Brain-X最新文献

{"title":"Multimodal diagnostic approaches in a male patient with hydrocephalus indicated by widening of the temporal horns of the lateral ventricles: A case report","authors":"Xiuyun Liu,&nbsp;Xinyang Liu,&nbsp;Huijie Yu,&nbsp;Kuo Zhang,&nbsp;Fang Guo,&nbsp;Mingxue Li,&nbsp;Shaobo Hao,&nbsp;Dong Ming","doi":"10.1002/brx2.63","DOIUrl":"https://doi.org/10.1002/brx2.63","url":null,"abstract":"<p>Idiopathic normal pressure hydrocephalus (iNPH) is a disease caused by the accumulation of cerebrospinal fluid, leading to ventricular enlargement and manifesting as gait disorders, cognitive impairment, and urinary incontinence. The current diagnostic methods mainly rely on the patient's clinical symptoms, cerebrospinal fluid drainage response, and imaging results. A definitive diagnosis is suggested by significant symptom improvement post-drainage or when imaging shows an Evan's index (EI) &gt; 0.3. Although these diagnostic methods have been widely used for many years, misdiagnoses still occur. Therefore, multimodal approaches are crucial for accurate diagnosis and treatment in complex cases. This paper reports a case of iNPH with an EI &lt; 0.3 but with increased temporal angle width and pronounced clinical symptoms.</p><p>A 68-year-old male patient was admitted to the Tianjin Medical University General Hospital (Tianjin, China) with progressive unbalanced gait, leg weakness, urinary incontinence, and memory decline. One year ago, he underwent a head magnetic resonance imaging (MRI) at a local hospital due to trembling hands and changes in temperament; the result showed no abnormalities. Following a fall 1 month ago, he was readmitted to the same hospital for a cervical vertebra MRI examination, but no treatment was prescribed. More recently, his symptoms deteriorated, and he was admitted to our hospital for further diagnosis and treatment. He had no history of hypertension, diabetes, or coronary heart disease. On the day of his admission, the doctors arranged for an MRI, a tap test, and an infusion study due to suspected iNPH.</p><p>As shown in Figure 1, the MRI results allow for the calculation of several parameters for diagnosing iNPH, including EI, z-Evans index (z-EI), Brain/Ventricle Ratio (BVR), Corpus Callosum Angle (CA), and disproportionate enlargement of the subarachnoid space (DESH). According to current diagnostic criteria, an EI value ≥ 0.3 is an important indicator of ventricular dilation (Figure 1A).<span>¹</span> In cases where EI &lt; 0.3, a z-EI &gt; 0.42 or a BVR &lt; 1 also suggests ventricular dilation. The presence of DESH indicates a high likelihood of iNPH (Figure 1C).<span>¹</span> In addition, a CA value &lt; 90° suggests iNPH (Figure 1B).<span>²</span> In this reported case, the EI, z-EI, BVR, and CA did not meet the diagnostic criteria for iNPH, and there were no obvious DESH signs on the MRI. However, the patient's temporal horns of the lateral ventricles were significantly widened, and he demonstrated significant clinical symptoms of iNPH.</p><p>We conducted a CSF tap test (CSF-TT) and an infusion study (CSF-IT) to assess the CSF fluid circulation pathway. The CSF-TT is considered a simple, safe, and effective clinical tool for diagnosing iNPH in patients.<span>³</span> During the procedure, 30–50 mL of CSF is released through a lumbar puncture. We then evaluate whe","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.63","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why we need neurodiversity in brain and behavioral sciences","authors":"Yinghui Xia,&nbsp;Peng Wang,&nbsp;Jonathan Vincent","doi":"10.1002/brx2.70","DOIUrl":"https://doi.org/10.1002/brx2.70","url":null,"abstract":"<p>In this article, we present the case for the adoption of a neurodiversity paradigm as an essential framework within the brain and behavioral sciences. We challenge the deficit-focused medical model by advocating for the recognition of neurocognitive variances—including autism, ADHD, dyslexia, schizophrenia, and bipolar disorder—as natural representations of human diversity. We call for a shift in research and practice towards valuing neurodivergent individuals' unique strengths and contributions and promoting inclusivity and empathy. In critiquing the tendency to pathologize cognitive differences, we argue for a re-evaluation of therapeutic goals to reflect a more nuanced understanding of neurodiversity. Highlighting the socio-ethical implications of therapy-focused research, we urge an appreciation of the potential for innovation and problem-solving that neurodivergent individuals bring to society. The conclusion is a call to action for an integrated approach in research, policy, and societal attitudes that affirms neurodiversity, fostering an environment in which all forms of cognitive functioning are celebrated as part of human advancement.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The L1CAM-positive extracellular vesicle-based biomarker as a promising predictor of Parkinson's disease","authors":"Minchao Lai,&nbsp;Keying Guo,&nbsp;Yongzhi Huang,&nbsp;Dian Wang,&nbsp;Yanhong Duo,&nbsp;Junliang Yuan,&nbsp;Bowen Shu","doi":"10.1002/brx2.66","DOIUrl":"https://doi.org/10.1002/brx2.66","url":null,"abstract":"<p>Parkinson's disease (PD) is a multifaceted neurodegenerative disorder characterized by a prolonged prodromal phase followed by the onset of clinical motor symptoms. The development of reliable biomarkers for individuals at risk of developing PD during this prodromal phase is a central focus of research in the field, to enable early interventions that could potentially modify the disease progression and improve patient outcomes.<span>^{1, 2}</span></p><p>Yan et al., have made significant progress by examining the potential of serum L1CAM-positive extracellular vesicle (L1EV) associated <i>α</i>-synuclein as a biomarker for identification of at-risk individuals for developing PD.<span>³</span> Their cross-sectional study involved a cohort of 576 subjects (from the Parkinson's Progression Markers Initiative (PPMI) and a German cohort) and aimed to evaluate the efficacy of serum L1EV derived <i>α</i>-synuclein in distinguishing individuals at risk of developing PD from healthy control (HC) subjects.</p><p>The findings of this study were encouraging, revealing the potential of serum L1EV <i>α</i>-synuclein as a promising indicator for screen out the ones with high risk of developing PD. By carefully establishing a threshold for serum L1EV <i>α</i>-synuclein levels, the researchers were able to distinguish subjects with independent rapid eye movement and sleep behavior disorder (iRBD) from healthy subjects with an impressive degree of accuracy, as demonstrated by an area under the curve (AUC) value of 0.88 using receiver operating characteristic (ROC) model. Furthermore, when applying the method to a multicenter cohort, this biomarker differentiated individuals with over 80% probability of occurrence of prodromal PD from individuals with &lt;5% probability of developing prodromal PD or healthy controls, both with AUC values of 0.80. The robust predictive power of <i>α</i>-synuclein from L1EV for distinguishing high-risk subjects from healthy controls was further underscored by an AUC value of 0.90.<span>³</span></p><p>In addition, the study demonstrated that subjects with multiple prodromal markers expressed higher levels of L1EV derived <i>α</i>-synuclein, especially in those with an abnormal dopamine transporter single-photon emission computed tomography (DAT- SPECT), suggesting that the simultaneous measurement of L1EV <i>α</i>-synuclein and specific prodromal markers could improve the stratification of at-risk individuals. Remarkably, the study also found that approximately 80% of the cases that eventually phenoconverted to PD or related Lewy body diseases exhibited L1EV <i>α</i>-synuclein levels higher than the derived threshold, further emphasizing the potential utility of this biomarker to discover the individuals at the highest risk of developing PD.</p><p>In addition to its diagnostic potential, the study highlighted the positive correlation between increased L1EV <i>α</i>-synuclein levels and positive results ","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.66","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-density implantable neural electrodes and chips for massive neural recordings","authors":"Longchun Wang,&nbsp;Yanxing Suo,&nbsp;Jiahao Wang,&nbsp;Xuanqi Wang,&nbsp;Kai Xue,&nbsp;Jingjing An,&nbsp;Xun Sun,&nbsp;Qinyu Chen,&nbsp;Xiaochen Tang,&nbsp;Yang Zhao,&nbsp;Bowen Ji,&nbsp;Jingquan Liu","doi":"10.1002/brx2.65","DOIUrl":"https://doi.org/10.1002/brx2.65","url":null,"abstract":"<p>High-density neural recordings with superior spatiotemporal resolution powerfully unveil cellular-scale neural communication, showing great promise in neural science, translational medicine, and clinical applications. To achieve such, many design and fabrication innovations enhanced the electrode, chip, or both for biocompatibility improvement, electrical performance upgrade, and size miniaturization, offering several thousands of recording sites. However, an enormous gap exists along the trajectory toward billions of recording sites for brain scale resolution, posing many more design challenges. This review tries to find possible insight into mitigating the gap by discussing the latest progress in high-density electrodes and chips for neural recordings. It emphasizes the design, fabrication, bonding techniques, and in vivo performance optimization of high-density electrodes. It discusses the promising opportunities for circuit-level and architecture-level multi-channel chip design innovations. We expect that joint effort and close collaboration between high-density electrodes and chips will pave the way to high-resolution neural recording tools supporting cutting-edge neuroscience discoveries and applications.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.65","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive review of Transformer-based models in neuroscience, neurology, and psychiatry","authors":"Shan Cong,&nbsp;Hang Wang,&nbsp;Yang Zhou,&nbsp;Zheng Wang,&nbsp;Xiaohui Yao,&nbsp;Chunsheng Yang","doi":"10.1002/brx2.57","DOIUrl":"https://doi.org/10.1002/brx2.57","url":null,"abstract":"<p>This comprehensive review aims to clarify the growing impact of Transformer-based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long-range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence-centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre-trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer-based models in neuroscience, neurology, and psychiatry.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.57","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 and cognitive impairment: From evidence to SARS-CoV-2 mechanism","authors":"Haodong Pan,&nbsp;Jingyan Niu,&nbsp;Lin Feng,&nbsp;Yue Yin,&nbsp;Chun Dang,&nbsp;Yaoheng Lu,&nbsp;Lei Li,&nbsp;Jianguang Ji,&nbsp;Kuikun Yang,&nbsp;Lihua Wang,&nbsp;Qian Li","doi":"10.1002/brx2.58","DOIUrl":"https://doi.org/10.1002/brx2.58","url":null,"abstract":"<p>Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) primarily manifests as respiratory dysfunction. However, emerging evidence suggests SARS-CoV-2 can invade the brain, leading to cognitive impairment (CI). It may spread to other brain regions through transsynaptic neurons, including the olfactory, optic, and vagus nerves. Moreover, it may invade the central nervous system through blood transmission or the lymphatic system. This review summarizes the neuroimaging evidence from clinical and imaging studies of COVID-19-associated CIs, including magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomography-computed tomography. The mechanisms underlying COVID-19-associated CIs are currently being actively investigated. They include nonimmune effects, such as viral proteins, tissue hypoxia, hypercoagulability, and pathological changes in neuronal cells, and immune effects, such as microglia and astrocyte activation, peripheral immune cell infiltration, blood-brain barrier impairment, cytokine network dysregulation, and intestinal microbiota. Inflammation is the central feature. Both central and systemic inflammation may cause acute and persistent neurological changes, and existing evidence indicates that inflammation underlies the elevated risk of Alzheimer's disease. Finally, potential therapeutic options for COVID-19-associated CIs are discussed. In-depth research into the pathological mechanisms is still needed to help develop new therapies.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.58","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140556366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positron emission tomography imaging of the P2X7 receptor with a novel tracer, [18F]GSK1482160, in a transgenic mouse model of Alzheimer's disease and healthy non-human primates","authors":"Yifan Qiu,&nbsp;Lei Bi,&nbsp;Guolong Huang,&nbsp;Zhijun Li,&nbsp;Huiyi Wei,&nbsp;Guocong Li,&nbsp;Junjie Wei,&nbsp;Kai Liao,&nbsp;Min Yang,&nbsp;Peizhen Ye,&nbsp;Yongshan Liu,&nbsp;Xianxian Zhao,&nbsp;Yuyi Hou,&nbsp;Yanfang Shen,&nbsp;Renwei Zhou,&nbsp;Tuoen Liu,&nbsp;Henry Hoi Yee Tong,&nbsp;Lu Wang,&nbsp;Hongjun Jin","doi":"10.1002/brx2.55","DOIUrl":"https://doi.org/10.1002/brx2.55","url":null,"abstract":"<p>This study aimed to evaluate [¹⁸F]GSK1482160 Positron emission tomography imaging for targeting P2X7R, a biomarker for neuroinflammation. Studies of acute neuroinflammation in rodents and transgenic mice with Alzheimer's disease (AD), as well as wild-type (WT) controls, were conducted via PET-CT-MRI scans after tail vein injection of [¹⁸F]GSK1482160. Imaging was quantified based on the time-activity curve, the standardized uptake value ratio, and the binding kinetics distribution volume ratio (DVR) to assess the expression of P2X7R. Tissues were collected post-PET for immunofluorescence staining. Correlation analysis was performed between DVR and Morris water maze test results. Finally, dynamic Positron Emission Tomography-Magnetic Resonance Imaging (PET-MRI) scans were performed in healthy non-human primates (NHPs). Our study demonstrated that AD mice had a significantly higher DVR than WT mice in the hippocampus (0.92 ± 0.06 vs. 0.79 ± 0.02, <i>p</i> &lt; 0.05), cortex (1.09 ± 0.03 vs. 0.88 ± 0.04, <i>p</i> &lt; 0.05), and striatum (1.02 ± 0.10 vs. 0.83 ± 0.1, <i>p</i> &lt; 0.05). Immunofluorescence staining showed increased expression of P2X7R in the AD, along with its colocalization with activated microglia and astrocytes. Correlation analysis indicated that brain regions with higher binding of [¹⁸F]GSK1482160 (i.e., the cortex, striatum, and hippocampus) were more vulnerable to cognitive impairment. PET-MRI scans of healthy NHPs demonstrated the feasibility of brain penetration and P2X7R target engagement for the translation of [¹⁸F]GSK1482160 in human studies.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.55","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention","authors":"Jiawei Ju,&nbsp;Aberham Genetu Feleke,&nbsp;Hongqi Li,&nbsp;Haiyang Li","doi":"10.1002/brx2.56","DOIUrl":"https://doi.org/10.1002/brx2.56","url":null,"abstract":"<p>Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (<i>p</i> &lt; 0.015) and 4.69% (<i>p</i> &lt; 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.56","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140181610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-inspired intelligence-driven scientific research","authors":"Long Bai,&nbsp;Jiacan Su","doi":"10.1002/brx2.54","DOIUrl":"https://doi.org/10.1002/brx2.54","url":null,"abstract":"<p>Illustration of brain-inspired AI-driven scientific research: predicting new information, discovering novel therapies, and designing new materials.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.54","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140164438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network insights: Transforming brain science and mental health through innovative analysis","authors":"Peng Wang,&nbsp;Lulu Cheng","doi":"10.1002/brx2.53","DOIUrl":"https://doi.org/10.1002/brx2.53","url":null,"abstract":"<p>Network analysis, an interdisciplinary method rooted in graph theory and complex systems, is a promising approach for advancing our understanding of the brain's complex architecture and its implications for behavior, cognition, and mental health. Network analysis transcends the traditional psychiatric diagnostic model, which oversimplifies mental disorders by treating them as distinct physical illnesses, often creating an “epistemic prison” that fails to account for the nuanced interplay between neurological, biological, psychosocial, and cultural influences shaped by patients' life experiences.<span>¹</span> By mapping and examining the intricate network of neuronal connections and larger brain region interactions, network analysis offers deep insights into brain communication pathways, their role in cognitive function, and how their disruption may lead to neurological disorders. Despite the potential of this method, the application of network analysis in brain science is underutilized, highlighting the need for increased awareness and the development of network-based studies to fully realize its transformative potential for behavior and brain research. Therefore, we introduce an insightful behavioral exemplar to increase awareness of the potential application of network analysis in brain science.</p><p>In their landmark study, Hu et al. not only challenged the compartmentalization of psychiatric diagnoses but also provided a novel lens through which we can view mental disorders from a neurobiological perspective.<span>²</span> By employing network analysis, they illustrated that psychiatric symptoms occur in isolation but as a part of a complex network at the behavioral level, significantly resonating with a variety of human brain functions and structures. This approach underscores the centrality of the motivation and pleasure factor, which is potentially linked to the brain's reward system, and its significant impact on broader cognitive and social functioning across different psychiatric conditions. The study integrated the transdiagnostic model with sophisticated statistical methods, such as the least absolute shrinkage and selection operator, further elucidating ways to examine potential intricate brain–behavior relationships in the future.<span>³</span> Such neuroscientific insights pave the way for a more nuanced understanding of psychopathology; additionally, they can inform targeted interventions that can modulate specific neural circuits implicated in multiple psychiatric disorders.</p><p>Although network analysis was employed behaviorally in this study, it offers methodological breakthroughs for prospective neurological studies, allowing for a unified representation of complex brain functions and statistically significant control over variables of interest. It illuminates how alterations in one node can reverberate throughout the entire network, providing a level of insight traditional models have f","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.53","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140053248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}