TY - JOUR
T1 - Aiding the diagnosis of dissociative identity disorder
T2 - pattern recognition study of brain biomarkers
AU - Reinders, Antje A. T. S
AU - Marquand, Andre F.
AU - Schlumpf, Yolanda R
AU - Chalavi, Sima
AU - Vissia, Eline M
AU - R.S. Nijenhuis, Ellert
AU - Dazzan, Paola
AU - Jäncke, Lutz
AU - Veltman, Dick J.
PY - 2019/9
Y1 - 2019/9
N2 - BackgroundA diagnosis of dissociative identity disorder (DID) is controversial and prone to under- and misdiagnosis. From the moment of seeking treatment for symptoms to the time of an accurate diagnosis of DID individuals received an average of four prior other diagnoses and spent 7 years, with reports of up to 12 years, in mental health services.AimTo investigate whether data-driven pattern recognition methodologies applied to structural brain images can provide biomarkers to aid DID diagnosis.MethodStructural brain images of 75 participants were included: 32 female individuals with DID and 43 matched healthy controls. Individuals with DID were recruited from psychiatry and psychotherapy out-patient clinics. Probabilistic pattern classifiers were trained to discriminate cohorts based on measures of brain morphology.ResultsThe pattern classifiers were able to accurately discriminate between individuals with DID and healthy controls with high sensitivity (72%) and specificity (74%) on the basis of brain structure. These findings provide evidence for a biological basis for distinguishing between DID-affected and healthy individuals.ConclusionsWe propose a pattern of neuroimaging biomarkers that could be used to inform the identification of individuals with DID from healthy controls at the individual level. This is important and clinically relevant because the DID diagnosis is controversial and individuals with DID are often misdiagnosed. Ultimately, the application of pattern recognition methodologies could prevent unnecessary suffering of individuals with DID because of an earlier accurate diagnosis, which will facilitate faster and targeted interventions.
AB - BackgroundA diagnosis of dissociative identity disorder (DID) is controversial and prone to under- and misdiagnosis. From the moment of seeking treatment for symptoms to the time of an accurate diagnosis of DID individuals received an average of four prior other diagnoses and spent 7 years, with reports of up to 12 years, in mental health services.AimTo investigate whether data-driven pattern recognition methodologies applied to structural brain images can provide biomarkers to aid DID diagnosis.MethodStructural brain images of 75 participants were included: 32 female individuals with DID and 43 matched healthy controls. Individuals with DID were recruited from psychiatry and psychotherapy out-patient clinics. Probabilistic pattern classifiers were trained to discriminate cohorts based on measures of brain morphology.ResultsThe pattern classifiers were able to accurately discriminate between individuals with DID and healthy controls with high sensitivity (72%) and specificity (74%) on the basis of brain structure. These findings provide evidence for a biological basis for distinguishing between DID-affected and healthy individuals.ConclusionsWe propose a pattern of neuroimaging biomarkers that could be used to inform the identification of individuals with DID from healthy controls at the individual level. This is important and clinically relevant because the DID diagnosis is controversial and individuals with DID are often misdiagnosed. Ultimately, the application of pattern recognition methodologies could prevent unnecessary suffering of individuals with DID because of an earlier accurate diagnosis, which will facilitate faster and targeted interventions.
KW - Magnetic resonance imaging (MRI)
KW - childhood trauma
KW - dissociation
KW - post-traumatic stress disorder (PTSD)
UR - http://www.scopus.com/inward/record.url?scp=85070498554&partnerID=8YFLogxK
U2 - 10.1192/bjp.2018.255
DO - 10.1192/bjp.2018.255
M3 - Article
SN - 0007-1250
VL - 215
SP - 536
EP - 544
JO - British Journal of Psychiatry
JF - British Journal of Psychiatry
IS - 3
ER -
