Approximately 1 in 5 adults in the United States experience mental illness in a given year.
Severe mental illnesses cause the brain to have trouble dealing with cognitively effortful states, like focusing attention over long periods of time, discriminating between two things that are difficult to tell apart, and responding quickly to information that is coming in fast.
A new study, published in the Journal of Neural Engineering, could improve patients’ abilities to manage symptoms of mental illness.
Previous research demonstrated that applying electrical stimulation at just the right time helps the brain of a patient with a severe mental illness work through difficult cognitive tasks.
However, it was done in a laboratory setting, free from the complexities of real-world activities of daily living.
Senior author Alik Widge, MD, Ph.D, Assistant Professor of Psychiatry at the University of Minnesota Medical School, and investigators at Massachusetts General Hospital (MGH), consisting of researchers from Brown University and MGH, including co-senior author David Borton, Ph.D., Assistant Professor of Engineering at Brown University, were the first to analyze patients’ brain activity to detect precisely when a patient is focused and their attention is fully devoted, compared to when he or she is ‘at rest’.
They studied patients who were undergoing surgery for severe epilepsy, who already had measurement electrodes in the relevant brain areas.
The study, which was part of DARPA’s SUBNETS program, found that specific signatures and algorithms can be used to tell when someone is focused and really trying to do a task that is hard for them, indicating that they could benefit from an electrical stimulation to get an extra push.
The study also demonstrates that there is no single region of the brain that can tell when someone is in this focused, effortful state.
In order to detect when the patient started to focus on a cognitive task, the researchers had to analyze the information at the network level.
It was essential to look at how the activity of one region coordinated with the activity of another.
“Using the same neural signals that could drive adaptive deep brain stimulation, we have shown that it is possible to detect mental states that might be amenable to closed-loop control,” said lead author Nicole Provenza, MS, Ph.D. candidate, Brown University.
“While further research is necessary to generalize our findings to real-world applications, we hope that this work will ultimately contribute to the development of more effective brain stimulation therapies for mental illness.”
“We want to take a patient-centered approach to treating mental illness,” explained Widge. “The job of a stimulator is not to take away the symptoms; its job is to help the patient manage his or her symptoms. It gives the power back to the individual and just gives them a little extra help when they need it.”
There is still more work to be done, but Widge is excited to take the next step and eventually make these ideas into real products that will help people.
Mental Illness
Mental illnesses are common in the United States. Nearly one in five U.S. adults live with a mental illness (46.6 million in 2017).
Mental illnesses include many different conditions that vary in degree of severity, ranging from mild to moderate to severe. Two broad categories can be used to describe these conditions: Any Mental Illness (AMI) and Serious Mental Illness (SMI).
AMI encompasses all recognized mental illnesses. SMI is a smaller and more severe subset of AMI. Additional information on mental illnesses can be found on the NIMH Health Topics Pages.
Definitions
The data presented here are from the 2017 National Survey on Drug Use and Health (NSDUH) by the Substance Abuse and Mental Health Services Administration (SAMHSA).
For inclusion in NSDUH prevalence estimates, mental illnesses include those that are diagnosable currently or within the past year; of sufficient duration to meet diagnostic criteria specified within the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); and, exclude developmental and substance use disorders.
Any Mental Illness
- Any mental illness (AMI) is defined as a mental, behavioral, or emotional disorder. AMI can vary in impact, ranging from no impairment to mild, moderate, and even severe impairment (e.g., individuals with serious mental illness as defined below).
Serious Mental Illness
- Serious mental illness (SMI) is defined as a mental, behavioral, or emotional disorder resulting in serious functional impairment, which substantially interferes with or limits one or more major life activities. The burden of mental illnesses is particularly concentrated among those who experience disability due to SMI.
Prevalence of Any Mental Illness (AMI)
- Figure 1 shows the past year prevalence of AMI among U.S. adults.
- In 2017, there were an estimated 46.6 million adults aged 18 or older in the United States with AMI. This number represented 18.9% of all U.S. adults.
- The prevalence of AMI was higher among women (22.3%) than men (15.1%).
- Young adults aged 18-25 years had the highest prevalence of AMI (25.8%) compared to adults aged 26-49 years (22.2%) and aged 50 and older (13.8%).
- The prevalence of AMI was highest among the adults reporting two or more races (28.6%), followed by White adults (20.4%). The prevalence of AMI was lowest among Asian adults (14.5%).
Mental Health Services — AMI
- Figure 2 presents data on mental health services received within the past year by U.S. adults aged 18 or older with any mental illness (AMI). NSDUH defines mental health services as having received inpatient treatment/counseling or outpatient treatment/counseling, or having used prescription medication for problems with emotions, nerves, or mental health.
- In 2017, among the 46.6 million adults with AMI, 19.8 million (42.6%) received mental health services in the past year.
- More women with AMI (47.6%) received mental health services than men with AMI (34.8%).
- The percentage of young adults aged 18-25 years with AMI who received mental health services (38.4%) was lower than adults with AMI aged 26-49 years (43.3%) and aged 50 and older (44.2%).
Prevalence of Serious Mental Illness (SMI)
- Figure 3 shows the past year prevalence of SMI among U.S. adults.
- In 2017, there were an estimated 11.2 million adults aged 18 or older in the United States with SMI. This number represented 4.5% of all U.S. adults.
- The prevalence of SMI was higher among women (5.7%) than men (3.3%).
- Young adults aged 18-25 years had the highest prevalence of SMI (7.5%) compared to adults aged 26-49 years (5.6%) and aged 50 and older (2.7%).
- The prevalence of SMI was highest among the adults reporting two or more races (8.1%), followed by White adults (5.2%). The prevalence of SMI was lowest among Asian adults (2.4%).
Mental Health Services — SMI
- Figure 4 presents data on mental health services received within the past year by U.S. adults 18 or older with serious mental illness (SMI). The NSDUH defines mental health services as having received inpatient treatment/counseling or outpatient treatment/counseling or having used prescription medication for problems with emotions, nerves, or mental health.
- In 2017, among the 11.2 million adults with SMI, 7.5 million (66.7%) received mental health treatment in the past year.
- More women with SMI (71.5%) received mental health treatment than men with AMI (57.7%).
- The percentage of young adults aged 18-25 years with AMI who received mental health treatment (57.4%) was lower than adults with AMI aged 26-49 years (66.2%) and aged 50 and older (75.6%).
Prevalence of Any Mental Disorder Among Adolescents
- Based on diagnostic interview data from National Comorbidity Survey Adolescent Supplement (NCS-A), Figure 5 shows lifetime prevalence of any mental disorder among U.S. adolescents aged 13-18.1
- An estimated 49.5% of adolescents had any mental disorder.
- Of adolescents with any mental disorder, an estimated 22.2% had severe impairment. DSM-IV based criteria were used to determine impairment level.
More information: Nicole R Provenza et al. Decoding task engagement from distributed network electrophysiology in humans, Journal of Neural Engineering (2019). DOI: 10.1088/1741-2552/ab2c58
Journal information: Journal of Neural Engineering
Provided by University of Minnesota
