Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3600011
Borbála Hunyadi;Selin Aviyente
Over the past decade, two complementary approaches have been proposed to detect the community structure of high-dimensional functional connectivity networks resulting from multiple modalities, time points, frequency bands or subjects. The first approach emerged from the field of network science where multilayer community detection algorithms such as maximizing multilayer modularity or minimizing the normalized cut have been proposed. The second approach emerged from the field of signal processing where tensors have been used to model high-dimensional networks where different low-rank tensor decomposition models are employed to reveal the underlying latent factors. While both research thrusts have provided valuable insight to the topology of brain networks, the equivalencies between the two approaches have not been studied in a systematic fashion up to date. This paper reviews the major community detection approaches for unraveling the topology of multilayer functional connectivity networks from the perspective of both network science and tensor decomposition. We show mathematical equivalencies between different tensor generative models and well-known graph partitioning objective functions. Finding these equivalencies can result in computationally efficient algorithms with optimality guarantees and inform the choice of different design parameters such as the number of communities.
{"title":"Brain Connectivity: From network science to tensor models","authors":"Borbála Hunyadi;Selin Aviyente","doi":"10.1109/MSP.2025.3600011","DOIUrl":"https://doi.org/10.1109/MSP.2025.3600011","url":null,"abstract":"Over the past decade, two complementary approaches have been proposed to detect the community structure of high-dimensional functional connectivity networks resulting from multiple modalities, time points, frequency bands or subjects. The first approach emerged from the field of network science where multilayer community detection algorithms such as maximizing multilayer modularity or minimizing the normalized cut have been proposed. The second approach emerged from the field of signal processing where tensors have been used to model high-dimensional networks where different low-rank tensor decomposition models are employed to reveal the underlying latent factors. While both research thrusts have provided valuable insight to the topology of brain networks, the equivalencies between the two approaches have not been studied in a systematic fashion up to date. This paper reviews the major community detection approaches for unraveling the topology of multilayer functional connectivity networks from the perspective of both network science and tensor decomposition. We show mathematical equivalencies between different tensor generative models and well-known graph partitioning objective functions. Finding these equivalencies can result in computationally efficient algorithms with optimality guarantees and inform the choice of different design parameters such as the number of communities.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"8-24"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3613224
Vince D. Calhoun;Damien Coyle;Javier Escudero;Borbala Hunyadi;Jing Sui
{"title":"Special Issue on Accelerating Brain Discovery Through Data Science and Neurotechnology: Part 1 [From the Guest Editors]","authors":"Vince D. Calhoun;Damien Coyle;Javier Escudero;Borbala Hunyadi;Jing Sui","doi":"10.1109/MSP.2025.3613224","DOIUrl":"https://doi.org/10.1109/MSP.2025.3613224","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"5-7"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11264270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3613864
Yuntao Han;Themis Prodromakis;Shiwei Wang
Spike sorting facilitates neuroscientific research by isolating single-unit activity from extracellular recordings, enabling the study of individual neuron behavior. On-chip spike sorting systems address the data deluge challenge in high-channel-count neural probes by performing near-sensor processing directly on implanted hardware. Through hardware miniaturization, this approach reduces the need for bulky cabling and facilitates wireless data transmission, enabling chronic recordings in freely moving subjects. As neural probes scale to hundreds or thousands of channels, on-chip implementations pave the way for large-scale real-time analysis of neuronal ensembles and thus facilitate translating the technology from lab to more real-world applications.
{"title":"On-Chip Spike Sorting: Developments, challenges, and future directions","authors":"Yuntao Han;Themis Prodromakis;Shiwei Wang","doi":"10.1109/MSP.2025.3613864","DOIUrl":"https://doi.org/10.1109/MSP.2025.3613864","url":null,"abstract":"Spike sorting facilitates neuroscientific research by isolating single-unit activity from extracellular recordings, enabling the study of individual neuron behavior. On-chip spike sorting systems address the data deluge challenge in high-channel-count neural probes by performing near-sensor processing directly on implanted hardware. Through hardware miniaturization, this approach reduces the need for bulky cabling and facilitates wireless data transmission, enabling chronic recordings in freely moving subjects. As neural probes scale to hundreds or thousands of channels, on-chip implementations pave the way for large-scale real-time analysis of neuronal ensembles and thus facilitate translating the technology from lab to more real-world applications.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"103-117"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1109/msp.2025.3595319
Fei He, Yipeng Liu, Ce Zhu
{"title":"Tensor Decomposition for Brain Data Characterization: A structured review on prerequisites, models, and constraints","authors":"Fei He, Yipeng Liu, Ce Zhu","doi":"10.1109/msp.2025.3595319","DOIUrl":"https://doi.org/10.1109/msp.2025.3595319","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"26 1","pages":""},"PeriodicalIF":14.9,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3615296
Maria Giulia Preti;Dimitri Van De Ville;Enrico Amico
The 17th-century physician Marcello Malpighi observed the existence of distinctive patterns of ridges and sweat glands on fingertips, catalyzing its widespread use in forensics, but also, more generally, inspiring research to develop ways to identify individuals based on unique biological characteristics. Today, this concept has expanded vastly into diverse data, and the term biometrics has been introduced to encompass all methods of automated human recognition, such as fingerprint, face, iris, retina, and voice analysis. More recently, neuroimaging data have been explored for this purpose, giving rise to the concept of “brain fingerprints,” derived from patterns of functional networks. This perspective challenges the classical view of neuroimaging analysis, which treats individuals as repeated measures of a population-level effect, where interindividual differences are considered noise rather than signal. In contrast, intersubject variability here represents the key feature in the data allowing the unique representation and identification of an individual. This marks a paradigm shift that has sparked a wave of new interdisciplinary research, branching from neuroscience to machine learning and signal processing.
{"title":"Brain Fingerprinting: A signal processing perspective","authors":"Maria Giulia Preti;Dimitri Van De Ville;Enrico Amico","doi":"10.1109/MSP.2025.3615296","DOIUrl":"https://doi.org/10.1109/MSP.2025.3615296","url":null,"abstract":"The 17th-century physician Marcello Malpighi observed the existence of distinctive patterns of ridges and sweat glands on fingertips, catalyzing its widespread use in forensics, but also, more generally, inspiring research to develop ways to identify individuals based on unique biological characteristics. Today, this concept has expanded vastly into diverse data, and the term <italic>biometrics</i> has been introduced to encompass all methods of automated human recognition, such as fingerprint, face, iris, retina, and voice analysis. More recently, neuroimaging data have been explored for this purpose, giving rise to the concept of “brain fingerprints,” derived from patterns of functional networks. This perspective challenges the classical view of neuroimaging analysis, which treats individuals as repeated measures of a population-level effect, where interindividual differences are considered noise rather than signal. In contrast, intersubject variability here represents the key feature in the data allowing the unique representation and identification of an individual. This marks a paradigm shift that has sparked a wave of new interdisciplinary research, branching from neuroscience to machine learning and signal processing.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"91-102"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1109/MSP.2025.3624644
Tülay Adali
{"title":"On Meaningful and Multidimensional Comparisons in Our Publications And Relevance to Practice/Industry","authors":"Tülay Adali","doi":"10.1109/MSP.2025.3624644","DOIUrl":"https://doi.org/10.1109/MSP.2025.3624644","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"42 4","pages":"3-4"},"PeriodicalIF":9.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11263964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：