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Updated: Apr 26, 2026

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
Published on: August 11, 2016
Mediodorsal and visual thalamic connectivity differ in schizophrenia and bipolar disorder with and without psychosis
Alan Anticevic1, Genevieve Yang2, Aleksandar Savic3
1alan.anticevic@yale.edu.
Abstract:
Empirical and theoretical studies implicate thalamocortical circuits in schizophrenia, supported by emerging resting-state functional connectivity studies (rs-fcMRI). Similar but attenuated alterations were found in bipolar disorder (BD). However, it remains unknown if segregated loops within thalamocortical systems show distinct rs-fcMRI alterations in schizophrenia. For instance, the mediodorsal (MD) nucleus, known to project to prefrontal networks, may be differently altered than the lateral geniculate nucleus (LGN), known to project to the occipital cortex. Also, it remains unknown if these circuits show different patterns of alterations in BD as a function of psychosis history, which may be associated with a more severe clinical course. We addressed these questions in 90 patients with chronic schizophrenia and 73 remitted BD patients (33 with psychosis history) matched to 146 healthy comparison subjects. We hypothesized that the MD vs LGN would show dissociations across diagnostic groups. We found that MD and LGN show more qualitative similarities than differences in their patterns of dysconnectivity in schizophrenia. In BD, patterns qualitatively diverged between thalamic nuclei although these effects were modest statistically. BD with psychosis history was associated with more severe dysconnectivity, particularly for the MD nucleus. Also, the MD nucleus showed connectivity reductions with the cerebellum in schizophrenia but not in BD. Results suggest dissociations for thalamic nuclei across diagnoses, albeit carefully controlling for medication is warranted in future studies. Collectively, these findings have implications for designing more precise neuroimaging-driven biomarkers that can identify common and divergent large-scale network perturbations across psychiatric diagnoses with shared symptoms.
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