Teens with psychosis spectrum disorders have significantly reductions in a number of cortical brain regions

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Psychotic spectrum (PS) disorders are characterized by abnormalities in beliefs, perceptions and behavior, but how these disorders manifest themselves in earlier development stages is largely unknown.

A new study in the Journal of the American Academy of Child and Adolescent Psychiatry (JAACAP), published by Elsevier, reports differences in brain structure among youth with PS disorders relative to typically developing youth.

The study found surface area reductions in a number of cortical brain regions in comparison to typically developing youth; youth with bipolar spectrum disorders; and youth with both psychosis and bipolar spectrum disorders.

The brain regions involved are important for everyday functioning and cognitive abilities.

“Psychosis is viewed as a psychiatric disorder that arises from neurodevelopmental alterations. However, until recently, the focus of neuroimaging studies has been on adults who have already developed a psychotic disorder,” said lead author Maria Jalbrzikowski, PhD, Assistant Professor in the Department of Psychiatry at the University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. “With access to large, publicly available datasets such as the Philadelphia Neurodevelopmental Cohort, we can really start to investigate how alterations in neurodevelopment contribute to the development of psychotic symptoms.”

The findings are based on the structural neuroimaging analyses conducted on participants from the Philadelphia Neurodevelopmental Cohort (PNC), a population-based sample of 10,000 youth in the Philadelphia metro area, between the ages of 9 and 22 years old.

Compared with all other groups, PS youth exhibited significantly decreased surface area in the orbitofrontal, cingulate, precentral, and postcentral regions.

Structural magnetic resonance neuroimaging (MRI) data were collected on a subset of the cohort (N = 989), followed by measuring the cortical thickness, surface areas of the brain.

Subcortical volumes were then calculated; study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376); psychosis spectrum (n = 113); bipolar spectrum (n = 117); and PS + bipolar spectrum (n = 109).

Compared with all other groups, PS youth exhibited significantly decreased surface area in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups.

The brain alterations were restricted to youth with only PS symptoms, not youth who exhibited both psychosis spectrum and bipolar spectrum symptoms.

“This suggests that those who have both types of symptoms (psychosis and bipolar spectrum) may have different underlying neural mechanisms that contribute to symptoms, in comparison to those with psychotic spectrum symptoms only,” said Dr. Jalbrzikowski.


Elucidation of biomarkers predictive of schizophrenia and other psychotic disorders is a high priority for the field. Neuroanatomical deviance, as assessed using quantitative metrics derived from magnetic resonance imaging (MRI) scans, has been suggested as potentially relevant to this goal.

Several studies have observed a steeper rate of cortical thinning, particularly in heteromodal association regions, among clinical high-risk individuals (CHR) who convert to psychosis compared with those who do not convert and healthy controls (Borgwardt et al., 2008Cannon et al., 2015Chung et al., 2018; Pantelis, 2003; Sun et al., 2009Takahashi et al., 2009).

However, prior studies have obtained conflicting results as to whether individuals at CHR with poorer clinical and functional outcomes manifest measurable neuroanatomical deviance at the time of baseline evaluation (i.e. when help-seeking individuals were initially evaluated and met criteria for a psychosis-risk syndrome), as would be necessary if these measures are to have utility as predictive biomarkers (Borgwardt et al., 2007Fusar-Poli et al., 2011Mechelli et al., 2011Pantelis et al., 2003Takahashi et al., 2009Velakoulis et al., 2006).

The wide age range of CHR samples (typically, 12–35 years) represents a major challenge for elucidation of neuroanatomical markers predictive of psychosis, given that deviance on such measures is only ascertainable with respect to age-appropriate norms, and there are marked developmental changes in brain structure across this age span (Brown et al., 2012Gogtay et al., 2004Tamnes et al., 2017).

Further, given the heterogeneity in the timing of potentially relevant risk exposures and age at onset of prodromal symptoms and subsequent psychosis, the degree to which CHR cases manifest neuroanatomical deviance may well depend on their age at evaluation.

Other factors that likely contribute to the inconsistent findings of prior MRI studies of CHR samples include the use of relatively small sample sizes and uneven application of statistical controls for multiple testing.

To overcome these limitations, we recently performed a machine-learning analysis of “brain age” using MRI measures in the North American Prodrome Longitudinal Study (NAPLS 2) sample (Chung et al., 2018).

A neuroanatomical-based age prediction model was developed using a supervised machine learning technique with T1 MRI scans from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and then applied to NAPLS2 scans for external validation and clinical application (Brown et al., 2012Chung et al., 2018Jernigan et al., 2016). The PING-derived model accurately predicted NAPLS healthy control subjects’ chronological ages, providing evidence of independent, external validation. CHR individuals ascertained at younger ages (i.e., 12 to 17 years) were observed to show deviance from the normal neuromaturational pattern (i.e., a gap between “brain age” and chronological age), which in turn was associated with greater risk of conversion to psychosis and a pattern of stably poor functional outcome.

In contrast, individuals who were 18 years of age or older showed age-normative neuroanatomical profiles at ascertainment (i.e., no gap between “brain age” and chronological age). A reevaluation of our prior findings showing a steeper rate of cortical thinning over time among CHR cases who converted to psychosis revealed that this effect was unique to the cases who were 18 years or older at ascertainment and did not apply to the younger cases (Cannon et al., 2015Chung et al., 2018).

This pattern is consistent with the view that neuroanatomical deviance manifesting in early adolescence marks vulnerability to a form of psychosis with an insidious onset and debilitating course of illness, while accelerated cortical thinning marks vulnerability to a more acute onset form of illness that does not manifest until late adolescence and early adulthood.

In the present study, we address two additional issues concerning the “early onset” form of psychosis. First, we aim to determine whether neuroanatomical deviance in early adolescence is associated with poorer premorbid functioning during childhood, as would be expected if this pattern is associated with a more insidious onset.

Second, we aim to clarify whether the neuroanatomical deviance associated with onset of psychosis in early adolescence is distributed in brain regions previously linked to schizophrenia, including superior, medial, and dorsolateral prefrontal cortex, anterior cingulate, superior temporal cortex, and parahippocampal gyrus (Borgwardt et al., 2007Fusar-Poli et al., 2011Mechelli et al., 2011Pantelis et al., 2003).

Although the “brain age” approach can quantitatively assess deviations in brain maturation at the single subject level, a feature that makes it particularly useful for prediction purposes, the anatomical features selected by the algorithm are not necessarily indicative of risk for a particular disorder (Chung et al., 2018).

By design, the fitted model parameters are data-driven, characterizing the regularized maturation pattern among brain structures that tracks with variations in chronological age among typically developing individuals. In other words, this “brain age” composite metric is not optimized for detecting schizophrenia risk per se and would presumably be sensitive to any condition in which cases deviate from the normal pattern of age-related neuroanatomical change during childhood/adolescence. Machine-learning algorithms trained to predict psychosis as an outcome could potentially yield a psychosis-specific pattern of neuroanatomical deviance (Koutsouleris et al., 2009).

However, in the NAPLS2 study, such models were found to perform poorly compared with the “brain age” classifier, probably because the structural brain changes related to psychosis onset occur against the backdrop of the gradual gray matter decline that is part of normative adolescent brain development and because heterogeneity in the pathways leading to full psychosis works against the accuracy of machine learning algorithms trained on an outcome that treats all converters as exemplars of a unitary outcome class (Chung et al., 2018).

Here we report the results of group contrasts of the baseline MRI data according to 2-year clinical outcome in the same NAPLS2 sample that was used in the brain age prediction study. This analysis provides added value to the “brain age” prediction results in that it provides a more direct test of neuroanatomical changes associated with risk for psychosis and whether these effects are moderated by age at ascertainment and associated with poor premorbid adjustment.

Based on the foregoing, we hypothesized that CHR individuals who are 17 years of age or younger would show smaller cortical volumes in dorsal lateral prefrontal cortex, medial prefrontal cortex, superior temporal gyrus, and parahippocampal areas. We also predicted that the younger CHR adolescents with persisting or worsening prodromal symptoms would show a greater degree of neuroanatomical deviance at baseline than those who are on the course of remission. We further hypothesized that the preexisting neuroanatomical deficits among younger CHR adolescents are associated with poorer premorbid functioning in childhood.


Source:
Elsevier
Media Contacts:
Mary Billingsley – Elsevier
Image Source:
The image is in the public domain.

Original Research: Closed access
“Structural Brain Alterations in Youth With Psychosis and Bipolar Spectrum Symptoms”. Maria Jalbrzikowski, PhD, David Freedman, PhD, Catherine E. Hegarty, PhD, Eva Mennigen, PhD, Katherine H. Karlsgodt, PhD, Loes M. Olde Loohuis, PhD, Roel A. Ophoff, PhD, Raquel E. Gur, MD, PhD, and Carrie E. Bearden, PhD.
Journal of the American Academy of Child and Adolescent Psychiatry doi:10.1016/j.jaac.2018.11.012.

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