Volume of Gray Matter May Be Useful in Treatment P
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Posted On: 04/21/2022 5:05:48 PM
Volume of Gray Matter May Be Useful in Treatment Planning for Mental Health Conditions
New research has found that examining the brain structure of patients with recent onset of depression and psychosis may help identify those who are more likely to have poor outcomes. The researchers believe that physicians will be able to offer these patients more effective, targeted treatments by identifying them while in the early stages of their disorder.
For their study, the researchers utilized data from the Pronia study. The Pronia study is a cohort study that is looking into prognostic tools for psychoses.
The group included 300 patients with recent onset depression and recent onset psychosis in its research. The scientists evaluated patient brain scans using a machine learning algorithm and classified them into a pair of clusters. Every cluster had distinctive characteristics that were strongly linked to the likelihood of recovery for patients.
The researchers found that patients with lower levels of gray matter were linked to poorer outcomes. On the other hand, patients with higher volumes of gray matter had higher chances of recovering from their conditions. Gray matter is the darker tissue in the brain, which is involved in functions such as decision-making, emotions and memory as well as muscle control.
The researchers then predicted conditions of patients months after they had received a diagnosis, using a second algorithm. They discovered that the use of biologically based clusters led to a higher accuracy in forecasting outcomes when compared to using conventional diagnostic systems.
Their findings also show that patients with lower gray matter volumes had poorer concentration and higher levels of inflammation and other cognitive impairments previously linked to schizophrenia and depression.
Paris Alexandros Lalousis, the lead author of the study, stated that most mental health conditions were diagnosed based on a patient’s clinical observations, symptoms and history instead of biological data. Lalousis explained that patients could have different diagnoses while having similar underlying biological mechanisms in their disorders, thus by understanding these mechanisms better, clinicians could be better equipped to plan treatments.
Lalousis noted that longer illness duration increased the likelihood of a patient being classified in the cluster with lower gray matter volume, which emphasized that structural MRI scans may provide useful insight to help guide treatment decisions.
The researchers are now focused on validating clusters in this trial and collecting patient data in real time, all in preparation for a larger clinical trial. The study was carried out by researchers at the University of Birmingham. Its findings were published in “Biological Psychiatry.”
While some teams are focusing on improving mental health illness diagnosis, other entities, such as Cybin Inc. (NYSE American: CYBN) (NEO: CYBN), have taken the direction of finding novel therapies, which could soon revolutionize mental health care as we know it due to superior efficacy levels.
NOTE TO INVESTORS: The latest news and updates relating to Cybin Inc. (NEO: CYBN) (NYSE American: CYBN) are available in the company’s newsroom at https://ibn.fm/CYBN
Please see full terms of use and disclaimers on the BioMedWire website applicable to all content provided by BMW, wherever published or re-published: http://BMW.fm/Disclaimer
New research has found that examining the brain structure of patients with recent onset of depression and psychosis may help identify those who are more likely to have poor outcomes. The researchers believe that physicians will be able to offer these patients more effective, targeted treatments by identifying them while in the early stages of their disorder.
For their study, the researchers utilized data from the Pronia study. The Pronia study is a cohort study that is looking into prognostic tools for psychoses.
The group included 300 patients with recent onset depression and recent onset psychosis in its research. The scientists evaluated patient brain scans using a machine learning algorithm and classified them into a pair of clusters. Every cluster had distinctive characteristics that were strongly linked to the likelihood of recovery for patients.
The researchers found that patients with lower levels of gray matter were linked to poorer outcomes. On the other hand, patients with higher volumes of gray matter had higher chances of recovering from their conditions. Gray matter is the darker tissue in the brain, which is involved in functions such as decision-making, emotions and memory as well as muscle control.
The researchers then predicted conditions of patients months after they had received a diagnosis, using a second algorithm. They discovered that the use of biologically based clusters led to a higher accuracy in forecasting outcomes when compared to using conventional diagnostic systems.
Their findings also show that patients with lower gray matter volumes had poorer concentration and higher levels of inflammation and other cognitive impairments previously linked to schizophrenia and depression.
Paris Alexandros Lalousis, the lead author of the study, stated that most mental health conditions were diagnosed based on a patient’s clinical observations, symptoms and history instead of biological data. Lalousis explained that patients could have different diagnoses while having similar underlying biological mechanisms in their disorders, thus by understanding these mechanisms better, clinicians could be better equipped to plan treatments.
Lalousis noted that longer illness duration increased the likelihood of a patient being classified in the cluster with lower gray matter volume, which emphasized that structural MRI scans may provide useful insight to help guide treatment decisions.
The researchers are now focused on validating clusters in this trial and collecting patient data in real time, all in preparation for a larger clinical trial. The study was carried out by researchers at the University of Birmingham. Its findings were published in “Biological Psychiatry.”
While some teams are focusing on improving mental health illness diagnosis, other entities, such as Cybin Inc. (NYSE American: CYBN) (NEO: CYBN), have taken the direction of finding novel therapies, which could soon revolutionize mental health care as we know it due to superior efficacy levels.
NOTE TO INVESTORS: The latest news and updates relating to Cybin Inc. (NEO: CYBN) (NYSE American: CYBN) are available in the company’s newsroom at https://ibn.fm/CYBN
Please see full terms of use and disclaimers on the BioMedWire website applicable to all content provided by BMW, wherever published or re-published: http://BMW.fm/Disclaimer
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