Brain Network Disorders
Volume 01 · Issue 01 · 2025
Brain Network Disorders
The launch of theBrain Network Disordersjournal marks a pivotal moment in neuroscience, addressing the urgent need to unravel the brain’s complex network functions. The brain orchestrates cognitive, emotional, and behavioral processes, yet its underlying mechanisms—especially how it transitions between health and disease—remain elusive due to the intricate connectivity of its networks.
This perspective highlights the need for a specialized publication dedicated to neuropsychiatric disorders, collectively termed "dysconnectivity syndromes." These conditions, including dementia-Alzheimer’s disease (AD), schizophrenia, and autism spectrum disorders, result from failures in interconnected physiological systems and neural circuits rather than isolated lesions. The proposedBrain Network Disorders(BND) journal aims to promote innovative paradigms for understanding these complex brain disorders by applying general systems theory and complexity sciences. The aim is to broaden the traditional conceptualization of these diseases by stressing the multifactorial roots of these disorders where the focus is on the dynamic interactions among several influences and the failure of multiple overlapping nets that drive clinical manifestations.
Neuromyelitis optica spectrum disorder (NMOSD) is a rare and devastating inflammatory autoimmune disease of the central nervous system characterized mainly by severe attacks of myelitis, optic neuritis, brainstem (including area postrema syndrome) and/or symptomatic brain syndromes. NMOSD follows a relapsing disease course, leading to severe physical disability and potential mortality. Recent advances have been made in NMOSD including the introduction of new diagnostic criteria, identification of new biomarkers and prognosis factors, better characterization of clinical aspects, and new therapeutic approaches. Magnetic resonance imaging (MRI) of the brain and spinal cord plays a crucial role in the diagnosis of NMOSD, confirmation of attacks (especially in aquaporin-4 [AQP4]-immunoglobulin G [IgG]-negative cases), and exclusion of other differential diagnoses such as multiple sclerosis. Furthermore, MRI is essential for monitoring disease activity, detecting new asymptomatic inflammatory lesions, and assessing treatment-related side effects. In this narrative review, we discussed the role of conventional MRI during diagnosis, relapses and follow-up in NMOSD based on the most relevant published data, especially considering its utility in clinical practice.
Alzheimer’s disease (AD), the most common form of dementia, is characterized by progressive cognitive decline and memory problems. With a growing elderly population and limited treatment options, AD poses a significant social and economic burden. Several modifiable risk factors for AD have been identified, including diabetes, hypertension, obesity, and dyslipidemia. These factors often occur together, making it difficult to isolate their individual effects on health. As a result, they are grouped under the term metabolic syndrome (MetS). Interestingly, there appears to be a strong association between how the body processes lipids in MetS and the development of AD. This connection likely involves inflammation, oxidative stress, blood–brain barrier dysfunction, and the processing of amyloid precursor protein. However, the exact mechanisms remain unclear. This review aims to explore the potential role of MetS-related mechanisms, with a particular focus on how dyslipidemia contributes to AD development. Additionally, we will discuss potential therapeutic avenues targeting lipid pathways to offer future treatment options.
Brain stimulation provides an alternative, reversible, and adjustable treatment option for patients with drug-resistant epilepsy (DRE). Deep brain stimulation (DBS) and responsive neural stimulation (RNS) have been applied in patients in the past decade, partly filling an essential gap for patients with DRE who are ineligible or unwilling to receive irreversible surgical treatments. Another new type of brain stimulation, chronic subthreshold cortical stimulation (CSCS), also deserves attention. In the past decade, clinical data about those therapies’ long-term safety and efficacy have been accumulated, and their clinical effectiveness has been further confirmed. Nonetheless, the therapeutic mechanisms of brain stimulation have been found far more complicated than the original design. The clinical effects of different stimulation targets of DBS and the indications for patient selection remain to be further demonstrated and clarified. There is a growing consensus that the efficacy of RNS is likely due to a long-term neuromodulatory effect on the epileptogenic network, rather than only arresting seizures. Triggering strategy (open-loop versus closed-loop) could not be seen as the essential difference between DBS and RNS in the neuromodulation mechanism for epilepsy. In addition to excitability inhibition and desynchronization for the epileptogenic zone, stimulation-induced modification of the connection and plasticity of the distributed network were identified. Further in-depth understanding of the modulatory mechanism will contribute to creating novel modulation systems and improving the efficacy of brain stimulation in the treatment of epilepsy.
Previous research has demonstrated that rats can learn to utilize a neuroprosthetic device to track infrared (IR) light sources outside their normal visual range. This device delivers IR information directly to the somatosensory cortex (S1) via intracortical microstimulation (ICMS). While S1 neurons have been shown to develop IR receptive fields, the role of the thalamus in processing this novel sensory input remains unclear. This study aimed to investigate the involvement of the thalamus in integrating IR sensory information and the emergence of IR representations within thalamocortical circuits.
Adult rats were implanted with a cortical neuroprosthesis delivering IR-driven ICMS to bilateral S1. Neural activity was simultaneously recorded from left and right hemispheres of S1 and right thalamic (ventral posterior medial [VPM], posterior medial [POM]) nuclei during an IR-guided behavioral task. IR receptive fields were characterized using information-theoretic analysis. Neural ensemble coding was evaluated using machine learning classification.
Widespread activation in response to ICMS was observed across all recorded regions. The percentage of neurons with significant mutual information between firing activity and IR stimulus features ranged from 56.2% to 74.2%. VPM ensembles demonstrated superior decoding performance for IR information compared to S1 ensembles in a support vector classification model, requiring fewer neurons to achieve similar accuracy (43.1 vs. 55.0 neurons, Tukey’s HSD, P <0.05). Importantly, animals retained the ability to process tactile information and recorded neurons were found to respond to whisker stimulation.
These findings demonstrate the rapid adaptation of thalamocortical circuits to represent a novel sensory modality and suggest a major role of the thalamus in processing the prosthetic sensation. Furthermore, these results suggest that ICMS-based neuroprostheses recruit neurons from the entire sensory pathway without compromising the endogenous sensory function.
Synchronization across neural circuits is inextricably associated with brain function and pathology. Although not largely explored, this framework can be applied to baseline anxiety and its disorder, which is characterized by aberrant levels of synchronization between the amygdala nuclei and other areas of the extended amygdala, particularly the bed nucleus of the stria terminalis (BNST) and those outside this complex. Here, we aimed to test the hypothesis that a temporally complex form of electrical stimulation (non-periodic stimulation [NPS]) of the amygdala, specifically designed to disrupt hypersynchronous activity in epilepsy, a major comorbidity of pathological anxiety, may reduce its symptoms.
Wistar rats were subjected to a physical restriction protocol model of stress to induce pathological anxiety and were assessed using the gold standard elevated plus maze (EPM) and open field (OF) tests.
In all criteria measured by the tests, NPS animals displayed reduced levels of anxiety-related symptoms, back at physiological levels.
Considering the known effects and mechanisms of NPS on epileptic phenomena, we hypothesized that the therapeutic effects were achieved by desynchronization (or normalization of synchronism levels) across brain circuits involving the amygdala, BNST, and others. Overall, past and present findings suggest that NPS may be considered as a therapeutic alternative for the treatment of anxiety disorders.
The brain is a complex dynamic system comprising multiple information-processing units. The characterization of brain systems and understanding of their organization and reconfiguration in different states remains an ongoing challenge. This study aims to explore the organization of brain complexity in Parkinson’s disease (PD).
In this study, we developed a framework to measure the complexity of each unit and explore aberrant patterns in PD. The resting-state blood oxygen level-dependent signals were converted into a state series with spatiotemporal coding of the whole brain system, and subsequently calculated the Shannon entropy of the state series (referred to as state entropy).
In a total of 152 participants (PD patients n = 73, normal controls n = 79), we revealed a descent entropy gradient from the neocortex, particularly the default mode network (DMN), to subcortical systems. Subsequent application of a two-sample t-test and multiple comparison correction revealed that the state entropy of the visual, sensorimotor, and limbic networks was significantly increased in the PD group. Additionally, we obtained the person-based similarity index (PBSI) of state entropy within each network to measure individual variability, finding that patients with PD have decreased PBSI scores on the visual, sensorimotor, and limbic networks, and increased PBSI of entropy on the DMN. Notably, when testing the classification performance of state entropy and PBSI metrics, we found that individual variability was more effective in recognizing patients with PD.
Our study reveals the organization of brain complexity across healthy and PD states, and suggests that personalized measures of brain reconfiguration have the potential to help develop clinical assistant diagnostic systems.
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