Brain Organoid and Systems Neuroscience Journal最新文献

{"title":"PigBET: A 2.5D deep learning segmentation framework for multimodal and longitudinal domestic pig MRI utilizing ImageNet pre-trained encoders","authors":"Zimu Li ,&nbsp;Loretta T. Sutkus ,&nbsp;Joanne E. Fil ,&nbsp;Pradeep Senthil ,&nbsp;Fan Lam ,&nbsp;Brad P. Sutton ,&nbsp;Ryan N. Dilger","doi":"10.1016/j.bosn.2025.07.001","DOIUrl":"10.1016/j.bosn.2025.07.001","url":null,"abstract":"<div><div>The domestic pig model is increasingly utilized in developmental neuroscience studies due to its anatomical and functional similarities to the human brain. Neuroimaging has become an important tool for studying pig brains. Skull Stripping is a critical but labor-intensive task for processing pig magnetic resonance imaging (MRI) data, presenting a significant hurdle in longitudinal and multimodal neuroimaging studies utilizing pig as a biomedical model. To address this, we developed PigBET, a deep learning-based skull stripping optimized for domestic pig. We stacked adjacent slices into three channels, capturing additional spatial context along the third axis. This strategy enables the use of pre-trained encoders, designed for natural images, for a 3D segmentation task. To further improve accuracy, we trained separate models for the axial, coronal, and sagittal planes, and combined their predictions using majority voting. This ensemble strategy enhances robustness and reduces orientation-specific errors. Training utilized a large quantity of 4-week-old pig magnetization-prepared rapid gradient-echo (MPRAGE) data, with transfer learning applied to 1-week-old, 8-week-old, and 18-week-old pig MPRAGE and 4-week-old pig dMRI data. PigBET achieved high segmentation accuracy on the primary test set with a mean Dice coefficient of 0.981 ± 0.004, IoU of 0.962 ± 0.008, and Hausdorff Distance (HD) of 3.4 ± 1.28 voxels. With transfer learning, it maintained robust performance across modalities and ages: Dice = 0.975–0.982, IoU = 0.952–0.965, HD = 1.7–3.5 voxels. When benchmarked against other methods, PigBET outperformed another U-Net approach without pre-trained encoders that previously showed capability of skull stripping pig data as well as a standard registration-based method combining FLIRT and ANTs. These results demonstrate that PigBET is robust and efficient and accommodates pig data from various ages and different MRI contrasts. This tool significantly advances the efficiency of MRI data processing for large-scale pig neuroimaging studies, making it a valuable resource for the biomedical field.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 180-194"},"PeriodicalIF":0.0,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional connectivity signatures in fMRI-derived connectome for the diagnosis of autism spectrum disorder","authors":"S.M. Shayez Karim,&nbsp;R.S. Rathore","doi":"10.1016/j.bosn.2025.06.004","DOIUrl":"10.1016/j.bosn.2025.06.004","url":null,"abstract":"<div><div>Rearrangement of synaptic connectivity is believed to be involved in shaping neuronal connections during brain developmental in early childhood, which determine mental health and behaviour of an individual. We here compare two types of the functional connectivity patterns: i) changes of healthy infants (3.7–32.6 months) to adults (25–35 yrs), reflecting normal developmental pattern, and ii) changes of healthy infants to autism spectrum disorder (ASD) patients (9.3–35.2 yrs), which encapsulate both developmental and ASD-specific pathological pattern. Using graph-based network parameters in the fMRI-derived connectome, we quantified changes and calculated average and median percent differences of various brain lobes. The connectome-to-connectome comparison suggests that synaptic rewiring is primarily concentrated in intra-thalamic and inter-lobe connections with the thalamus, and the ASD patients were characterised by significant thalamo-cortical hyperconnectivity. The average percent difference of the functional connectivity between ASD patients and adults for thalamus were as high as 82.58 % (degree), 72.8 % (betweenness centrality) 17.41 % (clustering coefficient) and 10.77/15.57 % (global/local efficiency). Regression models were built for normal brain development using functional connection data of healthy infants, child, adolescents and adults for each brain lobe. These regression curves suggest linearly increasing trends of functional connections from infant to adult in all brain lobes except in thalamus. Functional connections of ASD data are significantly different from this trends and characterized by significant overconnectivity. The distinct functional network signatures have the potential to serve as diagnostic markers of ASD. Towards this end, a method has been developed. The method can distinguish ASD subjects from their typically developing peers and healthy individuals with reasonable accuracy and specificity.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 170-179"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PIWI-interacting RNAs in neurological and neuropsychiatric disorders and potential for clinical development","authors":"Silvia Beatini ,&nbsp;Letizia La Rosa ,&nbsp;Lidia Giantomasi,&nbsp;Davide De Pietri Tonelli","doi":"10.1016/j.bosn.2025.06.003","DOIUrl":"10.1016/j.bosn.2025.06.003","url":null,"abstract":"<div><div>Neural diseases, including neurological and neuropsychiatric disorders, represent a significant and growing public health burden. While the understanding of the genetic origins of these diseases has advanced considerably in recent years, the underlying causal mechanisms of many sporadic cases remain poorly understood. Recent research highlights the role of noncoding RNAs, such as PIWI-interacting RNAs (piRNAs) and PIWI proteins, in regulating gene expression and maintaining genome stability within neural cells. The piRNA pathway is emerging as a significant regulator of chromatin accessibility and transcriptional homeostasis in nervous system development and plasticity. Concomitantly, a disruption of the piRNA pathway has been associated with neuronal dysfunction and age-associated neuropathologies, underscoring the urgent need to investigate their mechanistic roles in neuronal homeostasis and disease pathogenesis. This review focuses on the emerging functions of the piRNA pathway beyond the gonads, with particular attention given to its potential roles in mammalian nervous system physiology, disease mechanisms, and opportunities for diagnostic and therapeutic development.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 154-169"},"PeriodicalIF":0.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144296389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The yin and yang of neuronal mitophagy: Physiological functions and pathological implications in neurodevelopmental disorders","authors":"Christina Ploumi,&nbsp;Konstantinos Palikaras","doi":"10.1016/j.bosn.2025.06.001","DOIUrl":"10.1016/j.bosn.2025.06.001","url":null,"abstract":"<div><div>Maintenance of mitochondrial function is crucial, especially for the energy-demanding and metabolically active neuronal cells. Owing to their post-mitotic nature, neurons are highly vulnerable to mitochondrial dysfunction and therefore rely heavily on mitochondrial quality control mechanisms to mitigate damage and sustain cellular and organismal homeostasis. Mitophagy, the selective autophagic degradation of mitochondria, is the prominent mitochondrial turnover mechanism, required for supporting proper neuronal development and function. Impairment or deregulation of mitophagy pathways has been associated with a wide spectrum of neurological disorders. While mitophagy generally exerts neuroprotective effects, its excessive or uncontrolled activation can be detrimental, potentially leading to neuronal death. This review summarizes the primary pathways involved in neuronal mitophagy and explores their relevance to the etiology and pathophysiology of common neurodevelopmental disorders.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 142-153"},"PeriodicalIF":0.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetically encoded tools for Cell–Cell interactions underlying brain connectivity","authors":"Natsumi Hoshino ,&nbsp;Takeshi Yagi ,&nbsp;Takeharu Nagai ,&nbsp;Takashi Kanadome","doi":"10.1016/j.bosn.2025.06.002","DOIUrl":"10.1016/j.bosn.2025.06.002","url":null,"abstract":"<div><div>Brain development and function depend on complex yet organized neural networks and efficient communication between the neurons that form these networks. To understand the role of neural networks and communication between neurons, it is important to visualize interactions between neurons. In this review, we provide a comprehensive overview of visualization tools for cell–cell interactions, especially among neurons. We first introduce tools based on fluorescent protein technology that directly visualize cell–cell interactions, categorized into three different mechanisms: split fluorescent protein (split FP), dimerization-dependent fluorescent protein (ddFP), and Förster resonance energy transfer (FRET). Each type offers distinct advantages in terms of temporal resolution, reversibility, and spatial specificity, making them suitable for different experimental contexts within neural networks. Next, we introduce tools that indirectly visualize cell–cell interactions, based on a reporter system. Finally, we present several tools based on other mechanisms. By systematically comparing the performance and applicability of each tool, we provide a strategic framework for selecting appropriate tools based on experimental goals, ranging from short-lived synaptic interactions to long-term network connectivity studies.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 134-141"},"PeriodicalIF":0.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing artificial intelligence for transforming dementia care: Innovations in early detection and treatment","authors":"Saadeddine Habbal ,&nbsp;Maamoon Mian ,&nbsp;Musa Imam ,&nbsp;Jihane Tahiri ,&nbsp;Adam Amor ,&nbsp;P. Hemachandra Reddy","doi":"10.1016/j.bosn.2025.05.001","DOIUrl":"10.1016/j.bosn.2025.05.001","url":null,"abstract":"<div><div>Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions. This narrative review synthesizes recent literature on AI methodologies, including machine learning, deep learning, and neural networks, for their effectiveness in detecting and managing dementia. Emphasis is placed on AI’s integration of multimodal data, such as neuroimaging, genomics, and clinical records, to enhance diagnostic accuracy and predict disease progression. The review also evaluates AI-driven tools for non-invasive screening, personalized treatment planning, and patient monitoring. Findings indicate that AI significantly improves the accuracy and timeliness of dementia diagnoses, often detecting early-stage disease with greater precision than conventional methods. AI’s capacity to analyze complex datasets enables earlier interventions, which are critical for slowing the progression of AD. In the realm of treatment, AI-driven approaches are optimizing personalized care, predicting patient responses to therapies, and advancing drug discovery. The integration of AI into clinical practice is enhancing real-time decision-making and improving overall disease management. In conclusion, AI holds immense potential to transform the future of dementia care. While challenges such as ethical considerations, data privacy, and the need for widespread clinical validation remain, the benefits of AI in early detection, personalized treatment, and improved patient outcomes are substantial. Continued research and cross-disciplinary collaboration will be vital in fully realizing AI’s capabilities in addressing the global dementia epidemic.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 122-133"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural and functional covariance architecture of major depressive disorder: A meta-analytic structural equation modeling approach to primary neuroimaging analysis","authors":"Jodie P. Gray ,&nbsp;Larry R. Price ,&nbsp;Crystal Franklin ,&nbsp;Cassandra D. Leonardo ,&nbsp;Florence L. Chiang ,&nbsp;Ki Sueng Choi ,&nbsp;John Blangero ,&nbsp;David C. Glahn ,&nbsp;Helen S. Mayberg ,&nbsp;Peter T. Fox","doi":"10.1016/j.bosn.2025.04.008","DOIUrl":"10.1016/j.bosn.2025.04.008","url":null,"abstract":"<div><div>Neuroimaging studies of major depressive disorder (MDD) report widespread disease-attributed abnormalities of brain structure and function. However, reports from mass univariate-driven studies are inconsistent. The objective of this study was to determine if a neuroimaging-based biomarker of MDD, which can reliably distinguish patients from healthy controls, can be generated using multivariate measures. Multivariate modeling of MDD was achieved through generation of a meta-analytic node-and-edge network model of MDD in which disease impacted brain regions (nodes) and their covariances (edges) were quantified with structural equation modeling (SEM). SEM assessment and voxel-based morphometry (VBM) analysis in primary datasets served to test our hypothesis that multivariate analyses of MDD provide improved signal over mass univariate methods. Brain areas reliably impacted by MDD (nodes) and their covariances (edges) were informed by previously published coordinate-based meta-analysis activation/anatomical likelihood estimation (CBMA-ALE) by our group. Meta-analytic model was then fit in primary structural (T1) magnetic resonance imaging (MRI) data and resting-state functional MRI (rs-fMRI) data. Primary datasets were derived from two previously recruited cohorts. Outcome measures (testing for differences between MDD and controls) from standardized SEM included: a) model goodness of fit assessment, and b) individual edge strength. SEM measures were assessed in heterogeneous MDD patient groups, and subsequently re-tested in 7 clinical subgroups of MDD patients. Meta-analytically generated MDD network model yielded 9 nodes with 6 edges among the regions. Model goodness of fit in meta-analytic datasets were good to exceptional. Model goodness of fit in regionally sampled gray matter density in primary T1 data was exceptional in clinical subgroups of MDD, poor in clinically heterogeneous subgroups of MDD, and poor in healthy control subjects. VBM analysis of the same T1 datasets yielded sparse results. Model goodness did not distinguish MDD from controls in regionally sampled primary rs-fMRI. These findings support our hypothesis of improved multivariate signal in MDD compared to findings derived from mass univariate analyses, however this effect was only detectable in T1 data (groupwise). Improved SEM goodness of fit in clinical subgroups of MDD patients supports our hypothesis of detectable neuroimaging effects of clinical heterogeneity in MDD.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 96-106"},"PeriodicalIF":0.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast directed q-analysis for brain graphs","authors":"Felix Windisch,&nbsp;Florian Unger","doi":"10.1016/j.bosn.2025.04.004","DOIUrl":"10.1016/j.bosn.2025.04.004","url":null,"abstract":"<div><div>Recent innovations in reconstructing large scale, full-precision, neuron-synapse-level connectomes demand subsequent improvements to graph analysis methods, to keep up with the growing complexity and size of the data. One such tool is the recently introduced <em>directed</em> <span><math><mi>q</mi></math></span><em>-analysis</em>. We present numerous improvements, theoretical and applied, to this technique. On the theoretical side, we introduce modified definitions for key elements of directed <span><math><mi>q</mi></math></span>-analysis, which remedy a well-hidden and previously undetected bias. This also leads to new, beneficial perspectives to the associated computational challenges. Most importantly, we present a high-speed, publicly available, low-level implementation that provides speed-ups of several orders of magnitude on <em>C. Elegans</em>. Furthermore, the speed gains grow with the size of the considered graph. This is made possible due to the mathematical and algorithmic improvements as well as a carefully crafted implementation. These speed-ups enable, for the first time, the analysis of full-sized connectomes like those obtained by recent reconstructive methods.</div><div>Additionally, the speed-ups allow comparative analysis to corresponding null models, appropriately designed randomly structured artificial graphs that do not correspond to actual brains. This in turn, allows for assessing the efficacy and usefulness of directed <span><math><mi>q</mi></math></span>-analysis for studying the brain. We report on the results in this paper.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 115-121"},"PeriodicalIF":0.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of omics to explore novel disease pathways in Down Syndrome neurodegeneration – Focusing on integrated stress response","authors":"Beatriz Barros-Santos ,&nbsp;Carlos Campos-Marques ,&nbsp;Andreia Filipa Salvador ,&nbsp;Joana Margarida Silva","doi":"10.1016/j.bosn.2025.04.006","DOIUrl":"10.1016/j.bosn.2025.04.006","url":null,"abstract":"<div><div>Down Syndrome is characterized by the trisomy of chromosome 21, leading to widespread molecular and neurological alterations, including early-onset Alzheimer's disease. Emerging evidence highlights the role of RNA metabolism, RNA-binding proteins, and stress granules in these processes. The integrated stress response, a key regulator of translation and protein homeostasis, may be particularly disrupted in DS due to the overexpression of genes involved in the balance between protein degradation and RNA transcription. However, its impact on neurodegeneration in DS remains poorly understood. This project aims to integrate transcriptomic and proteomic data from human and animal models with Down Syndrome to dissect the interplay between integrated stress response, RNA-binding proteins, and stress granule dynamics. By identifying key molecular disruptions in RNA homeostasis and protein synthesis, we aim to investigate novel disease-driving mechanisms that can be conserved among species. These insights will likely help to establish ISR as a potential therapeutic target, advancing our understanding of DS-related neurodegenerative pathways that could be behind the age-related neurodegeneration observed in DS.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 89-95"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter subject variability analysis in normal ageing and prodromal AD","authors":"Surya Das ,&nbsp;Subha D. Puthankattil","doi":"10.1016/j.bosn.2025.04.007","DOIUrl":"10.1016/j.bosn.2025.04.007","url":null,"abstract":"<div><div>Inter subject variability examines the individual similarity/difference in neural dynamics within a population, both during rest and cognitive tasks. The current study investigates inter subject similarity using Inter Subject Correlation (<span><math><msub><mrow><mi>ISC</mi></mrow><mrow><mi>corr</mi></mrow></msub></math></span>), Inter Subject Coherence (<span><math><msub><mrow><mi>ISC</mi></mrow><mrow><mi>coh</mi></mrow></msub></math></span>) and Inter Subject Functional Connectivity (ISFC) in prodromal AD and healthy controls. Electroencephalogram (EEG) signals were recorded from 13 prodromal AD and 20 healthy subjects belonging to an ageing population. Inter subject correlation and inter subject coherence were estimated for all the EEG channels for each subject pair. Weighted phase lag index (WPLI) based functional connectivity is used for estimating ISFC. Reduction in values of <span><math><mrow><msub><mrow><mi>ISC</mi></mrow><mrow><mi>corr</mi></mrow></msub><mspace></mspace><mrow><mi>and</mi></mrow><mspace></mspace><msub><mrow><mi>ISC</mi></mrow><mrow><mi>coh</mi></mrow></msub></mrow></math></span> was observed in all the recording conditions for the patient group with respect to the healthy controls, with statistically significant difference being observed only in the resting state. ISFC analysis showed a reduction in inter subject similarity in the prodromal AD patient group in comparison to the healthy controls in both resting and cognitive tasks. Results from the study infers the reduction <span><math><mrow><mi>of</mi><mspace></mspace><msub><mrow><mi>ISC</mi></mrow><mrow><mi>corr</mi></mrow></msub><mspace></mspace><mrow><mi>and</mi></mrow><mspace></mspace><msub><mrow><mi>ISC</mi></mrow><mrow><mi>coh</mi></mrow></msub></mrow></math></span> and ISFC in the patient group indicating a reduction in intersubject similarity. The reduction in intersubject similarity observed in prodromal AD group may result from the heterogeneity of brain dynamics within the population during the progression of AD. The study further highlights that the reduction in intersubject similarity is more pronounced during resting states compared to cognitive tasks. Future studies shall benefit from examining intersubject similarity within the patient group which could aid in the development of biomarkers for early detection of the disease.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 107-114"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}