Cited by Reply to ‘Issues of parcellation in the calculation of structure

Dissociable Physiological Biomarkers of Psychiatric Symptoms in Parkinson’s disease in the Subthalamic Nucleus: Evidence from Deep Brain Stimulation Intracranial Recordings DOI

Linbin Wang, Ying Zhao, Peng Huang

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Abstract Psychiatric symptoms in Parkinson’s disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity diverse psychiatric PD, using resting-state subthalamic nucleus (STN) local field potentials (LFPs) frontal EEG 75 PD patients undergoing deep brain stimulation (DBS). Our analysis revealed three levels of segregation: 1) Spectral: Depression was associated with increased alpha activity, apathy elevated beta theta alongside reduced beta, anxiety decreased low gamma, impulsivity gamma obsessive-compulsive disorders (OCD) delta activity. 2) Spatial: OCD localized anatomical STN, spanned both electrophysiological motor mapped STN. 3) Structural connectivity: UK Biobank analyses white matter pathways constraining STN These findings disentangle neurophysiological substrates psychiatry, identifying symptom-specific biomarkers informing targeted neuromodulation strategies.

Language: Английский

Citations

MRI signatures of cortical microstructure in human development align with oligodendrocyte cell-type expression DOI

Sila Genc, Gareth Ball, Maxime Chamberland

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 7, 2025

Language: Английский

Citations

Dynamic and Static Structure–Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease DOI

Han Wu,

Yinping Lu,

Luyao Wang

et al.

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(5)

Published: April 1, 2025

ABSTRACT The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection AD. Static SFC refers to the overall whereas dynamic transient variations. In this study, we aimed assess potential combining static with machine learning (ML) We analyzed discovery cohort an external validation cohort, including AD, mild cognitive impairment (MCI), healthy control (HC) groups. Then, quantified differences between at different stages AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier used test ability classify stages. also correlations features physiological biomarkers. increased progression, showed greater variability decreased stability. Using selected ElasticNet, GNB achieved high performance differentiating HC MCI (area under curve [AUC] = 91.1%) (AUC 89.03%). Significant were found combined use ML has strong value accurate classification significant This study demonstrates provides novel perspective understanding mechanisms contributes improving its detection.

Language: Английский

Citations

Functional Divisions of the Left Anterior and Posterior Temporoparietal Junction for Phonological and Semantic Processing in Chinese Character Reading DOI

Aqian Li,

Chuansheng Chen, Yuan Feng

et al.

NeuroImage, Journal Year: 2025, Volume and Issue: unknown, P. 121201 - 121201

Published: April 1, 2025

Language: Английский

Citations

“Personalized Brain Morphometric Feature as a Transdiagnostic Predictor of Psychopathology: Insights from Dual Systems Models” DOI

Ms. Sung-min Kang,

Kunru Song, Jialin Zhang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 19, 2025

Abstract Background Adolescents are particularly vulnerable to developing psychopathological symptoms, yet neurobiological markers that can identify these vulnerabilities in a personalized and interpretable manner remain limited. Dual Systems Models suggest this vulnerability may result from asynchronous development of neural systems subserving cognitive-control socioemotional functions. Given rigorous empirical evidence is sparse, study aimed quantify developmental imbalances evaluate their predictive value for psychopathology. Methods Based on Models, the dorsolateral prefrontal cortex (DLPFC) ventral striatum (VS) were selected as key regions two brain systems, respectively. Using longitudinal data Adolescent Brain Cognitive Development (ABCD) (baseline: n=11,238, ages 9.92±0.625 years; 2-year follow-up: n=7,870, 12.2±0.652 4-year n=2972, 14.1±0.693 years), we derived imbalance score based morphometric features, including surface area, thickness gray-matter volume. Nested linear mixed models employed assess associations between scores Additionally, generalized additive used capture trajectories explore potential non-linear with To examine reproducibility, Lifespan Human Connectome Project (HCP-D, N=652, 8-21 years) interrogated. Results The score, quantified difference VS volume DLPFC exhibited strong reliability validity. During adolescence, declining trend, decreasing growth rates variability individuals. was positively associated externalizing symptoms showed U-shaped relationship internalizing symptoms. These findings replicated HCP-D sample. Conclusions novel neuroanatomical empirically supports function transdiagnostic marker enhance understanding neurodevelopmental mechanisms underlying adolescent psychopathology offer implications precision prevention intervention strategies.

Language: Английский

Citations

Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach DOI

Xi Zhang, Lan Yang,

Jiayu Lu

et al.

Translational Psychiatry, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

Bipolar disorder (BD) is a neuropsychiatric characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture evolving processes of emotion cognition BD. Nevertheless, prior investigations dFC typically centered on larger time scales, limiting sensitivity transient changes. This study employed hidden Markov model (HMM) analysis delve deeper into moment-to-moment temporal patterns brain activity We utilized resting-state magnetic resonance imaging (rs-fMRI) data from 43 BD patients 51 controls evaluate altered dynamic spatiotemporal architecture whole-brain network identify unique activation Additionally, we investigated relationship between dynamics structural disruption through ridge regression (RR) algorithm. The results demonstrated that spent less hyperconnected state with higher efficiency lower segregation. Conversely, anticorrelated states featuring overall negative correlations, particularly among pairs default mode (DMN) sensorimotor (SMN), DMN insular-opercular ventral attention networks (ION), subcortical (SCN) SMN, as well SCN ION. Interestingly, hypoactivation cognitive control may be associated primarily situated frontal parietal lobes. mechanisms dysfunction offered fresh perspectives for exploring physiological foundation dynamics.

Language: Английский

Citations

Competitive interactions shape brain dynamics and computation across species DOI

Andrea I. Luppi, Yonatan Sanz Perl, Jakub Vohryzek

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 22, 2024

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling examine dynamical relevance of interactions mammalian connectome. Across human, macaque, mouse show that models most faithfully reproduce activity, consistently combines modular with diffuse, long-range interactions. The model outperforms cooperative-only model, excellent fit both spatial properties living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive effective connectivity produce greater levels synergistic information local-global hierarchy, lead superior capacity when used neuromorphic computing. Altogether, this work provides a mechanistic link between architecture, properties, computation brain.

Language: Английский

Citations

Issues of parcellation in the calculation of structure–function coupling DOI

Adam Turnbull, Feng Lin, Zhengwu Zhang

et al.

Nature reviews. Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 14, 2024

Language: Английский

Citations

Disrupted topologic efficiency of white matter structural connectome in migraine: a graph-based connectomics study DOI

Yanliang Mei, Dong Qiu, Zhonghua Xiong

et al.

The Journal of Headache and Pain, Journal Year: 2024, Volume and Issue: 25(1)

Published: Nov. 24, 2024

To delineate the structural connectome alterations in patients with chronic migraine (CM), episodic (EM), and healthy controls (HCs). The pathogenesis of chronification remains elusive, brain network changes potentially playing a key role. However, there is paucity research employing graph theory analysis to explore whole networks CM EM. individual 60 CM, 34 EM, 39 control participants were constructed by using deterministic diffusion-tensor tractography. Graph metrics including global efficiency, characteristic path length, local clustering coefficient, small-world parameters evaluated describe topologic organization white matter networks. Additionally, nodal coefficient efficiency considered assess regional characteristics connectome. A graph-based statistic was used properties across groups. revealed significant disruptions patients, characterized reduced increased length compared HCs. exhibited significantly lower than EM patients. Notably, group demonstrated marked reductions frontal temporal regions group. Nodal can effectively distinguish from Moreover, disrupted associated attack frequency MIDAS score after Bonferroni correction. Decreased connectivity may serve as neuroimaging marker for disease progression, providing valuable insights into pathophysiology migraine.

Language: Английский

Citations

Parallel and converging multisensory cascades in the Drosophila connectome DOI

Richard F. Betzel, Maria Grazia Puxeddu, Caio Seguin

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 8, 2024

Connectomes are network maps of synaptic connectivity. A key functional role any connectome is to constrain inter-neuronal signaling and sculpt the flow activity across nervous system. therefore play a central in rapid tranmission information about an organism’s environment from sensory neurons higher-order for action planning ultimately effectors. Here, we use parsimonious model spread investigate connectome’s shaping putative cascades. Our allows us simulate pathways sensors rest brain, mapping similarity these between different modalities identifying convergence zones–neurons that activated simultaneously by modalities. Further, considered two multisensory integration scenarios – cooperative case where interacted “speed up” (reduce) neurons’ activation times competitive “winner take all” case, streams vied same neural territory. Finally, data-driven algorithm partition into classes based on their behavior during cascade simulations. work helps underscore “simple” models enriching data, while offering classification joint connectional/dynamical properties.

Language: Английский

Citations