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Improved Method to Predict Patterns of Dementia

Improved Method to Predict Patterns of DementiaEmerging studies suggest a model that may help explain the brain damage associated with dementia and Alzheimer’s. The new paradigm may also be useful for predicting cognitive decline.

One study investigated the structural integrity of brain circuits while the other study examined the functional connections of brain networks. Each study suggested a similar model to predict brain degeneration in various forms of dementia.

Experts say that this convergence of ideas into a unified theory is particularly significant because, until now, models for predicting regional neurodegeneration in humans have remained elusive.

Researchers have known that different dementias involve distinct parts of the brain. Prior research has led to the theory that neurodegenerative diseases target specific networks of neurons that are linked by connectivity rather than where they reside in the brain.

Further, this neurodegenerative process is thought to involve the accumulation of abnormal toxic proteins and possibly even the spread of these proteins between neurons, which my travel from neuron to neuron through their synaptic connections.

One study, led by Drs. Juan Zhou and William Seeley, from the University of California, San Francisco, addressed this theory.

“We were interested in whether knowing the healthy brain’s functional “wiring diagram” would help us predict specific patterns of neurodegeneration seen in patients,” explains Dr. Seeley.

“For each illness we studied, specific regions emerged as critical network ‘epicenters,’ and functional connectivity to these epicenters predicted each region’s vulnerability. The findings best fit a model wherein disease spreads from neuron to neuron along network connections that link brain structures.”

In the other study, led by Dr. Ashish Raj from Weill Medical College of Cornell University, researchers modeled this same kind of “transneuronal” disease transmission by mathematically analyzing structural connectivity networks obtained from healthy brain MRIs.

Their model predicted spatially distinct “eigenmodes,” tightly connected subnetworks in the brain, which mirrored classic patterns of damage seen in dementia.

“Our findings provide the first plausible explanation of why various dementias appear to selectively target distinct regions of the brain—as a simple mechanical consequence of transneuronal spread within the brain networks.

This also suggests that all dementias, previously considered to be pathologically distinct, might share a common progression mechanism,” concludes Dr. Raj.

“Importantly, this model of disease progression may be useful clinically for prediction of future cognitive decline in patients, based on their current MRI scans. Knowledge of what the future holds would allow patients to make informed choices regarding their lifestyle and therapeutic interventions.”

Source: Cell Press

Sad elderly man photo by shutterstock.

Improved Method to Predict Patterns of Dementia

Rick Nauert PhD

Rick Nauert, PhDDr. Rick Nauert has over 25 years experience in clinical, administrative and academic healthcare. He is currently an associate professor for Rocky Mountain University of Health Professionals doctoral program in health promotion and wellness. Dr. Nauert began his career as a clinical physical therapist and served as a regional manager for a publicly traded multidisciplinary rehabilitation agency for 12 years. He has masters degrees in health-fitness management and healthcare administration and a doctoral degree from The University of Texas at Austin focused on health care informatics, health administration, health education and health policy. His research efforts included the area of telehealth with a specialty in disease management.

APA Reference
Nauert PhD, R. (2018). Improved Method to Predict Patterns of Dementia. Psych Central. Retrieved on December 5, 2020, from
Scientifically Reviewed
Last updated: 8 Aug 2018 (Originally: 22 Mar 2012)
Last reviewed: By a member of our scientific advisory board on 8 Aug 2018
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