Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes.

BACKGROUND: The current definitions of psychotic illness lack biological validity, motivating alternative biomarker-driven disease entities. Building on experimental constructs-Biotypes-that were previously developed from cognitive and neurophysiologic measures, we contrast brain anatomy characteristics across Biotypes alongside conventional diagnoses, examining gray matter density (GMD) as an independent validator for the Biotypes. METHODS: Whole brain GMD measures were examined in probands, their relatives, and healthy subjects organized by Biotype and then by DSM-IV-TR diagnosis (n = 1409) using voxel-based morphometry with subsequent subject-level regional characterization and distribution analyses. RESULTS: Probands grouped by Biotype versus healthy controls showed a stepwise pattern of GMD reductions as follows: Biotype1, extensive and diffusely distributed GMD loss, with the largest effects in frontal, anterior/middle cingulate cortex, and temporal regions; Biotype2, intermediate and more localized reductions, with the largest effects in insula and frontotemporal regions; and Biotype3, small reductions localized to anterior limbic regions. Relatives showed regionally distinct GMD reductions versus healthy controls, with primarily anterior (frontotemporal) effects in Biotype1; posterior (temporo-parieto-cerebellar) in Biotype2; and normal GMD in Biotype3. Schizophrenia and schizoaffective probands versus healthy controls showed overlapping GMD reductions, with the largest effects in frontotemporal and parietal regions; psychotic bipolar probands had small reductions, primarily in frontal regions. GMD changes in relatives followed regional patterns observed in probands, albeit less extensive. Biotypes showed stronger between-group separation based on GMD than the conventional diagnoses and were the strongest predictor of GMD change. CONCLUSIONS: GMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.

Hippocampal volume is reduced in schizophrenia and schizoaffective disorder but not in psychotic bipolar I disorder demonstrated by both manual tracing and automated parcellation (FreeSurfer).

This study examined hippocampal volume as a putative biomarker for psychotic illness in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) psychosis sample, contrasting manual tracing and semiautomated (FreeSurfer) region-of-interest outcomes. The study sample (n = 596) included probands with schizophrenia (SZ, n = 71), schizoaffective disorder (SAD, n = 70), and psychotic bipolar I disorder (BDP, n = 86); their first-degree relatives (SZ-Rel, n = 74; SAD-Rel, n = 62; BDP-Rel, n = 88); and healthy controls (HC, n = 145). Hippocampal volumes were derived from 3Tesla T1-weighted MPRAGE images using manual tracing/3DSlicer3.6.3 and semiautomated parcellation/FreeSurfer5.1,64bit. Volumetric outcomes from both methodologies were contrasted in HC and probands and relatives across the 3 diagnoses, using mixed-effect regression models (SAS9.3 Proc MIXED); Pearson correlations between manual tracing and FreeSurfer outcomes were computed. SZ (P = .0007-.02) and SAD (P = .003-.14) had lower hippocampal volumes compared with HC, whereas BDP showed normal volumes bilaterally (P = .18-.55). All relative groups had hippocampal volumes not different from controls (P = .12-.97) and higher than those observed in probands (P = .003-.09), except for FreeSurfer measures in bipolar probands vs relatives (P = .64-.99). Outcomes from manual tracing and FreeSurfer showed direct, moderate to strong, correlations (r = .51-.73, P < .05). These findings from a large psychosis sample support decreased hippocampal volume as a putative biomarker for schizophrenia and schizoaffective disorder, but not for psychotic bipolar I disorder, and may reflect a cumulative effect of divergent primary disease processes and/or lifetime medication use. Manual tracing and semiautomated parcellation regional volumetric approaches may provide useful outcomes for defining measurable biomarkers underlying severe mental illness.

Clinical phenotypes of psychosis in the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP).

OBJECTIVE: Developing categorical diagnoses that have biological meaning within the clinical phenotype of psychosis (schizophrenia, schizoaffective disorder, and bipolar I disorder with psychosis) is as important for developing targeted treatments as for nosological goals. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) was formed to examine a broad array of intermediate phenotypes across psychotic disorders and to test the hypothesis that intermediate phenotype characteristics are homogeneous within phenomenologically derived DSM-IV diagnoses. METHOD: The consortium recruited 933 stable probands with schizophrenia, schizoaffective disorder, or psychotic bipolar I disorder, 1,055 of their first-degree relatives, and 459 healthy comparison subjects for clinical characterization and dense phenotyping. Clinical, psychosocial, and family characteristics were contrasted. RESULTS: All proband groups showed lower psychosocial functioning than the relatives or comparison group. On average, schizophrenia probands showed more symptoms and lower psychosocial functioning than probands with psychotic bipolar disorder, but there was considerable overlap in clinical manifestations. The characteristics of schizoaffective disorder were more often similar to schizophrenia than to psychotic bipolar disorder. The rates of lifetime suicide attempts were high across all proband groups, with the highest reported frequencies in the schizoaffective and bipolar groups. Proband family lineages included both families with "pure" psychosis diagnoses and families with mixed schizophrenia-bipolar diagnoses. CONCLUSIONS: Symptoms, psychosocial functioning, and familial lineage overlap across the three DSM-IV psychosis diagnoses used in B-SNIP. The comingling of psychosis diagnoses within families suggests overlapping genetic determinants across psychoses. These data provide scant evidence for distinct phenotypic clustering around traditional phenomenological diagnoses.

Brain gray matter phenotypes across the psychosis dimension.

This study sought to examine whole brain and regional gray matter (GM) phenotypes across the schizophrenia (SZ)-bipolar disorder psychosis dimension using voxel-based morphometry (VBM 8.0 with DARTEL segmentation/normalization) and semi-automated regional parcellation, FreeSurfer (FS 4.3.1/64 bit). 3T T1 MPRAGE images were acquired from 19 volunteers with schizophrenia (SZ), 16 with schizoaffective disorder (SAD), 17 with psychotic bipolar I disorder (BD-P) and 10 healthy controls (HC). Contrasted with HC, SZ showed extensive cortical GM reductions, most pronounced in fronto-temporal regions; SAD had GM reductions overlapping with SZ, albeit less extensive; and BD-P demonstrated no GM differences from HC. Within the psychosis dimension, BD-P showed larger volumes in fronto-temporal and other cortical/subcortical regions compared with SZ, whereas SAD showed intermediate GM volumes. The two volumetric methodologies, VBM and FS, revealed highly overlapping results for cortical GM, but partially divergent results for subcortical volumes (basal ganglia, amygdala). Overall, these findings suggest that individuals across the psychosis dimension show both overlapping and unique GM phenotypes: decreased GM, predominantly in fronto-temporal regions, is characteristic of SZ but not of psychotic BD-P, whereas SAD display GM deficits overlapping with SZ, albeit less extensive.

Psychometric properties of the Quick Inventory of Depressive Symptomatology in adolescents.

OBJECTIVE: The clinician-rated (QIDS-C16) and self-report (QIDS-SR16) versions of the 16-item Quick Inventory of Depressive Symptomatology have been extensively examined in adult populations. This study evaluated both versions of the QIDS and the 17-item Children's Depressive Rating Scale - Revised (CDRS-R) in an adolescent outpatient sample. METHOD: Both the QIDS-C16 and QIDS-SR16 were completed for the adolescents. Three different methods were used to complete the QIDS-C16: (a) adolescents' responses to clinician interviews; (b) parents' responses to clinician interview; and (c) a composite score using the most pathological response from the two interviews. Both classical and item response theory methods were used. Factor analyses evaluated the dimensionality of each scale. RESULTS: The sample included 140 adolescent outpatients. All versions of the QIDS, save the parent interview, and the CDRS-R were very reliable (α ≥ 0.8). All four versions of the QIDS are reasonably effective and unidimensional. The CDRS-R was clearly at least two-dimensional. The CDRS-R was the most discriminating among low and extremely high levels of depression. The QIDS-SR16 was the most discriminating at moderate levels of depression. There was no relation between the QIDS scores and concurrent Axis III comorbidities. CONCLUSION: The QIDS-C16 and the QIDS-SR16 are suitable for use in adolescents.

A Comparison of the QIDS-C16, QIDS-SR16, and the MADRS in an Adult Outpatient Clinical Sample.

BACKGROUND: This study compared the 16-item Clinician and Self-Report versions of the Quick Inventory of Depressive Symptomatology (QIDS-C16 and QIDS-SR16) and the 10-item Montgomery-Asberg Depression Rating Scale (MADRS) in adult outpatients. The comparison was based on psychometric features and their performance in identifying those in a major depressive episode as defined by the Mini-International Neuropsychiatric Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. METHODS: Of 278 consecutive outpatients, 181 were depressed. Classical test theory, factor analysis, and item response theory were used to evaluate the psychometric features and receiver operating characteristic (ROC) analyses. RESULTS: All three measures were unidimensional. All had acceptable reliability (coefficient a=.87 for MADRS10, .82 for QIDS-C16, and .80 for QIDS-SR16). Test information function was higher for the MADRS (ie, it was most sensitive to individual differences in levels of depression). The MADRS and QIDS-C16 slightly but consistently outperformed the QIDS-SR16 in differentiating between depressed versus nondepressed patients. CONCLUSION: All three measures have satisfactory psychometric properties and are valid screening tools for a major depressive episode.

Brief psychiatric rating scale expanded version: How do new items affect factor structure?

Our goal was to suggest a factor structure for the Brief Psychiatric Rating Scale Expanded Version (BPRS-E) based upon a large and diverse sample and to determine which of the new items improved the factors derived from the 18-item version of the scale that have been used in clinical research for decades. We investigated the consistency of our proposed model over time and across demographic groups. As part of the Texas Medication Algorithm Project, the BPRS-E was administered to a total of 1440 psychiatric outpatients in three different diagnostic groups on multiple occasions. The sample was randomly split so that exploratory factor analysis could be done with the first half, and the model could be confirmed on the second half. A four-factor structure including factors assessing depression/anxiety, psychosis, negative symptoms, and activation was found. For each factor, we specify items in the expanded version that added to the breadth of the commonly used clinical factors while improving or maintaining goodness of fit and reliability. The final model proposed was consistent over time and across diagnosis, phase of illness, age, gender, ethnicity, and level of education. The BPRS-E has a stable four-factor structure, making it useful as a clinical outcome measure.

Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project.

CONTEXT: The Texas Medication Algorithm Project is an evaluation of an algorithm-based disease management program for the treatment of the self-declared persistently and seriously mentally ill in the public mental health sector. OBJECTIVE: To present clinical outcomes for patients with major depressive disorder (MDD) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU). DESIGN: Effectiveness, intent-to-treat, prospective trial comparing patient outcomes in clinics offering ALGO with matched clinics offering TAU. SETTING: Four ALGO clinics, 6 TAU clinics, and 4 clinics that offer TAU to patients with MDD but provide ALGO for schizophrenia or bipolar disorder. Patients Male and female outpatients with a clinical diagnosis of MDD (psychotic or nonpsychotic) were divided into ALGO and TAU groups. The ALGO group included patients who required an antidepressant medication change or were starting antidepressant therapy. The TAU group initially met the same criteria, but because medication changes were made less frequently in the TAU group, patients were also recruited if their Brief Psychiatric Rating Scale total score was higher than the median for that clinic's routine quarterly evaluation of each patient. MAIN OUTCOME MEASURES: Primary outcomes included (1) symptoms measured by the 30-item Inventory of Depressive Symptomatology-Clinician-Rated scale (IDS-C(30)) and (2) function measured by the Mental Health Summary score of the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) obtained every 3 months. A secondary outcome was the 30-item Inventory of Depressive Symptomatology-Self-Report scale (IDS-SR(30)). RESULTS: All patients improved during the study (P<.001), but ALGO patients had significantly greater symptom reduction on both the IDS-C(30) and IDS-SR(30) compared with TAU. ALGO was also associated with significantly greater improvement in the SF-12 mental health score (P =.046) than TAU. CONCLUSION: The ALGO intervention package during 1 year was superior to TAU for patients with MDD based on clinician-rated and self-reported symptoms and overall mental functioning.

Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. METHOD: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. RESULTS: ALGO and TAU patients showed significant initial decreases in symptoms (p =.03 and p <.001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. CONCLUSION: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale and study design.

BACKGROUND: Medication treatment algorithms may improve clinical outcomes, uniformity of treatment, quality of care, and efficiency. However, such benefits have never been evaluated for patients with severe, persistent mental illnesses. This study compared clinical and economic outcomes of an algorithm-driven disease management program (ALGO) with treatment-as-usual (TAU) for adults with DSM-IV schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) treated in public mental health outpatient clinics in Texas. DISCUSSION: The disorder-specific intervention ALGO included a consensually derived and feasibility-tested medication algorithm, a patient/family educational program, ongoing physician training and consultation, a uniform medical documentation system with routine assessment of symptoms and side effects at each clinic visit to guide ALGO implementation, and prompting by on-site clinical coordinators. A total of 19 clinics from 7 local authorities were matched by authority and urban status, such that 4 clinics each offered ALGO for only 1 disorder (SCZ, BD, or MDD). The remaining 7 TAU clinics offered no ALGO and thus served as controls (TAUnonALGO). To determine if ALGO for one disorder impacted care for another disorder within the same clinic ("culture effect"), additional TAU subjects were selected from 4 of the ALGO clinics offering ALGO for another disorder (TAUinALGO). Patient entry occurred over 13 months, beginning March 1998 and concluding with the final active patient visit in April 2000. Research outcomes assessed at baseline and periodically for at least 1 year included (1) symptoms, (2) functioning, (3) cognitive functioning (for SCZ), (4) medication side effects, (5) patient satisfaction, (6) physician satisfaction, (7) quality of life, (8) frequency of contacts with criminal justice and state welfare system, (9) mental health and medical service utilization and cost, and (10) alcohol and substance abuse and supplemental substance use information. Analyses were based on hierarchical linear models designed to test for initial changes and growth in differences between ALGO and TAU patients over time in this matched clinic design.

Itemized clinician ratings versus global ratings of symptom severity in patients with schizophrenia.

This study compares ratings obtained with an itemized clinician-rated symptom severity measure--the 24-item Brief Psychiatric Rating Scale (BPRS24)--with a Physician Global Rating Scale (PhGRS) and a Patient Global Rating Scale (PtGRS) in assessing treatment outcomes in patients with schizophrenia (SCZ). A total of 91 patients (31 inpatients and 60 outpatients) with SCZ were enrolled in a feasibility study of the use of medication algorithms in the treatment of SCZ. Clinicians completed the BPRS24 and the PhGRS; patients completed the PtGRS at each visit. The analyses reported here were conducted using the original BPRS18 and four items from the BPRS18 that rate the positive symptoms of psychosis (the Positive Symptom Rating Scale or PSRS), comparing anchored with global rating scales and with one another. The PtGRS had the lowest effect size (0.8) and was negatively correlated with the other ratings in inpatients. The PhGRS was significantly correlated (0.46) with the BPRS18, but the same person completed both ratings. The effect size of the PhGRS (0.6) was generally lower than with the BPRS18 (1.4) in differentiating responders from non-responders. On average, the PSRS had a slightly lower effect size than the longer itemized BPRS18, but the results support its use as a quantitative rating in circumstances where it is not feasible to routinely use a lengthier scale.