Intracranial Causes of Cognitive Impairment That Can Be Mistaken for Alzheimer's

Alzheimer’s disease is the most common form of dementia, accounting for up to 60-70% of dementia cases. As of 2021, more than six million Americans and one in nine people aged 65 and older are living with Alzheimer’s disease.

However, despite the vast prevalence of Alzheimer’s disease and continually growing comprehension of its pathology, the rate of misdiagnosis continues to remain high—the 2021 World Alzheimer’s Report estimates that up to 30% of people diagnosed with Alzheimer’s disease are misdiagnosed. There are many intracranial causes of cognitive impairment that can be mistaken for Alzheimer’s.

Below, we detail several examples of intracranial causes of cognitive impairment, unique defining pathologies of Alzheimer’s disease, and the need for more reliable, affordable, and accessible longitudinal cognitive assessment tools to improve diagnostic accuracy.

It’s Not Always Alzheimer’s: Intracranial Causes of Cognitive Impairment

When a patient presents symptoms of cognitive impairment, such as increased memory loss and confusion, difficulty learning new information, or challenges with problem-solving, it can be easy to jump right to Alzheimer’s disease. While Alzheimer’s disease is clinically characterized by a variety of progressive cognitive deficits, there are many intracranial causes of cognitive impairment. Several examples are detailed below.

• Intracranial atherosclerotic stenosis: A study of 1,701 participants revealed that Mild Cognitive Impairment was identified in 34% of asymptomatic intracranial atherosclerotic stenosis participants. The study concluded that asymptomatic intracranial atherosclerotic stenosis is independently associated with cognitive impairment in Caucasian individuals.

• Intracranial calcification in chronic hypoparathyroidism: Intracranial calcification is closely associated with chronic hypoparathyroidism. Neuropsychological testing in a study of 11 hypoparathyroidism patients revealed cognitive impairment in 65% of the subjects, while computed tomography showed intracranial calcification in six out of 10 patients. The study concluded that cognitive and neuropsychological deficits commonly occur in patients with chronic hypoparathyroidism and may be pathophysiologically related to the presence of intracranial calcification.

• Intracerebral hemorrhage: In a study comparing 20 patients with cerebral amyloid angiopathy-related and deep intracerebral hemorrhage, 20 patients with Alzheimer’s-related Mild Cognitive Impairment, and 17 healthy controls, researchers found that mild and major vascular cognitive disorders were observed in 87.5% and 2.5% of all intracerebral hemorrhage patients, respectively. The study concluded that vascular cognitive disorders are frequent after intracerebral hemorrhage while also noting that patterns of cognitive impairment present in both deep and cerebral amyloid angiopathy patients did not vary but were distinct from the effects of Alzheimer’s.

• Intracranial arachnoid cysts: In a review of studies that reported dycognition or psychiatric symptoms in intracranial arachnoid cyst patients, the survey concluded that the presence of arachnoid cysts may irreversibly affect cognition. A range of cognitive impairments were noted, including verbal perception and memory, complex verbal tasks, visuospatial functions, and visual attention.

Alzheimer’s Identification and Diagnosis: Unique Defining Pathologies of Alzheimer’s

Although there are similar cognitive impairments in Alzheimer’s disease and other neurological conditions, Alzheimer’s has unique defining pathologies. Most commonly, Alzheimer’s disease is characterized by beta-amyloid plaques and neurofibrillary tangles resulting from abnormal tau hyperphosphorylation. These distinct pathologies are crucial in making an Alzheimer’s disease diagnosis and are commonly identified through imaging and diagnostic procedures, such as magnetic resonance imaging, positron emission tomography, and cerebrospinal fluid analysis.

However, identifying cognitive impairments that may be indicative of Alzheimer’s disease typically begins with pencil and paper cognitive assessments, such as the Mini-Mental State Exam (MMSE) and Montreal Cognitive Assessment (MoCA). These assessments are purely cognitive batteries, meaning they are not necessarily able to distinguish between cognitive impairment due to a unique defining pathology of Alzheimer’s versus intracranial causes of cognitive impairment.

Improving Diagnostic Accuracy of Alzheimer’s Disease

As Alzheimer’s is a progressive disease, obtaining a timely and accurate diagnosis is critical for effective intervention and treatment. Given that simple cognitive assessments are not suited for Alzheimer’s diagnosis and diagnostic tools are expensive, invasive, and rather inaccessible, there is an urgent need for new tools to aid in Alzheimer’s diagnosis.

To improve diagnostic accuracy, there is a need for:

• More longitudinal patient data.
• Measurement tools with a higher degree of sensitivity.
• Strong analytical tools, such as artificial intelligence, that can better identify the unique similarities in behavior between patients with Alzheimer’s pathologies and patients without.

Altoida: Pioneering Fully Digital Precision Diagnosis

At Altoida, we are building the world’s-first precision neurology platform and app-based medical device—backed by 11 years of clinical validation—to accelerate and improve drug development, neurological disease research, and patient care.

By completing a series of augmented reality and motor activities designed to simulate complex Activities of Daily Living (ADLs) on a smartphone or tablet, Altoida’s device extracts and provides new and robust measurements of neurocognitive function across 13 neurocognitive domains. Our device measures and analyzes nearly 800 multimodal cognitive and functional digital biomarkers. Through the collection of highly granular data from integrated smartphone or tablet sensors, Altoida’s device produces comprehensive neurocognitive domain scores.

Our web-based platform allows researchers to manage and monitor populations. Subject data from Altoida’s tests and other health data, such as prescriptions, traditional biomarker data, and existing conditions, will be available in the platform and can be observed longitudinally to reveal trends and patterns.

This method, along with our innovative artificial intelligence, will pioneer fully digital predictive neurological disease diagnosis. After our Breakthrough Device designation by the FDA, Altoida’s device will provide patients with a predictive score that will enable a highly accurate prediction of whether a patient aged 55 and older will or will not convert from Mild Cognitive Impairment to Alzheimer’s disease. 

When this score is taken in conjunction with our neurocognitive domain scores, researchers will be able to make specific, personalized conclusions about how neurological diseases are uniquely affecting patients. This, in turn, will enable a personalized precision approach to treatment and care plan development for neurological disease patients.