This study examined the effects of misregistration on electrode position in deep brain stimulation (DBS) by using the AFIDs framework to measure registration accuracy in patient scans. AFID registration errors (AFREs) revealed spatial patterns of misregistration that accounted for 28% of the variance in electrode position, highlighting AFIDs as a valuable tool for assessing registration accuracy, quality control, and optimization in DBS research.

This study created normative brain growth charts for rhesus macaques using over 1,500 MRI scans, addressing a gap in nonhuman primate developmental models. It mapped lifespan trajectories of brain volume, cortical thickness, and surface area, identifying key developmental milestones and similarities to human brain maturation. The resulting open-access resource enables cross-species comparisons and supports translational neuroscience, particularly in studies with small NHP sample sizes.

This study investigated the genetic relationship between cannabis use and schizophrenia using data from the All of Us Research Program. Researchers found that genetic risk for both cannabis use disorder (CUD) and schizophrenia independently contributed to heavy cannabis use and schizophrenia, with evidence of pleiotropy even among individuals without documented cannabis use. These results highlight the importance of incorporating genetic risk for cannabis use in models of schizophrenia to better understand its underlying causes.

This study introduces a novel mathematical method for analyzing complex spike patterns from large-scale neural recordings. By decomposing spike data into simple, structured elements, the approach enables comparison across trials, sub-pattern detection, and links to behavior using a clear distance measure. Applied to macaque motor and prefrontal cortex recordings, the method uncovered previously hidden neural structures that predict memory-guided decisions and errors, offering a powerful new tool for interpreting large-scale neural activity.

This systematic review and meta-analysis examined grammatical (syntactic) impairments in people with schizophrenia across 45 studies. Findings showed strong and consistent evidence that individuals with schizophrenia have significant difficulties in both understanding and producing syntactically complex language, with syntactic comprehension being the most affected. The study highlights the importance of addressing these language deficits through targeted cognitive and linguistic interventions, and it underscores the potential for personalized communication strategies in clinical care.

This study examined the topographic organization of the zona incerta (ZI) using in vivo diffusion MRI and data-driven connectivity. The findings revealed a rostral-caudal gradient, with the rostral ZI connecting to prefrontal regions and the caudal ZI connecting to sensorimotor cortices, as well as a central ZI region linked to the dorsal prefrontal cortex. These results, replicated across datasets and individuals, provide insights into the ZI’s role in motor, cognitive, and emotional control, with implications for refining neuromodulatory targets.

This study examined whether structural patterns in the early visual cortex are linked to PTSD symptoms. Using two large datasets, researchers found that reduced structural covariance in this brain region was specifically associated with PTSD symptoms, but not with depression or general stress. These findings suggest a potential neural signature of PTSD that may help improve diagnosis and understanding of the disorder.

This study disentangles the what and how components of sequence learning in one experimental paradigm. We confirm that when sequence items are unknown, most learning is learning what to do. However, when the sequence items are known from the beginning, practice still leads to improvements that are effector-specific and generalizable to other sequences.

Recent advances in artificial intelligence provide opportunities to automate the capture and representation of complex features of human language, potentially improving the efficiency of language assessment. This review presents computerized approaches for analyzing narrative language and identifying language disorders in children, highlighting key methods, challenges, and implications for clinical practice.

This study examined whether learning sign language influences visual perception by comparing bimodal bilinguals (hearing signers) and hearing non-signers on face matching and motion discrimination tasks. While no group differences were found, higher ASL proficiency predicted better motion perception, suggesting that linguistic experience may shape visual processing in specific ways.

Sensory expectations influence movement preparation, as humans and monkeys probabilistically cued about a future mechanical perturbation adjust their preparatory activity and improve corrective responses. High-density neural recordings reveal that these expectations shape motor cortical dynamics, with neural population activity scaling with perturbation probability and driving rapid responses through learned sensory-feedback integration.

This study conducted 13 independent replications of Nozaradan et al. (2011) to test whether neural activity at imagined beat frequencies reflects conscious beat perception. While small effects were observed, they were much weaker than in the original study and not influenced by music or dance training, suggesting that larger samples are needed to detect such effects reliably. Contrary to expectations, only neural activity at the stimulus frequency—not the imagery-related frequency—predicted task performance, raising questions about the reliability of frequency tagging for studying conscious beat perception.

This study examined brain-based subtypes across individuals with autism, ADHD, and typical development using MRI measures, focusing on cortical myelination (T1w/T2w-ratio). Although no significant differences in myelination were found between diagnostic groups, multimodal clustering (combining myelination, cortical thickness, and surface area) revealed three distinct subgroups that cut across diagnoses. These findings suggest that multimodal brain imaging may help identify biologically meaningful subtypes, potentially improving personalized approaches to neurodevelopmental conditions.

Extracellular vesicles (EVs) carry molecular signatures of their parent cells and hold promise as biomarkers for neurological diseases, yet their lipid content remains understudied. In this study, we used LC-MS/MS to analyze lipidomic changes in BV-2 microglia and their EVs following pro-inflammatory stimulation with LPS, revealing distinct lipid profiles that reflect cellular activation states. These findings highlight the potential of EV lipidomics to infer cellular function and support further exploration of EV lipids as biomarkers in neurological disease.

This study looked at the link between attention and divisive normalization in humans. They used frequency-tagged fMRI to isolate visual cortical populations that simultaneously encode multiple competing inputs. The results show that responses of these sites are suppressed during inattention and enhanced during attention - offering a noninvasive translational bridge to study fine-grained computations underlying attentional selection.

This study examined auditory brainstem development in Autistic children and adults using ABRs and ASRs to understand sensory processing differences. Autistic children showed reduced and delayed auditory responses, while Autistic adults exhibited heightened reactivity, suggesting a developmental shift from early hypo-responsivity to later hyper-reactivity. These findings highlight how early auditory disruptions may lead to long-term changes in sensory processing in Autism.

This study shows that targeted loss of the ATRX chromatin remodeler in microglia alters chromatin accessibility profiles, leading to the de-repression of endogenous retroelements, triggering viral mimicry. Functionally, we find that ATRX microglial deficiency alters the electrophysiological properties of hippocampal neurons and causes deficits in object recognition and spatial memory.

This study developed and tested AI models to predict successful psychotropic medication use in neurodivergent children, aiming to reduce trial-and-error prescribing. Using data from research cohorts and clinical electronic medical records, the models accurately predicted medication success across stimulants, antidepressants, and antipsychotics, with strong performance in both internal and external datasets. The findings support the feasibility of AI-based decision aids to improve personalized medication management and enhance care in community settings.

This study systematically evaluates AudioDome capabilities at the Western Interdisciplinary Research Building, assessing the strengths and limitations of its sound reproduction technologies for research applications. The findings presented in this manuscript offer critical insights into the methodological validity of the AudioDome, providing WIN researchers with a foundational framework for its effective use in experimentation.