinical and research trial settings, both for early detection of AD and ongoing monitoring of cognition during treatment.

Embic Corporation Quantifies Amyloid Load with Digital Cognitive Biomarkers

Grant Funded Approach Could Accelerate Enrollment into Alzheimer’s Clinical Trials and Expedite Approval of New Treatments.

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NEWPORT BEACH, Calif., Oct. 26, 2021 /PRNewswire/ — Embic Corporation, a leading developer of digital cognitive biomarkers, has secured grant funding from the National Institute on Aging (NIA) (Grant#: R43AG074769) to further validate their approach for identifying individuals who are accumulating abnormal levels of amyloid protein. Identifying such individuals through this inexpensive and non-invasive approach will significantly accelerate research into new treatments for Alzheimer’s disease while enabling timely intervention and better treatment outcomes for patients in the disease’s earliest stages.

In a preliminary study, Embic’s digital cognitive biomarkers, which are generated from standardized neuropsychological assessment data, identified subjects who were both “cognitively normal” and “amyloid positive” with 88% – 90% accuracy. This NIA grant will underwrite an additional study to validate the preliminary results using publicly available data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The successful replication and validation of the earlier results will confirm further utility of Embic’s digital cognitive biomarkers, and establish a pragmatic and cost-effective approach for identifying persons with accumulating Alzheimer’s disease pathology.

Commenting on the grant, Embic CEO, Dennis Fortier, said, “A key challenge in developing new Alzheimer’s therapies is identifying subjects with both minimal cognitive deficits and levels of amyloid consistent with early-stage disease. The current approach of performing expensive and invasive PET scans on potential trial candidates, has a scan failure rate as high as 80% and adds undue time and expense to the research process. A brief test that pragmatically identifies candidates who are likely to have an amyloid-positive PET scan could identify a much larger pool of potential research subjects while significantly reducing the screen-fail rate in the enrollment process. This could ultimately bring new treatments to market sooner.”

About Embic Corporation: Embic Corporation (www.embic.us) is a data analytics company that develops digital biomarkers for characterizing human cognition and brain health. The company’s intellectual property includes multiple patents in the cognitive health field, a registry of individuals monitoring their brain health, and a proprietary dataset of two million cognitive assessments that facilitates ongoing R&D efforts. 

SOURCE Embic Corporation