511-schizophrenia

Schizophrenia

Overview

• Lifetime prevalence ~ 0.3-0.7%
  • Broader definitions suggest 2-3 or 3-5%
• ~1/3 chronic & severe
• Onset post-puberty, early adulthood
• Males earlier onset & greater severity
• Pervasive disturbance in mood, thinking, movement, action, memory, perception
• Increased (early) mortality
• Geographic variations in incidence, prevalence, and mortality (McGrath, Saha, Chant, & Welham, 2008)

Symptoms

Psychotic (“positive”) symptoms

• “Additions” to behavior
• Disordered thought
• Delusions of grandeur, persecution
• Hallucinations (usually auditory)
• Bizarre behavior

“Negative” symptoms

• “Reductions” in behavior
• Poverty of speech
• Flat affect
• Social withdrawal
• Anhedonia (loss of pleasure)
• Catatonia (reduced movement)

Cognitive symptoms

• Memory
• Attention
• Planning, decision-making
• Social cognition
• Movement

Affective dysregulation

• Depressive, manic states

(Os & Kapur, 2009)

(Os & Kapur, 2009)

Biological bases

• Genetic predisposition
• Brain abnormalities
• Neurochemical factors
• Developmental origins

Genetic predisposition

• Heritability 80%
• vs. 60% for osteoarthritis
• 30-50% for hypertension

(Os & Kapur, 2009)

• But, no single gene…

(Johnson et al., 2017)

• NOTCH4, TNF:
  • Part of major histocompatibility complex (MHC), cell membrane specializations involved in the immune system
• DRD2 (dopamine D2 receptor), KCNN3 (Ca+ activated K+ channel), GRM3 (metabotropic glutamate receptor); (Johnson et al., 2017)

Brain abnormalities

Ventricles larger, esp in males

(Suddath, Christison, Torrey, Casanova, & Weinberger, 1990)

• Ventricular enlargement increases across time
• Especially in the more impaired

(Davis et al., 1998)

(Davis et al., 1998)

(Kempton, Stahl, Williams, & DeLisi, 2010)

• Enlargement precedes diagnosis?
• Like trajectories B or F

(Kempton et al., 2010)

Hippocampus, amygdala, thalamus, nucleus accumbens (NAcc) smaller

• Related to ventricular enlargement?
• Early disturbance in brain development?

(Erp et al., 2015)

Cohen’s d effect sizes±s.e. for regional brain volume differences between Individuals with schizophrenia and healthy controls. Effect sizes for all subcortical volumes depicted were corrected for sex, age and intracranial volume (ICV). The effect size for ICV was corrected for sex and age. The number of independent data points (NSz and NHV) for each region are listed in Table 1.

Animal model example

• Dentate gyrus (DG) in hippocampus
  • spatial coding, learning & memory, emotion processing
• DG dysfunction implicated in schizophrenia
• Gene linked to schizophrenia, Transmembrane protein 108 (Tmem108) enriched in DG granule neurons
• Tmem108 expression increased during postnatal period critical for DG development
• Tmem108-deficient neurons form fewer and smaller spines
• Tmem108-deficient mice display schizophrenia-relevant behavioral deficits
• (Jiao et al., 2017)

White matter disruption

(Kelly et al., 2017)

1. Fractional anisotropy (FA) differences between schizophrenia patients and healthy controls for 25 white matter (WM) regions representing major fasciculi. Gradient bar indicates Cohen’s d effect sizes after meta-analysis. (b) Cohen’s d effect sizes after meta-analysis, sorted in increasing magnitude of Cohen’s d effect sizes across 29 cohorts for FA differences in schizophrenia patients (N=1963) versus healthy controls (N=2359), after including age, sex, age × sex, age2 and age2 × sex as covariates. Error bars represent 95% confidence intervals. Significant regions after adjusting for multiple regions tested (P<0.05/25=0.002) are highlighted in orange. (c) Forest plot of effect sizes for 29 cohorts. Interactive three-dimensional (3D) visualization of the results is available at www.enigma-viewer.org.

• White matter loss over age

(Kochunov et al., 2016)

Disconnectivity in cortical networks

(Uhlhaas, 2013)

• But connectivity findings inconsistent (Fornito & Bullmore, 2015)
• Reduced structural connectivity vs.
  • Synaptic, dendritic, axonal connections b/w regions
  • Usually measured via DTI or related diffusion-based MRI technique
• Increased functional connectivity
  • BOLD, EEG, or MEG covariance
  • Task-free ‘resting’ state or task-based

(Fornito & Bullmore, 2015)

Figure 1. De-coupling of network structure and function in schizophrenia. (a) Shows an example of a brain-wide map of structural connectivity deficits in patients with schizophrenia, highlighting a relatively diffuse impairment that particularly affects fronto-posterior anatomical connectivity. In this whole-brain analysis, no increases of structural connectivity were found. Letters denote different regions (see below for key). (b) Illustrates frontal regions showing decreased and increased functional connectivity with seed regions in the dorsal (top) and ventral (bottom) caudate nucleus, respectively, in patients with schizophrenia (yellow, blue) and their unaffected, first-degree relatives (magenta, green). Thus, despite a fairly global impairment of structural connectivity (depicted in (a)), systems-specific increases in functional connectivity can be observed (b). (c,d) Brain-wide alterations of structural (c) and functional (d) connectivity in the same sample of patients with schizophrenia. Blue and green depict links where anatomical and functional connectivity, respectively, were reduced in patients; red depicts links where functional connectivity was increased in the patient group. (a) reproduced from [24•], (b) from [18•], and (c,d) from [23••] with permission. Regional abbreviations in (a) are as follow: A. Left Superior Frontal, B. Right Superior Frontal, C. Left Supplementary Motor Area, D. Left Superior Medial Frontal, E. Right Supplementary Motor Area, F. Right Superior Medial Frontal, G. Right Superior Parietal, H. Right Superior Occipital, I. Left Cuneus, J. Left Superior Occipital, K. Left Precuneus, L. Right Precuneus, M. Left Middle Temporal, N. Left Middle Occipital, O. Left Inferior Temporal, P. Left Fusiform, Q. Right Cuneus, R. Left Hippocampus, S. Left Middle Cingulum.

• Global signal (cortical gray matter BOLD signal CGm) variations?

(Yang et al., 2014)

Fig 1. Power and variance of CGm signal in SCZ and BD. (A) Power of CGm signal in 90 SCZ patients (red) relative to 90 HCS (black) (see SI Appendix, Table S1 for demographics). (B) Mean power across all frequencies before and after GSR indicating an increase in SCZ [F(1, 178) = 7.42, P < 0.01], and attenuation by GSR [F(1, 178) = 5.37, P < 0.025]. (C) CGm variance also showed increases in SCZ [F(1, 178) = 7.25, P < 0.01] and GSR-induced reduction in SCZ [F(1, 178) = 5.25, P < 0.025]. (D–F) Independent SCZ sample (see SI Appendix, Table S2 for demographics), confirming increased CGm power [F(1, 143) = 9.2, P < 0.01] and variance [F(1, 143) = 9.25, P < 0.01] effects, but also the attenuating impact of GSR on power [F(1, 143) = 7.75, P < 0.01] and variance [F(1, 143) = 8.1, P < 0.01]. (G–I) Results for BD patients (n = 73) relative to matched HCS (see SI Appendix, Table S3 for demographics) did not reveal GSR effects observed in SCZ samples [F(1, 127) = 2.89, P = 0.092, n.s.] and no evidence for increase in CGm power or variance. All effects remained when examining all gray matter voxels (SI Appendix, Fig. S1). Error bars mark ± 1 SEM. ***P < 0.001 level of significance. n.s., not significant.

(Yang et al., 2014)

Fig 2. Relationship between SCZ symptoms and CGm BOLD signal power. We extracted average CGm power for each patient with available symptom ratings (n = 153). (A) Significant positive relationship between CGm power and symptom ratings in SCZ (r = 0.18, P < 0.03), verified using Spearman’s ρ given somewhat nonnormally distributed data (ρ = 0.2, P < 0.015). (B and C) Results held across SCZ samples, increasing confidence in the effect (i.e., joint probability of independent effects P < 0.002, marked in blue boxes). All identified relationships held when examining Gm variance (SI Appendix, Fig. S4). Notably, all effects were no longer significant after GSR, suggesting GS carries clinically meaningful information. The shaded area marks the 95% confidence interval around the best-fit line.

• Disconnectivity b/w ‘hubs’ -> higher functional connectivity

(Fornito & Bullmore, 2015)

Neurochemical factors

Dopamine hypothesis

Evidence for…

• DA (\(D_2\) receptor) antagonists (e.g. chlorpromazine)
  • improve positive symptoms
• Typical antipsychotics are DA \(D_2\) antagonists
• DA agonists
  • amphetamine, cocaine, L-DOPA
  • mimic or exacerbate symptoms

Evidence against…

• New, atypical antipsychotics
  • (e.g. Clozapine) INCREASE DA in frontal cortex, affect 5-HT
• Mixed evidence for high DA metabolite levels in CSF
• Some DA neurons may release 5-HT, cannabinoids, glutamate (Seutin, 2005)

Glutamate/ketamine hypothesis

• Psychomimetic drugs induce schizophrenia-like states
  • Phencyclidine (PCP), ketamine
  • both are NMDA receptor antagonists
• Ketamine
  • dissociative (secondary) anesthetic
  • side effects include hallucinations, blurred vision, delirium, floating sensations, vivid dreams
  • binds to serotonin (\(5HT_{2a}\)) receptor, \(\kappa\) opioid receptor, and \(\sigma\) receptor “chaperone”
  • may be dopamine \(D_2\) receptor antagonist
• Schizophrenia \(\rightarrow\) underactivation of NMDA receptors?
  • NMDA receptor role in learning, plasticity
  • DG neurons in (Jiao et al., 2017) were glutamate-releasing.
• NMDAR antagonists -> neurodegeneration, excitotoxicity, & apoptosis

Developmental origins

Rapid gray matter loss in adolescents?

(Thompson et al., 2001)

Early life stress

• 2x greater odds for children in urban environments
• Higher risk among migrant populations (Cantor-Graae & Selten, 2005)
• Exposure to infection in utero, other birth complications
• Exposure to cannibis
• Paternal age > 40

Complex pathways

• (Levine, Levav, Pugachova, Yoffe, & Becher, 2016)
  • Children (N=51,233) of parents who born during Nazi era (1922-1945)
  • Emigrated before (indirect exposure) or after (direct exposure) to Nazi era
  • Children exposed to direct stress of Nazi era in utero or postnatally
    • Did not differ in rates of schizophrenia, but
    • Had higher rehospitalization rates
• (Debost et al., 2015)
  • Danish cohort (n=1,141,447)
  • Exposure to early life stress
    • in utero did not increase risk of schizophrenia, but
    • during 0-2 years increased risk
  • Increased risk associated with an allele of a cortisol-related gene

In summary

• Wide-ranging disturbance of mood, thought, action, perception
• Broad changes in brain structure, function, chemistry, development
• Dopamine hypothesis giving way to glutamate hypothesis
• Genetic (polygenic = multiple genes) risk + environmental factors

Prospects

Outcomes following hospitalization

(Os & Kapur, 2009)

The future of psychiatric research

• The Research Domain Criteria (RDoC) Project
  • Negative valence, positive valence, cognitive systems, social processes, arousal/regulatory systems
• U.K. Biobank
• Enhancing Neuro Imaging Genetics through Meta Analysis (ENIGMA) Consortium

References

Cantor-Graae, E., & Selten, J.-P. (2005). Schizophrenia and migration: A meta-analysis and review. The American Journal of Psychiatry, 162(1), 12–24. https://doi.org/10.1176/appi.ajp.162.1.12

Davis, K. L., Buchsbaum, M. S., Shihabuddin, L., Spiegel-Cohen, J., Metzger, M., Frecska, E., … Powchik, P. (1998). Ventricular enlargement in poor-outcome schizophrenia. Biological Psychiatry, 43(11), 783–793. https://doi.org/10.1016/s0006-3223(97)00553-2

Debost, J.-C., Petersen, L., Grove, J., Hedemand, A., Khashan, A., Henriksen, T., … Mortensen, P. B. (2015). Investigating interactions between early life stress and two single nucleotide polymorphisms in HSD11B2 on the risk of schizophrenia. Psychoneuroendocrinology, 60, 18–27. https://doi.org/10.1016/j.psyneuen.2015.05.013

Erp, T. G. M. van, Hibar, D. P., Rasmussen, J. M., Glahn, D. C., Pearlson, G. D., Andreassen, O. A., … Turner, J. A. (2015). Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry. https://doi.org/10.1038/mp.2015.63

Fornito, A., & Bullmore, E. T. (2015). Reconciling abnormalities of brain network structure and function in schizophrenia. Curr. Opin. Neurobiol., 30, 44–50. https://doi.org/10.1016/j.conb.2014.08.006

Jiao, H.-F., Sun, X.-D., Bates, R., Xiong, L., Zhang, L., Liu, F., … Mei, L. (2017). Transmembrane protein 108 is required for glutamatergic transmission in dentate gyrus. Proceedings of the National Academy of Sciences, 114(5), 1177–1182. https://doi.org/10.1073/pnas.1618213114

Johnson, E. C., Border, R., Melroy-Greif, W. E., Leeuw, C. A. de, Ehringer, M. A., & Keller, M. C. (2017). No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol. Psychiatry, 82(10), 702–708. https://doi.org/10.1016/j.biopsych.2017.06.033

Kelly, S., Jahanshad, N., Zalesky, A., Kochunov, P., Agartz, I., Alloza, C., … Donohoe, G. (2017). Widespread white matter microstructural differences in schizophrenia across 4322 individuals: Results from the ENIGMA schizophrenia DTI working group. Mol. Psychiatry. https://doi.org/10.1038/mp.2017.170

Kempton, M. J., Stahl, D., Williams, S. C. R., & DeLisi, L. E. (2010). Progressive lateral ventricular enlargement in schizophrenia: A meta-analysis of longitudinal MRI studies. Schizophr. Res., 120(1-3), 54–62. https://doi.org/10.1016/j.schres.2010.03.036

Kochunov, P., Ganjgahi, H., Winkler, A., Kelly, S., Shukla, D. K., Du, X., … Hong, L. E. (2016). Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia. Hum. Brain Mapp., 37(12), 4673–4688. https://doi.org/10.1002/hbm.23336

Levine, S. Z., Levav, I., Pugachova, I., Yoffe, R., & Becher, Y. (2016). Transgenerational effects of genocide exposure on the risk and course of schizophrenia: A population-based study. Schizophrenia Research, 176(2), 540–545. https://doi.org/10.1016/j.schres.2016.06.019

McGrath, J., Saha, S., Chant, D., & Welham, J. (2008). Schizophrenia: A concise overview of incidence, prevalence, and mortality. Epidemiologic Reviews, 30, 67–76. https://doi.org/10.1093/epirev/mxn001

Os, J. van, & Kapur, S. (2009). Schizophrenia. The Lancet, 374(9690), 635–645. https://doi.org/10.1016/S0140-6736(09)60995-8

Seutin, V. (2005). Dopaminergic neurones: Much more than dopamine? Br. J. Pharmacol., 146(2), 167–169. https://doi.org/10.1038/sj.bjp.0706328

Suddath, R. L., Christison, G. W., Torrey, E. F., Casanova, M. F., & Weinberger, D. R. (1990). Anatomical abnormalities in the brains of monozygotic twins discordant for schizophrenia. The New England Journal of Medicine, 322(12), 789–794. https://doi.org/10.1056/NEJM199003223221201

Thompson, P. M., Vidal, C., Giedd, J. N., Gochman, P., Blumenthal, J., Nicolson, R., … Rapoport, J. L. (2001). Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proceedings of the National Academy of Sciences, 98(20), 11650–11655. https://doi.org/10.1073/pnas.201243998

Uhlhaas, P. J. (2013). Dysconnectivity, large-scale networks and neuronal dynamics in schizophrenia. Curr. Opin. Neurobiol., 23(2), 283–290. https://doi.org/10.1016/j.conb.2012.11.004

Yang, G. J., Murray, J. D., Repovs, G., Cole, M. W., Savic, A., Glasser, M. F., … Anticevic, A. (2014). Altered global brain signal in schizophrenia. Proc. Natl. Acad. Sci. U. S. A., 111(20), 7438–7443. https://doi.org/10.1073/pnas.1405289111