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Background: Working memory (WM) deficits are a key feature of schizophrenia and are also seen in unaffected siblings. These deficits might arise from disrupted transitions from one brain state to another. Using a robust algorithm called the Bayesian Switching Dynamical System (BSDS), we studied hidden brain states and their transitions during a WM task in people with schizophrenia.
Methods: We used BSDS to identify brain states based on regions of interest (ROIs) within the default mode network and the frontoparietal network in 161 patients with schizophrenia, 37 unaffected siblings, and 96 healthy controls during N-back (0, 2, and resting fixation) tasks. We estimated group differences in the properties of brain states and studied the influence of WM performance and clinical characteristics on them using General Linear Models.
Results: We identified 4 brain states underlying the WM task: high-demand, low-demand, fixation, and non-dominant states. Compared with controls and siblings, patients showed reduced occupancy and lifetime of high-demand state during the "2-back," reduced lifetime of low-demand state during the "0-back," but increased occupancy and lifetime of fixation state during both task periods. Aberrant high-demand state mediated the association between WM performance and negative symptoms. Compared with controls and patients, siblings showed increased occupancy of high-demand and reduced fixation state during the resting fixation condition; this putative compensatory process correlated with better WM performance.
Conclusions: Latent brain states of intrinsic connectivity that represent internal mental processes affect WM performance, influencing the expression of negative symptoms in schizophrenia and cognitive resilience in unaffected siblings.
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http://dx.doi.org/10.1186/s12916-025-04216-6 | DOI Listing |
Cereb Cortex
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Department of Psychology, University of Lübeck, Ratzeburger Allee 160, Lübeck 23562, Germany.
The human auditory system must distinguish relevant sounds from noise. Severe hearing loss can be treated with cochlear implants (CIs), but how the brain adapts to electrical hearing remains unclear. This study examined adaptation to unilateral CI use in the first and seventh months after CI activation using speech comprehension measures and electroencephalography recordings, both during passive listening and an active spatial listening task.
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Research Imaging Institute, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229, United States.
Statistical Parametric Mapping (SPM) adheres to rigorous methodological standards, including: spatial normalization, inter-subject averaging, voxel-wise contrasts, and coordinate reporting. This rigor ensures that a thematically diverse literature is amenable to meta-analysis. BrainMap is a community database (www.
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Department of Psychology, Stanford University, 450 Jane Stanford Way, Building 420, Stanford, CA 94305, United States.
The SPM software package played a major role in the establishment of open source software practices within the field of neuroimaging. I outline its role in my career development and the impact it has had within our field.
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August 2025
Section on Functional Imaging Methods & Functional MRI Core Facility, National Institute of Mental Health, 10 Center Drive, Rm 1D80, Bethesda, MD 20892, United States.
Statistical Parametric Mapping (SPM) has been profoundly influential to neuroimaging as it has fostered rigorous, statistically grounded structure for model-based inferences that have led to mechanistic insights about the human brain over the past 30 years. The statistical constructs shared with the world through SPM have been instrumental for deriving meaning from neuroimaging data; however, they require simplifying assumptions which can provide results that, while statistically sound, may not accurately reflect the mechanisms of brain function. A platform that fosters the exploration of the rich and varying neuronal and physiologic underpinnings of the measured signals and their associations to behavior and physiologic measures needs a different set of tools.
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September 2025
Neuropsychology and Applied Cognitive Neuroscience Laboratory, State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Reward processing involves several components, including reward anticipation, cost-effort computation, reward consumption, reward sensitivity, and reward learning. Recent research has highlighted the cerebellum's role in reward processing. This study aimed to investigate the effects of cerebellar stimulation on reward processing using high-definition transcranial direct current stimulation (HD-tDCS).
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