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•The movie animates naming-related neural network dynamics on a millisecond scale.•It visualizes neural interactions via intra- and inter-hemispheric white matter tracts.•Intra/interhemispheric connectivities are enhanced during perception and response.•Left intrahemispheric connectivity predominates during response preparation.•Distinct network dynamics support naming based on images, sounds, or sentences. We built normative brain atlases that animate millisecond-scale intra- and inter-hemispheric white matter-level connectivity dynamics supporting object recognition and speech production. We quantified electrocorticographic modulations during three naming tasks using event-related high-gamma activity from 1,114 nonepileptogenic intracranial electrodes (i.e., non-lesional areas unaffected by epileptiform discharges). Using this electrocorticography data, we visualized functional connectivity modulations defined as significant naming-related high-gamma modulations occurring simultaneously at two sites connected by direct white matter streamlines on diffusion-weighted imaging tractography. Immediately after stimulus onset, intra- and inter-hemispheric functional connectivity enhancements were confined mainly across modality-specific perceptual regions. During response preparation, left intra-hemispheric connectivity enhancements propagated in a posterior-to-anterior direction, involving the left precentral and prefrontal areas. After overt response onset, inter- and intra-hemispheric connectivity enhancements mainly encompassed precentral, postcentral, and superior-temporal (STG) gyri. We found task-specific connectivity enhancements during response preparation as follows. Picture naming enhanced activity along the left arcuate fasciculus between the inferior-temporal and precentral/posterior inferior-frontal (pIFG) gyri. Nonspeech environmental sound naming augmented functional connectivity via the left inferior longitudinal and fronto-occipital fasciculi between the medial-occipital and STG/pIFG. Auditory descriptive naming task enhanced usage of the left frontal U-fibers, involving the middle-frontal gyrus. Taken together, the commonly observed network enhancements include inter-hemispheric connectivity optimizing perceptual processing exerted in each hemisphere, left intra-hemispheric connectivity supporting semantic and lexical processing, and inter-hemispheric connectivity for symmetric oral movements during overt speech. Our atlases improve the currently available models of object recognition and speech production by adding neural dynamics via direct intra- and inter-hemispheric white matter tracts. [Display omitted]

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Psychiatric symptoms are prevalent among people with epilepsy (PWE), yet psychiatric care remains underdeveloped in epilepsy services worldwide. Many psychiatrists lack sufficient familiarity with epilepsy, contributing to gaps in care. Japan, however, has a distinctive history where psychiatrists played a central role in epilepsy treatment, especially in managing epilepsy‐related psychosis. This legacy, though fading, offers valuable insights. This review proposes a renewed framework to reestablish the psychiatrist's role in epilepsy care, informed by Japan's historical context and current global trends. The framework consists of five domains: (1) the historical relationship between psychiatry and epilepsy; (2) diagnosis and treatment of psychiatric symptoms in PWE; (3) psychosocial interventions; (4) interdisciplinary collaboration; and (5) future directions in training, research, policy, and clinical integration. While psychiatry's role in epilepsy has diminished in many countries, Japan may still retain structural and cultural foundations for reintegration. By redefining psychiatric involvement, we aim to inspire general psychiatrists and trainees to engage with epilepsy care. Reaffirming the psychiatric perspective is essential for delivering comprehensive, patient‐centered care to PWE.

The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative ( n  = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program ( n  = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.

•We present the ontogeny of neural dynamics supporting auditory perception.•Early STG responses for noise detection enhance with age.•Subsequent STG neural costs for noise processing reduce with age.•Early left anterior STG responses to spoken sentences enhance with age.•Subsequent left posterior STG responses to spoken sentences reduce with age. : A prominent view of language acquisition involves learning to ignore irrelevant auditory signals through functional reorganization, enabling more efficient processing of relevant information. Yet, few studies have characterized the neural spatiotemporal dynamics supporting rapid detection and subsequent disregard of irrelevant auditory information, in the developing brain. To address this unknown, the present study modeled the developmental acquisition of cost-efficient neural dynamics for auditory processing, using intracranial electrocorticographic responses measured in individuals receiving standard-of-care treatment for drug-resistant, focal epilepsy. We also provided evidence demonstrating the maturation of an anterior-to-posterior functional division within the superior-temporal gyrus (STG), which is known to exist in the adult STG. : We studied 32 patients undergoing extraoperative electrocorticography (age range: eight months to 28 years) and analyzed 2,039 intracranial electrode sites outside the seizure onset zone, interictal spike-generating areas, and MRI lesions. Patients were given forward (normal) speech sounds, backward-played speech sounds, and signal-correlated noises during a task-free condition. We then quantified sound processing-related neural costs at given time windows using high-gamma amplitude at 70–110 Hz and animated the group-level high-gamma dynamics on a spatially normalized three-dimensional brain surface. Finally, we determined if age independently contributed to high-gamma dynamics across brain regions and time windows. : Group-level analysis of noise-related neural costs in the STG revealed developmental enhancement of early high-gamma augmentation and diminution of delayed augmentation. Analysis of speech-related high-gamma activity demonstrated an anterior-to-posterior functional parcellation in the STG. The left anterior STG showed sustained augmentation throughout stimulus presentation, whereas the left posterior STG showed transient augmentation after stimulus onset. We found a double dissociation between the locations and developmental changes in speech sound-related high-gamma dynamics. Early left anterior STG high-gamma augmentation (i.e., within 200 ms post-stimulus onset) showed developmental enhancement, whereas delayed left posterior STG high-gamma augmentation declined with development. : Our observations support the model that, with age, the human STG refines neural dynamics to rapidly detect and subsequently disregard uninformative acoustic noises. Our study also supports the notion that the anterior-to-posterior functional division within the left STG is gradually strengthened for efficient speech-sound perception after birth.

The left prefrontal cortex is essential for verbal communication. It remains uncertain at what timing, to what extent, and what type of phrase initiates left-hemispheric dominant prefrontal activation during comprehension of spoken sentences. We clarified this issue by measuring event-related high-gamma activity during a task to respond to three-phrase questions configured in different orders. Questions beginning with a wh- interrogative deactivated the left posterior prefrontal cortex right after the 1st phrase offset and the anterior prefrontal cortex after the 2nd phrase offset. Left prefrontal high-gamma activity augmented subsequently and maximized around the 3rd phrase offset. Conversely, questions starting with a concrete phrase deactivated the right orbitofrontal region and then activated the left posterior prefrontal cortex after the 1st phrase offset. Regardless of sentence types, high-gamma activity emerged earlier, by one phrase, in the left posterior prefrontal than anterior prefrontal region. Sentences beginning with a wh- interrogative may initially deactivate the left prefrontal cortex to prioritize the bottom-up processing of upcoming auditory information. A concrete phrase may obliterate the inhibitory function of the right orbitofrontal region and facilitate top-down lexical prediction by the left prefrontal cortex. The left anterior prefrontal regions may be recruited for semantic integration of multiple concrete phrases.

Type 2 diabetes (T2D) risk prediction remains a challenge, particularly in underrepresented populations, including people living with HIV (PWH) and those of non-European ancestry. We evaluated the performance of two metaPRS (polygenic risk score) models, integrating genetic markers related to inflammation and lipid metabolism, in predicting T2D risk across ancestry groups (African and European), with and without HIV. The metaPRS were generated in a subset from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study (6,034 Black; 11,972 White) and validated in 7,580 (4,120 Black; 3,460 White) PWH from the Centers for AIDS Research of Integrated Clinical Systems (CNICS), as well as an additional 4,152 (2,586 Black; 1,566 White) seronegative participants from REGARDS. Incorporating the metaPRS into models provided non-significant improvements in T2D risk prediction compared to single-trait T2D PRS and clinical risk factors. Performance was similar in PWH and in people without HIV, suggesting that these general population-derived genetic scores are transferable to PWH. Future studies should focus on refining PRS models in diverse populations and exploring genetic factors specific to PWH regarding T2D risk.

Background The present study aimed to investigate the efficacy of positron emission tomography with 18Fluoro‐deoxyglucose (FDG‐PET/CT) for predicting malignant intraductal papillary mucinous neoplasm (IPMN). Methods The records of 88 patients pathologically diagnosed with IPMN after surgery at Ehime University Hospital and Ehime Prefectural Central Hospital from April 2009 to December 2020 were retrospectively reviewed. The patients’ characteristics, blood chemistry, and imaging examinations were evaluated as potential predictors of malignant IPMN. Of the PET/CT results, the maximum standardized uptake value (SUVmax) of the tumor, the tumor‐to‐blood pool ratio of the SUV (TBR), and the tumor‐to‐liver ratio of the SUV (TLR) were compared. Results On pathology, the diagnosis was adenoma (IPMA) in 40 patients, high‐grade dysplasia (HGD) in 26 patients, and carcinoma (IPMC) in 22 patients. HGD and IPMC were defined as malignant IPMN. On multivariate analyses, TLR ≥ 1.3 and high‐risk stigmata were independent predictors of malignant IPMN (P = .001 and P = .007, respectively). When both HRS and TLR ≥ 1.3 were present, the positive predictive value for malignancy was 88.2%. Furthermore, TLR was significantly higher for patients with IPMC than with HGD (P = .039). Conclusion TLR can be a useful predictor for differentiating benign from malignant IPMN and may be associated with postoperative outcomes. Although the FDG PET/CT maximum standardized uptake value (SUVmax) of the tumor is a useful parameter, it is not an absolute value but a relative value, and it is affected by various factors such as equipment and imaging protocols. In order to make SUVmax more universal, the ratio of SUVmax of the tumor to SUV mean of the liver right lobe (TLR) was investigated. The present study showed that the TLR can be a useful predictor for differentiating benign from malignant IPMNs and may be associated with outcomes, such as postoperative recurrence.

The pathogenesis and clinical heterogeneity of Parkinson’s disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA  + mutation carriers and 154 LRRK2  + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA  + patients and unaffected GBA  + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2  + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into “endotypes”. The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.