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. 2015 Sep;72(9):882-91.
doi: 10.1001/jamapsychiatry.2015.0566.

Association of Thalamic Dysconnectivity and Conversion to Psychosis in Youth and Young Adults at Elevated Clinical Risk

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Free PMC article

Association of Thalamic Dysconnectivity and Conversion to Psychosis in Youth and Young Adults at Elevated Clinical Risk

Alan Anticevic et al. JAMA Psychiatry. .
Free PMC article

Abstract

Importance: Severe neuropsychiatric conditions, such as schizophrenia, affect distributed neural computations. One candidate system profoundly altered in chronic schizophrenia involves the thalamocortical networks. It is widely acknowledged that schizophrenia is a neurodevelopmental disorder that likely affects the brain before onset of clinical symptoms. However, no investigation has tested whether thalamocortical connectivity is altered in individuals at risk for psychosis or whether this pattern is more severe in individuals who later develop full-blown illness.

Objectives: To determine whether baseline thalamocortical connectivity differs between individuals at clinical high risk for psychosis and healthy controls, whether this pattern is more severe in those who later convert to full-blown illness, and whether magnitude of thalamocortical dysconnectivity is associated with baseline prodromal symptom severity.

Design, setting, and participants: In this multicenter, 2-year follow-up, case-control study, we examined 397 participants aged 12-35 years of age (243 individuals at clinical high risk of psychosis, of whom 21 converted to full-blown illness, and 154 healthy controls). The baseline scan dates were January 15, 2010, to April 30, 2012.

Main outcomes and measures: Whole-brain thalamic functional connectivity maps were generated using individuals' anatomically defined thalamic seeds, measured using resting-state functional connectivity magnetic resonance imaging.

Results: Using baseline magnetic resonance images, we identified thalamocortical dysconnectivity in the 243 individuals at clinical high risk for psychosis, which was particularly pronounced in the 21 participants who converted to full-blown illness. The pattern involved widespread hypoconnectivity between the thalamus and prefrontal and cerebellar areas, which was more prominent in those who converted to full-blown illness (t(173) = 3.77, P < .001, Hedge g = 0.88). Conversely, there was marked thalamic hyperconnectivity with sensory motor areas, again most pronounced in those who converted to full-blown illness (t(173) = 2.85, P < .001, Hedge g = 0.66). Both patterns were significantly correlated with concurrent prodromal symptom severity (r = 0.27, P < 3.6 × 10(-8), Spearman ρ = 0.27, P < 4.75 × 10(-5), 2-tailed).

Conclusions and relevance: Thalamic dysconnectivity, resembling that seen in schizophrenia, was evident in individuals at clinical high risk for psychosis and more prominently in those who later converted to psychosis. Dysconnectivity correlated with symptom severity, supporting the idea that thalamic connectivity may have prognostic implications for risk of conversion to full-blown illness.

Conflict of interest statement

Conflict of Interest Disclosures: None reported.

Figures

Figure 1
Figure 1. Regions With Between-Group Differences in Thalamic Connectivity
Significant between-group effects were found for 5 regions of interest (ROIs) after a 1-way analysis of variance F test using cluster protection after 10 000 permutations (see Methods). Results were visualized using surface-based and volume maps. All displayed foci revealed significant between-group effects within the thalamocortical masks identified in our prior work (in which patients with chronic illness exhibited robust thalamocortical disruptions; eAppendix and eTables 3 and 4 in the Supplement). Blue and yellow areas indicate regions where the F test is driven by a reduction or increase, respectively, in thalamic connectivity in the clinical high risk for psychosis (CHR) groups. Magnitudes (left) and distributions (right) across groups for each of the identified regions qualitatively illustrate the direction of the effect. Effect sizes (Hedge g [Hg]) reflect the shift for the CHR converted to full-blown illness (CHR-C) group relative to controls. For a complete list of regions and statistics, see the Table. We used the Hg as a measure of effect size to account for differences in sample size between the CHR-C and CHR-nonconversion (CHR-NC) groups. Error bars indicate ±1 SEM. The histograms are based on the data extracted from the F map presented in the surface view panel. We present reduced-threshold pairwise effects in the eAppendix in the Supplement. L indicates left; and R, right. a P < .01. b P < .001. c P < .05.
Figure 2
Figure 2. Association Between Thalamic Hyperconnectivity and Hypoconnectivity Across Study Participants
Regions with reduced (blue) and increased (yellow) thalamic connectivity (Figure 1). A significant negative association is evident across all participants, collapsing across all 3 samples (r = −0.58, P < 4.1 × 10−38). Vertical/horizontal green dashed lines mark the zero points. Patients with clinical high risk of psychosis (CHR) who converted to full-blown illness (CHR-C) had a shift across the zero lines, indicative of weaker prefrontal-cerebellar-thalamic connectivity but stronger sensory-motor-thalamic connectivity. Patients with CHR who did not convert (CHR-NC) had a more intermediate degree of disruption, suggesting a gradient (inset arrow for qualitative illustration). Ovals for each group mark the 95%CI. L indicates left; and R, right.
Figure 3
Figure 3. Association Between Prodromal Schizophrenia Symptoms and Thalamic Dysconnectivity
Regions showing reduced (blue) and increased (yellow) thalamic connectivity. Significant positive association was found between thalamic connectivity across all areas showing increased connectivity (yellow regions) and composite positive symptoms on the Scale of Prodromal Symptoms (SOPS) across all participants (r = 0.21, P < 4.1 × 10−5, Spearman ρ = 0.21, P < 4.8 × 10−5). A significant negative association was found between thalamic connectivity across all areas showing reduced connectivity (blue regions) and composite positive symptoms on the SOPS across all participants (r = −0.31, P < 9.54 × 10−10, Spearman ρ = −0.32, P < 1.36 × 10−9, 2-tailed). We computed a difference score between the regions showing hyperconnectivity (yellow) and hypoconnectivity (blue). The purpose of this calculation was to establish that the total magnitude of connectivity disruptions in either direction still relates to psychotic symptoms as opposed to these 2 patterns capturing independent sources of variability. We found a significant association between thalamic connectivity difference score and composite positive symptoms on the SOPS across all participants (r = 0.27, P < 3.6 × 10−8, Spearman ρ = 0.27, P < 4.75 × 10−5, 2-tailed). For a figure presenting clinical high risk (CHR) patients only, see eFigure 7 in the Supplement. CHR-C indicates clinical high risk of psychosis converted to full-blown illness; CHR-NC, clinical high risk of psychosis not converted to full-blown illness; L indicates left; and R, right.
Figure 4
Figure 4. Thalamic Dysconnectivity as a Function of Medication Status
Regions with reduced (blue) and increased (yellow) thalamic connectivity, driven by the clinical high risk of psychosis (CHR) converted to full-blown illness (CHR-C). The association between medication status and thalamic hyperconnectivity indicated that CHR-C patients who remained unmedicated (CHR-C-UM) exhibited the most severe hyperconnectivity, significantly differing from controls (t169 = 3.7, P < .001, 2-tailed, Hedge g = 0.81) and CHR patients who did not convert (CHR-NC) without medication (CHR-NC-UM) (t190 = 2.27, P = .02, 2-tailed, Hedge g = 0.53). However, no significant difference was found between medicated (CHR-C-M and CHR-NC-M) and CHR-C-UM and CHR-NC-UM patients (t19 = 0.46, P = .76). Again, CHR-C-UM patients exhibited significant reductions in thalamic connectivity with prefrontal cortex and cerebellar nodes relative to controls (t169 = 2.34, P = .02, 2-tailed, Hedge g = 0.31) but not the other clinical groups. We used Hedge g as a measure of effect size to account for differences in sample sizes. Error bars indicate ±1 SEM. L indicates left; and R, right. a P < .001. b P < .05.

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