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Deng Y, Ren S, Liu Q, Zhou D, Zhong C, Jin Y, Xie L, Gu J, Xiao C. A high heterozygosity genome assembly of Aedes albopictus enables the discovery of the association of PGANT3 with blood-feeding behavior. BMC Genomics 2024; 25:336. [PMID: 38570743 PMCID: PMC10993458 DOI: 10.1186/s12864-024-10133-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/16/2024] [Indexed: 04/05/2024] Open
Abstract
The Asian tiger mosquito, Aedes albopictus, is a global invasive species, notorious for its role in transmitting dangerous human arboviruses such as dengue and Chikungunya. Although hematophagous behavior is repulsive, it is an effective strategy for mosquitoes like Aedes albopictus to transmit viruses, posing a significant risk to human health. However, the fragmented nature of the Ae. albopictus genome assembly has been a significant challenge, hindering in-depth biological and genetic studies of this mosquito. In this research, we have harnessed a variety of technologies and implemented a novel strategy to create a significantly improved genome assembly for Ae. albopictus, designated as AealbF3. This assembly boasts a completeness rate of up to 98.1%, and the duplication rate has been minimized to 1.2%. Furthermore, the fragmented contigs or scaffolds of AealbF3 have been organized into three distinct chromosomes, an arrangement corroborated through syntenic plot analysis, which compared the genetic structure of Ae. albopictus with that of Ae. aegypti. Additionally, the study has revealed a phylogenetic relationship suggesting that the PGANT3 gene is implicated in the hematophagous behavior of Ae. albopictus. This involvement was preliminarily substantiated through RNA interference (RNAi) techniques and behavioral experiment. In summary, the AealbF3 genome assembly will facilitate new biological insights and intervention strategies for combating this formidable vector of disease. The innovative assembly process employed in this study could also serve as a valuable template for the assembly of genomes in other insects characterized by high levels of heterozygosity.
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Affiliation(s)
- Yuhua Deng
- Institute of Translational Medicine Research, The First People's Hospital of Foshan, #81, North Lingnan Avenue, Foshan, China
| | - Shuyi Ren
- Department of Pathogen Biology, Institute of Tropical Medicine, School of Public Healthy, Southern Medical University, Guangzhou, China
| | - Qiong Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, 229 Taibai North Road, Xi'an, China
| | - Dan Zhou
- Department of Breast Surgery, The First People's Hospital of Foshan, #81, North Lingnan Avenue, Foshan, China
| | - Caimei Zhong
- Department of Dermatology, Shunde District Center for Prevention and Cure of Chronic Diseases, Shunde, China
| | - Yabin Jin
- Institute of Translational Medicine Research, The First People's Hospital of Foshan, #81, North Lingnan Avenue, Foshan, China
| | - Lihua Xie
- School of Basic Medical Sciences, Fujian Medical University, No. 1 Xuefu North Road, University Town, Fuzhou, China
| | - Jinbao Gu
- Department of Pathogen Biology, Institute of Tropical Medicine, School of Public Healthy, Southern Medical University, Guangzhou, China.
| | - Chuanle Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, #7 Jinsui Road, Tianhe District, Guangzhou, China.
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Parker JE, Aristieta A, Gittis A, Rubin JE. Introducing the STReaC (Spike Train Response Classification) toolbox. J Neurosci Methods 2024; 401:110000. [PMID: 38486714 PMCID: PMC10936710 DOI: 10.1016/j.jneumeth.2023.110000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background This work presents a toolbox that implements methodology for automated classification of diverse neural responses to optogenetic stimulation or other changes in conditions, based on spike train recordings. New Method The toolbox implements what we call the Spike Train Response Classification algorithm (STReaC), which compares measurements of activity during a baseline period with analogous measurements during a subsequent period to identify various responses that might result from an event such as introduction of a sustained stimulus. The analyzed response types span a variety of patterns involving distinct time courses of increased firing, or excitation, decreased firing, or inhibition, or combinations of these. Excitation (inhibition) is identified from a comparative analysis of the spike density function (interspike interval function) for the baseline period relative to the corresponding function for the response period. Results The STReaC algorithm as implemented in this toolbox provides a user-friendly, tunable, objective methodology that can detect a variety of neuronal response types and associated subtleties. We demonstrate this with single-unit neural recordings of rodent substantia nigra pars reticulata (SNr) during optogenetic stimulation of the globus pallidus externa (GPe). Comparison with existing methods In several examples, we illustrate how the toolbox classifies responses in situations in which traditional methods (spike counting and visual inspection) either fail to detect a response or provide a false positive. Conclusions The STReaC toolbox provides a simple, efficient approach for classifying spike trains into a variety of response types defined relative to a period of baseline spiking.
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Affiliation(s)
- John E. Parker
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, U.S.A
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
| | - Asier Aristieta
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, U.S.A
| | - Aryn Gittis
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, U.S.A
| | - Jonathan E. Rubin
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, U.S.A
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
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Chang Y, Sun H, Liu S, He Y, Zhao S, Wang J, Wang T, Zhang J, Gao J, Yang Q, Li M, Zhao X. Identification of BBX gene family and its function in the regulation of microtuber formation in yam. BMC Genomics 2023; 24:354. [PMID: 37365511 DOI: 10.1186/s12864-023-09406-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
BBX proteins play important roles in all of the major light-regulated developmental processes. However, no systematic analysis of BBX gene family regarding the regulation of photoperiodic microtuber formation has been previously performed in yam. In this study, a systematic analysis on the BBX gene family was conducted in three yam species, with the results, indicating that this gene plays a role in regulating photoperiodic microtuber formation. These analyses included identification the BBX gene family in three yam species, their evolutionary relationships, conserved domains, motifs, gene structure, cis-acting elements, and expressional patterns. Based on these analyses, DoBBX2/DoCOL5 and DoBBX8/DoCOL8 showing the most opposite pattern of expression during microtuber formation were selected as candidate genes for further investigation. Gene expression analysis showed DoBBX2/DoCOL5 and DoBBX8/DoCOL8 were highest expressed in leaves and exhibited photoperiod responsive expression patterns. Besides, the overexpression of DoBBX2/DoCOL5 and DoBBX8/DoCOL8 in potato accelerated tuber formation under short-day (SD) conditions, whereas only the overexpression of DoBBX8/DoCOL8 enhanced the accelerating effect of dark conditions on tuber induction. Tuber number was increased in DoBBX8/DoCOL8 overexpressing plants under dark, as well as in DoBBX2/DoCOL5 overexpressing plants under SD. Overall, the data generated in this study may form the basis of future functional characterizations of BBX genes in yam, especially regarding their regulation of microtuber formation via the photoperiodic response pathway.
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Affiliation(s)
- Yingying Chang
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Haoyuan Sun
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Shiyu Liu
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Yulong He
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Shanshan Zhao
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Jiage Wang
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
| | - Tianle Wang
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
- Engineering Technology Research Center of Nursing and Utilization of Genuine Chinese Crude Drugs in Henan Province / Engineering Laboratory of Green Medicinal Material Biotechnology of Henan Province, Xinxiang, 453007, China
| | - Jiangli Zhang
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
- Engineering Technology Research Center of Nursing and Utilization of Genuine Chinese Crude Drugs in Henan Province / Engineering Laboratory of Green Medicinal Material Biotechnology of Henan Province, Xinxiang, 453007, China
| | - Jin Gao
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
- Henan International Joint Laboratory of Agricultural Microbial Ecology and Technology, Xinxiang, 453007, China
| | - Qingxiang Yang
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China
- Henan International Joint Laboratory of Agricultural Microbial Ecology and Technology, Xinxiang, 453007, China
| | - Mingjun Li
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China.
- Engineering Technology Research Center of Nursing and Utilization of Genuine Chinese Crude Drugs in Henan Province / Engineering Laboratory of Green Medicinal Material Biotechnology of Henan Province, Xinxiang, 453007, China.
| | - Xiting Zhao
- College of Life Sciences, Henan Normal University, Xinxiang, 453007, China.
- Engineering Technology Research Center of Nursing and Utilization of Genuine Chinese Crude Drugs in Henan Province / Engineering Laboratory of Green Medicinal Material Biotechnology of Henan Province, Xinxiang, 453007, China.
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Sun Y, Lu J, Yang J, Liu Y, Liu L, Zeng F, Niu Y, Dong L, Yang F. Construction of a caries diagnosis model based on microbiome novelty score. HUA XI KOU QIANG YI XUE ZA ZHI = HUAXI KOUQIANG YIXUE ZAZHI = WEST CHINA JOURNAL OF STOMATOLOGY 2023; 41:208-217. [PMID: 37056188 PMCID: PMC10427253 DOI: 10.7518/hxkq.2023.2022301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 12/30/2022] [Indexed: 04/15/2023]
Abstract
OBJECTIVES This study aimed to analyze the bacteria in dental caries and establish an optimized dental-ca-ries diagnosis model based on 16S ribosomal RNA (rRNA) data of oral flora. METHODS We searched the public databa-ses of microbiomes including NCBI, MG-RAST, EMBL-EBI, and QIITA and collected data involved in the relevant research on human oral microbiomes worldwide. The samples in the caries dataset (1 703) were compared with healthy ones (20 540) by using the microbial search engine (MSE) to obtain the microbiome novelty score (MNS) and construct a caries diagnosis model based on this index. Nonparametric multivariate ANOVA was used to analyze and compare the impact of different host factors on the oral flora MNS, and the model was optimized by controlling related factors. Finally, the effect of the model was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS 1) The oral microbiota distribution obviously differed among people with various oral-health statuses, and the species richness and species diversity index decreased. 2) ROC curve was used to evaluate the caries data set, and the area under ROC curve was AUC=0.67. 3) Among the five hosts' factors including caries status, country, age, decayed missing filled tooth (DMFT) indices, and sampling site displayed the strongest effect on MNS of samples (P=0.001). 4) The AUC of the model was 0.87, 0.74, 0.74, and 0.75 in high caries, medium caries, low caries samples in Chinese children, and mixed dental plaque samples after controlling host factors, respectively. CONCLUSIONS The model based on the analysis of 16S rRNA data of oral flora had good diagnostic efficiency.
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Affiliation(s)
- Yanfei Sun
- School of Stomatology, Qingdao University, Qingdao 266003, China
- Dept. of Pediatric Dentistry, Center of Stomatology, Municipal Hospital, Qingdao 266071, China
| | - Jie Lu
- Dept. of Stomatology, Pujiang Stomatological Hospital, Jinhua 322299, China
| | - Jiazhen Yang
- Dept. of Pediatric Dentistry, Stomatological Hospital of Qingdao, Qingdao 266000, China
| | - Yuhan Liu
- Central Laboratory, Stomatological Hospital of Qing-dao, Qingdao 266000, China
| | - Lu Liu
- Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
| | - Fei Zeng
- Dept. of Stomatology, Affiliated Hospital of Jining Medical University, Jining 272000, China
| | - Yufen Niu
- Dept. of Pediatric Dentistry, Center of Stomatology, Municipal Hospital, Qingdao 266071, China
- School of Stomatology, Dalian Medical University, Dalian 116044, China
| | - Lei Dong
- Dept. of Pediatric Dentistry, Center of Stomatology, Municipal Hospital, Qingdao 266071, China
- School of Stomatology, Dalian Medical University, Dalian 116044, China
| | - Fang Yang
- School of Stomatology, Qingdao University, Qingdao 266003, China
- Dept. of Pediatric Dentistry, Center of Stomatology, Municipal Hospital, Qingdao 266071, China
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Zhang P, Liu J, Jia N, Wang M, Lu Y, Wang D, Zhang J, Zhang H, Wang X. Genome-wide identification and characterization of the bZIP gene family and their function in starch accumulation in Chinese chestnut ( Castanea mollissima Blume). FRONTIERS IN PLANT SCIENCE 2023; 14:1166717. [PMID: 37077628 PMCID: PMC10106562 DOI: 10.3389/fpls.2023.1166717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/21/2023] [Indexed: 05/03/2023]
Abstract
The transcription factors of basic leucine zipper (bZIP) family genes play significant roles in stress response as well as growth and development in plants. However, little is known about the bZIP gene family in Chinese chestnut (Castanea mollissima Blume). To better understand the characteristics of bZIPs in chestnut and their function in starch accumulation, a series of analyses were performed including phylogenetic, synteny, co-expression and yeast one-hybrid analyses. Totally, we identified 59 bZIP genes that were unevenly distributed in the chestnut genome and named them CmbZIP01 to CmbZIP59. These CmbZIPs were clustered into 13 clades with clade-specific motifs and structures. A synteny analysis revealed that segmental duplication was the major driving force of expansion of the CmbZIP gene family. A total of 41 CmbZIP genes had syntenic relationships with four other species. The results from the co-expression analyses indicated that seven CmbZIPs in three key modules may be important in regulating starch accumulation in chestnut seeds. Yeast one-hybrid assays showed that transcription factors CmbZIP13 and CmbZIP35 might participate in starch accumulation in the chestnut seed by binding to the promoters of CmISA2 and CmSBE1_2, respectively. Our study provided basic information on CmbZIP genes, which can be utilized in future functional analysis and breeding studies.
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Affiliation(s)
- Penglong Zhang
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
| | - Jing Liu
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
| | - Nan Jia
- Changli Institute of Pomology, Hebei Academy of Agriculture and Forestry Science, Changli, Hebei, China
| | - Meng Wang
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
| | - Yi Lu
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
| | - Dongsheng Wang
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
| | - Jingzheng Zhang
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
| | - Haie Zhang
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
| | - Xuan Wang
- Engineering Research Center of Chestnut Industry Technology, Ministry of Education, Qinhuangdao, Hebei, China
- Hebei Key Laboratory of Horticultural Germplasm Excavation and Innovative Utilization, College of Horticulture Science and Technology, Hebei Normal University of Science and Technology, Changli, Hebei, China
- *Correspondence: Xuan Wang,
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Mayor D, Panday D, Kandel HK, Steffert T, Banks D. CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals. ENTROPY 2021; 23:e23030321. [PMID: 33800469 PMCID: PMC7998823 DOI: 10.3390/e23030321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.
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Affiliation(s)
- David Mayor
- School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK
- Correspondence:
| | - Deepak Panday
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Hari Kala Kandel
- Department of Computing, Goldsmiths College, University of London, New Cross, London SE14 6NW, UK;
| | - Tony Steffert
- MindSpire, Napier House, 14-16 Mount Ephraim Rd, Tunbridge Wells TN1 1EE, UK;
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
| | - Duncan Banks
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
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Buccino AP, Hurwitz CL, Garcia S, Magland J, Siegle JH, Hurwitz R, Hennig MH. SpikeInterface, a unified framework for spike sorting. eLife 2020; 9:e61834. [PMID: 33170122 PMCID: PMC7704107 DOI: 10.7554/elife.61834] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/09/2020] [Indexed: 12/21/2022] Open
Abstract
Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.
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Affiliation(s)
- Alessio P Buccino
- Department of Biosystems Science and Engineering, ETH ZurichZürichSwitzerland
- Centre for Integrative Neuroplasticity (CINPLA), University of OsloOsloNorway
| | - Cole L Hurwitz
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRSLyonFrance
| | | | | | | | - Matthias H Hennig
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
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Spikes and Nets (S&N): A New Fast, Parallel Computing, Point Process Software for Multineuronal Discharge and Connectivity Analysis. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10242-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Li Y, Wang G, Tan X, Ouyang J, Zhang M, Song X, Liu Q, Leng Q, Chen L, Xie L. ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection. BMC Med Genomics 2020; 13:52. [PMID: 32241270 PMCID: PMC7118832 DOI: 10.1186/s12920-020-0683-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data. RESULTS Here we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only. CONCLUSIONS The pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.
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Affiliation(s)
- Yuyu Li
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture; College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai, 201306, China.,Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China
| | - Guangzhi Wang
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture; College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai, 201306, China.,Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China
| | - Xiaoxiu Tan
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China
| | - Jian Ouyang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China
| | - Menghuan Zhang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China
| | - Xiaofeng Song
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Qi Liu
- Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 20009, China
| | - Qibin Leng
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, 78 Heng Zhi Gang, Lu Hu Road, Guangzhou, 510095, China
| | - Lanming Chen
- Key Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture; College of Food Science and Technology, Shanghai Ocean University, 999 Hu Cheng Huan Road, Shanghai, 201306, China.
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai, 201203, China.
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Unakafova VA, Gail A. Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data. Front Neuroinform 2019; 13:57. [PMID: 31417389 PMCID: PMC6682703 DOI: 10.3389/fninf.2019.00057] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 07/11/2019] [Indexed: 11/13/2022] Open
Abstract
Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.
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Affiliation(s)
| | - Alexander Gail
- Cognitive Neurosciences Laboratory, German Primate Center, Göttingen, Germany
- Primate Cognition, Göttingen, Germany
- Georg-Elias-Mueller-Institute of Psychology, University of Goettingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Göttingen, Germany
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Abstract
Despite a multitude of commercially available multi-electrode array (MEA) systems that are each capable of rapid data acquisition from cultured neurons or slice cultures, there is a general lack of available analysis tools. These analysis gaps restrict the efficient extraction of meaningful physiological features from data sets, and limit interpretation of how experimental manipulations modify neural network activity. Here, we present the development of a user-friendly, publicly-available software called MEAnalyzer. This software contains several spike train analysis methods including relevant statistical calculations, periodicity analysis, functional connectivity analysis, and advanced data visualizations in a user-friendly graphical user interface that requires no coding from the user. Widespread availability of this user friendly and mathematically advanced program will stimulate and enhance the use of MEA technologies.
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Mahmud M, Vassanelli S. Open-Source Tools for Processing and Analysis of In Vitro Extracellular Neuronal Signals. ADVANCES IN NEUROBIOLOGY 2019; 22:233-250. [PMID: 31073939 DOI: 10.1007/978-3-030-11135-9_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The recent years have seen unprecedented growth in the manufacturing of neurotechnological tools. The latest technological advancements presented the neuroscientific community with neuronal probes containing thousands of recording sites. These next-generation probes are capable of simultaneously recording neuronal signals from a large number of channels. Numerically, a simple 128-channel neuronal data acquisition system equipped with a 16 bits A/D converter digitizing the acquired analog waveforms at a sampling frequency of 20 kHz will generate approximately 17 GB uncompressed data per hour. Today's biggest challenge is to mine this staggering amount of data and find useful information which can later be used in decoding brain functions, diagnosing diseases, and devising treatments. To this goal, many automated processing and analysis tools have been developed and reported in the literature. A good amount of them are also available as open source for others to adapt them to individual needs. Focusing on extracellularly recorded neuronal signals in vitro, this chapter provides an overview of the popular open-source tools applicable on these signals for spike trains and local field potentials analysis, and spike sorting. Towards the end, several future research directions have also been outlined.
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Affiliation(s)
- Mufti Mahmud
- Computing and Technology, School of Science and Technology, Nottingham Trent University, Nottingham, UK.
| | - Stefano Vassanelli
- NeuroChip Lab, Department of Biomedical Sciences, University of Padova, Padova, Italy
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13
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Timme NM, Lapish C. A Tutorial for Information Theory in Neuroscience. eNeuro 2018; 5:ENEURO.0052-18.2018. [PMID: 30211307 PMCID: PMC6131830 DOI: 10.1523/eneuro.0052-18.2018] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 04/10/2018] [Accepted: 05/30/2018] [Indexed: 11/21/2022] Open
Abstract
Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.
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Affiliation(s)
- Nicholas M Timme
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, Indianapolis, IN 46202
| | - Christopher Lapish
- Department of Psychology, Indiana University - Purdue University Indianapolis, 402 N. Blackford St, Indianapolis, IN 46202
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14
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Zhang B, Dai J, Zhang T. NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis. Biomed Eng Online 2017; 16:129. [PMID: 29132360 PMCID: PMC5683334 DOI: 10.1186/s12938-017-0419-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 11/03/2017] [Indexed: 02/07/2023] Open
Abstract
Background In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful. Results Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level. Conclusions With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments. Electronic supplementary material The online version of this article (10.1186/s12938-017-0419-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Zhang
- State Key Laboratory of Brain and Cognitive Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ji Dai
- State Key Laboratory of Brain and Cognitive Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. .,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China. .,Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, CAS Center for Excellence in Brain Science and Intelligence Technology, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, 518055, China.
| | - Tao Zhang
- State Key Laboratory of Brain and Cognitive Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
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15
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Cessac B, Kornprobst P, Kraria S, Nasser H, Pamplona D, Portelli G, Viéville T. PRANAS: A New Platform for Retinal Analysis and Simulation. Front Neuroinform 2017; 11:49. [PMID: 28919854 PMCID: PMC5585572 DOI: 10.3389/fninf.2017.00049] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 07/17/2017] [Indexed: 01/28/2023] Open
Abstract
The retina encodes visual scenes by trains of action potentials that are sent to the brain via the optic nerve. In this paper, we describe a new free access user-end software allowing to better understand this coding. It is called PRANAS (https://pranas.inria.fr), standing for Platform for Retinal ANalysis And Simulation. PRANAS targets neuroscientists and modelers by providing a unique set of retina-related tools. PRANAS integrates a retina simulator allowing large scale simulations while keeping a strong biological plausibility and a toolbox for the analysis of spike train population statistics. The statistical method (entropy maximization under constraints) takes into account both spatial and temporal correlations as constraints, allowing to analyze the effects of memory on statistics. PRANAS also integrates a tool computing and representing in 3D (time-space) receptive fields. All these tools are accessible through a friendly graphical user interface. The most CPU-costly of them have been implemented to run in parallel.
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Affiliation(s)
- Bruno Cessac
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
| | - Pierre Kornprobst
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
| | - Selim Kraria
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
| | - Hassan Nasser
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
| | - Daniela Pamplona
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
| | - Geoffrey Portelli
- Biovision Team, Inria, Université Côte d'AzurSophia Antipolis, France
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Huang C, Resnik A, Celikel T, Englitz B. Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding. PLoS Comput Biol 2016; 12:e1004984. [PMID: 27304526 PMCID: PMC4909286 DOI: 10.1371/journal.pcbi.1004984] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 05/16/2016] [Indexed: 01/29/2023] Open
Abstract
Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. A neuron is a tiny computer that transforms electrical inputs into electrical outputs. While neurons have been investigated and modeled for many decades, some aspects remain elusive. Recently, it was demonstrated that the membrane (voltage) state of a neuron determines its threshold to spiking. In the present study we asked, what are the consequences of this dependence for the computation the neuron performs. We find that this so called adaptive threshold allows neurons to be more focused on inputs which arrive close in time with other inputs. Also, it allows neurons to represent their information more robustly, such that a readout of their activity is less influenced by the state the brain is in. The present use of information theory provides a solid foundation for these results. We obtained the results primarily in detailed simulations, but performed neural recordings to verify these properties in real neurons. In summary, an adaptive spiking threshold allows neurons to specifically compute robustly with a focus on tight temporal correlations in their input.
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Affiliation(s)
- Chao Huang
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Laboratory of Neural Circuits and Plasticity, University of Southern California, Los Angeles, California, United States of America
| | - Andrey Resnik
- Laboratory of Neural Circuits and Plasticity, University of Southern California, Los Angeles, California, United States of America
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- * E-mail: (BE); (TC)
| | - Bernhard Englitz
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- * E-mail: (BE); (TC)
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17
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Mahmud M, Vassanelli S. Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges. Front Neurosci 2016; 10:248. [PMID: 27313507 PMCID: PMC4889584 DOI: 10.3389/fnins.2016.00248] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/19/2016] [Indexed: 12/02/2022] Open
Abstract
In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.
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Affiliation(s)
- Mufti Mahmud
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova Padova, Italy
| | - Stefano Vassanelli
- NeuroChip Laboratory, Department of Biomedical Sciences, University of Padova Padova, Italy
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18
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Fukushima M, Rauske PL, Margoliash D. Temporal and rate code analysis of responses to low-frequency components in the bird's own song by song system neurons. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2015; 201:1103-14. [PMID: 26319311 DOI: 10.1007/s00359-015-1037-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 07/17/2015] [Accepted: 08/05/2015] [Indexed: 10/23/2022]
Abstract
Auditory feedback (AF) plays a critical role in vocal learning. Previous studies in songbirds suggest that low-frequency (<~1 kHz) components may be salient cues in AF. We explored this with auditory stimuli including the bird's own song (BOS) and BOS variants with increased relative power at low frequencies (LBOS). We recorded single units from BOS-selective neurons in two forebrain nuclei (HVC and Area X) in anesthetized zebra finches. Song-evoked responses were analyzed based on both rate (spike counts) and temporal coding of spike trains. The BOS and LBOS tended to evoke similar spike-count responses in substantially overlapping populations of neurons in both HVC and Area X. Analysis of spike patterns demonstrated temporal coding information that discriminated among the BOS and LBOS stimuli significantly better than spike counts in the majority of HVC (94 %) and Area X (85 %) neurons. HVC neurons contained more and a broader range of temporal coding information to discriminate among the stimuli than Area X neurons. These results are consistent with a role of spike timing in coding differences in the spectral components of BOS in HVC and Area X neurons.
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Affiliation(s)
- Makoto Fukushima
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA.
| | - Peter L Rauske
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, 60637, USA
| | - Daniel Margoliash
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA.,Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, 60637, USA
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19
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High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons. PLoS Comput Biol 2015; 11:e1004121. [PMID: 26098109 PMCID: PMC4476555 DOI: 10.1371/journal.pcbi.1004121] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 01/11/2015] [Indexed: 01/28/2023] Open
Abstract
The manner in which populations of inhibitory (INH) and excitatory (EXC) neocortical neurons collectively encode stimulus-related information is a fundamental, yet still unresolved question. Here we address this question by simultaneously recording with large-scale multi-electrode arrays (of up to 128 channels) the activity of cell ensembles (of up to 74 neurons) distributed along all layers of 3–4 neighboring cortical columns in the anesthetized adult rat somatosensory barrel cortex in vivo. Using two different whisker stimulus modalities (location and frequency) we show that individual INH neurons – classified as such according to their distinct extracellular spike waveforms – discriminate better between restricted sets of stimuli (≤6 stimulus classes) than EXC neurons in granular and infra-granular layers. We also demonstrate that ensembles of INH cells jointly provide as much information about such stimuli as comparable ensembles containing the ~20% most informative EXC neurons, however presenting less information redundancy – a result which was consistent when applying both theoretical information measurements and linear discriminant analysis classifiers. These results suggest that a consortium of INH neurons dominates the information conveyed to the neocortical network, thereby efficiently processing incoming sensory activity. This conclusion extends our view on the role of the inhibitory system to orchestrate cortical activity. Perception of the environment relies on neuronal computation in the cerebral cortex. However, the exact algorithms by which cortical neuronal networks process relevant information from the inputs of sensory organs are only poorly understood. To address this problem we stimulated distinct whiskers and recorded the neuronal responses from identified cortical whisker representations of the rat using multi-site electrodes. For rodents the whisker system is one main sensory input channel, offering the unique property that for each whisker an identified cortical area ("barrel-related column") represents its main cortical input station. In the present study we were able to demonstrate that the action potential firing of single inhibitory neurons provides more information about behaviorally relevant qualities of whisker stimulation (identity of the stimulated whisker and frequency of stimulation) than excitatory neurons. In addition, information about stimulation qualities was encoded with less redundancy in inhibitory neurons. In summary, the results of our study suggest that inhibitory neurons carry substantial information about the sensory environment and can thereby adequately orchestrate neuronal activity in sensory cortices.
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20
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The neural representation of interaural time differences in gerbils is transformed from midbrain to cortex. J Neurosci 2015; 34:16796-808. [PMID: 25505332 DOI: 10.1523/jneurosci.2432-14.2014] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Interaural time differences (ITDs) are the dominant cue for the localization of low-frequency sounds. While much is known about the processing of ITDs in the auditory brainstem and midbrain, there have been relatively few studies of ITD processing in auditory cortex. In this study, we compared the neural representation of ITDs in the inferior colliculus (IC) and primary auditory cortex (A1) of gerbils. Our IC results were largely consistent with previous studies, with most cells responding maximally to ITDs that correspond to the contralateral edge of the physiological range. In A1, however, we found that preferred ITDs were distributed evenly throughout the physiological range without any contralateral bias. This difference in the distribution of preferred ITDs in IC and A1 had a major impact on the coding of ITDs at the population level: while a labeled-line decoder that considered the tuning of individual cells performed well on both IC and A1 responses, a two-channel decoder based on the overall activity in each hemisphere performed poorly on A1 responses relative to either labeled-line decoding of A1 responses or two-channel decoding of IC responses. These results suggest that the neural representation of ITDs in gerbils is transformed from IC to A1 and have important implications for how spatial location may be combined with other acoustic features for the analysis of complex auditory scenes.
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21
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Assecondi S, Ostwald D, Bagshaw AP. Reliability of information-based integration of EEG and fMRI data: a simulation study. Neural Comput 2014; 27:281-305. [PMID: 25514112 DOI: 10.1162/neco_a_00695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.
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Affiliation(s)
- Sara Assecondi
- School of Psychology, University of Birmingham, Birmingham, B17 2TT, U.K.
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22
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Paprocki B, Szczepanski J. Transmission efficiency in ring, brain inspired neuronal networks. Information and energetic aspects. Brain Res 2013; 1536:135-43. [PMID: 23891793 DOI: 10.1016/j.brainres.2013.07.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Revised: 07/15/2013] [Accepted: 07/16/2013] [Indexed: 11/19/2022]
Abstract
Organisms often evolve as compromises, and many of these compromises can be expressed in terms of energy efficiency. Thus, many authors analyze energetic costs processes during information transmission in the brain. In this paper we study information transmission rate per energy used in a class of ring, brain inspired neural networks, which we assume to involve components like excitatory and inhibitory neurons or long-range connections. Choosing model of neuron we followed a probabilistic approach proposed by Levy and Baxter (2002), which contains all essential qualitative mechanisms participating in the transmission process and provides results consistent with physiologically observed values. Our research shows that all network components, in broad range of conditions, significantly improve the information-energetic efficiency. It turned out that inhibitory neurons can improve the information-energetic transmission efficiency by 50%, while long-range connections can improve the efficiency even by 70%. We also found that the most effective is the network with the smallest size: we observed that two times increase of the size can cause even three times decrease of the information-energetic efficiency. This article is part of a Special Issue entitled Neural Coding 2012.
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Affiliation(s)
- Bartosz Paprocki
- Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Kopernika 1, Poland.
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23
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Paprocki B, Szczepanski J. How do the amplitude fluctuations affect the neuronal transmission efficiency. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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24
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Cajigas I, Malik WQ, Brown EN. nSTAT: open-source neural spike train analysis toolbox for Matlab. J Neurosci Methods 2012; 211:245-64. [PMID: 22981419 PMCID: PMC3491120 DOI: 10.1016/j.jneumeth.2012.08.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2012] [Revised: 08/06/2012] [Accepted: 08/07/2012] [Indexed: 11/23/2022]
Abstract
Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT--an open source neural spike train analysis toolbox for Matlab®. By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems.
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Affiliation(s)
- I Cajigas
- Department of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
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25
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Abstract
It is becoming increasingly clear that the brain processes sensory stimuli differently according to whether they are passively or actively acquired, and these differences can be seen early in the sensory pathway. In the nucleus of the solitary tract (NTS), the first relay in the central gustatory neuraxis, a rich variety of sensory inputs generated by active licking converge. Here, we show that taste responses in the NTS reflect these interactions. Experiments consisted of recordings of taste-related activity in the NTS of awake rats as they freely licked exemplars of the five basic taste qualities (sweet, sour, salty, bitter, umami). Nearly all taste-responsive cells were broadly tuned across taste qualities. A subset responded to taste with long latencies (>1.0 s), suggesting the activation of extraoral chemoreceptors. Analyses of the temporal characteristics of taste responses showed that spike timing conveyed significantly more information than spike count alone in almost one-half of NTS cells, as in anesthetized rats, but with less information per cell. In addition to taste-responsive cells, the NTS contains cells that synchronize with licks. Since the lick pattern per se can convey information, these cells may collaborate with taste-responsive cells to identify taste quality. Other cells become silent during licking. These latter "antilick" cells show a surge in firing rate predicting the beginning and signaling the end of a lick bout. Collectively, the data reveal a complex array of cell types in the NTS, only a portion of which include taste-responsive cells, which work together to acquire sensory information.
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26
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Abstract
It is becoming increasingly clear that the brain processes sensory stimuli differently according to whether they are passively or actively acquired, and these differences can be seen early in the sensory pathway. In the nucleus of the solitary tract (NTS), the first relay in the central gustatory neuraxis, a rich variety of sensory inputs generated by active licking converge. Here, we show that taste responses in the NTS reflect these interactions. Experiments consisted of recordings of taste-related activity in the NTS of awake rats as they freely licked exemplars of the five basic taste qualities (sweet, sour, salty, bitter, umami). Nearly all taste-responsive cells were broadly tuned across taste qualities. A subset responded to taste with long latencies (>1.0 s), suggesting the activation of extraoral chemoreceptors. Analyses of the temporal characteristics of taste responses showed that spike timing conveyed significantly more information than spike count alone in almost one-half of NTS cells, as in anesthetized rats, but with less information per cell. In addition to taste-responsive cells, the NTS contains cells that synchronize with licks. Since the lick pattern per se can convey information, these cells may collaborate with taste-responsive cells to identify taste quality. Other cells become silent during licking. These latter "antilick" cells show a surge in firing rate predicting the beginning and signaling the end of a lick bout. Collectively, the data reveal a complex array of cell types in the NTS, only a portion of which include taste-responsive cells, which work together to acquire sensory information.
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27
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Mahmud M, Bertoldo A, Girardi S, Maschietto M, Vassanelli S. SigMate: a Matlab-based automated tool for extracellular neuronal signal processing and analysis. J Neurosci Methods 2012; 207:97-112. [PMID: 22513383 DOI: 10.1016/j.jneumeth.2012.03.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Revised: 03/20/2012] [Accepted: 03/21/2012] [Indexed: 11/19/2022]
Abstract
Rapid advances in neuronal probe technology for multisite recording of brain activity have posed a significant challenge to neuroscientists for processing and analyzing the recorded signals. To be able to infer meaningful conclusions quickly and accurately from large datasets, automated and sophisticated signal processing and analysis tools are required. This paper presents a Matlab-based novel tool, "SigMate", incorporating standard methods to analyze spikes and EEG signals, and in-house solutions for local field potentials (LFPs) analysis. Available modules at present are - 1. In-house developed algorithms for: data display (2D and 3D), file operations (file splitting, file concatenation, and file column rearranging), baseline correction, slow stimulus artifact removal, noise characterization and signal quality assessment, current source density (CSD) analysis, latency estimation from LFPs and CSDs, determination of cortical layer activation order using LFPs and CSDs, and single LFP clustering; 2. Existing modules: spike detection, sorting and spike train analysis, and EEG signal analysis. SigMate has the flexibility of analyzing multichannel signals as well as signals from multiple recording sources. The in-house developed tools for LFP analysis have been extensively tested with signals recorded using standard extracellular recording electrode, and planar and implantable multi transistor array (MTA) based neural probes. SigMate will be disseminated shortly to the neuroscience community under the open-source GNU-General Public License.
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Affiliation(s)
- Mufti Mahmud
- NeuroChip Laboratory, Department of Human Anatomy and Physiology, University of Padova, via f. Marzolo 3, 35131 Padova, Italy
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Aldworth ZN, Bender JA, Miller JP. Information transmission in cercal giant interneurons is unaffected by axonal conduction noise. PLoS One 2012; 7:e30115. [PMID: 22253900 PMCID: PMC3257269 DOI: 10.1371/journal.pone.0030115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Accepted: 12/09/2011] [Indexed: 11/19/2022] Open
Abstract
What are the fundamental constraints on the precision and accuracy with which nervous systems can process information? One constraint must reflect the intrinsic “noisiness” of the mechanisms that transmit information between nerve cells. Most neurons transmit information through the probabilistic generation and propagation of spikes along axons, and recent modeling studies suggest that noise from spike propagation might pose a significant constraint on the rate at which information could be transmitted between neurons. However, the magnitude and functional significance of this noise source in actual cells remains poorly understood. We measured variability in conduction time along the axons of identified neurons in the cercal sensory system of the cricket Acheta domesticus, and used information theory to calculate the effects of this variability on sensory coding. We found that the variability in spike propagation speed is not large enough to constrain the accuracy of neural encoding in this system.
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Affiliation(s)
- Zane N. Aldworth
- Center for Computational Biology, Montana State University, Bozeman, Montana, United States of America
- * E-mail:
| | - John A. Bender
- Center for Computational Biology, Montana State University, Bozeman, Montana, United States of America
| | - John P. Miller
- Center for Computational Biology, Montana State University, Bozeman, Montana, United States of America
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Gardner D, Banfelder J, Jagdale AB, Victor JD. Towards massively-parallel analytic capabilities for multielectrode recordings. BMC Neurosci 2011. [PMCID: PMC3240480 DOI: 10.1186/1471-2202-12-s1-p361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
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Macke J, Berens P, Bethge M. Statistical analysis of multi-cell recordings: linking population coding models to experimental data. Front Comput Neurosci 2011; 5:35. [PMID: 21847379 PMCID: PMC3147152 DOI: 10.3389/fncom.2011.00035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2011] [Accepted: 07/14/2011] [Indexed: 11/23/2022] Open
Affiliation(s)
- Jakob Macke
- Computational Vision and Neuroscience Group, Max Planck Institute for Biological Cybernetics Tübingen, Germany
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Beste C, Otto T, Hoffmann S. The biopsychology-nonlinear analysis toolbox: a free, open-source Matlab-toolbox for the non-linear analysis of time series data. Neuroinformatics 2011; 8:197-200. [PMID: 20532677 DOI: 10.1007/s12021-010-9075-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
We provide a free, open-source toolbox for non-linear time series analyses. The major goal of this project was to provide a toolbox for nonlinear time series analyses that is easily accessible to a wide range of neuroscientists. The toolbox offers modular, powerful and flexible algorithms embedded in an easy to handle graphical user interface (GUI). The toolbox can be run within the Matlab environment, but also as stand-alone solution without reference to a programming environment that is also usable for different PC operating systems (Windows and Linux). The Biopsychology--Nonlinear Analysis Toolbox and documentation are available freely and open-source from http://biopsynltoolbox.sourceforge.net.
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Affiliation(s)
- Christian Beste
- Institute for Cognitive Neuroscience, Department of Biopsychology, Ruhr-Universität Bochum, Universitätsstrasse 150, 44780 Bochum, Germany.
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Dorval AD. Estimating Neuronal Information: Logarithmic Binning of Neuronal Inter-Spike Intervals. ENTROPY 2011; 13:485-501. [PMID: 24839390 PMCID: PMC4020285 DOI: 10.3390/e13020485] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Neurons communicate via the relative timing of all-or-none biophysical signals called spikes. For statistical analysis, the time between spikes can be accumulated into inter-spike interval histograms. Information theoretic measures have been estimated from these histograms to assess how information varies across organisms, neural systems, and disease conditions. Because neurons are computational units that, to the extent they process time, work not by discrete clock ticks but by the exponential decays of numerous intrinsic variables, we propose that neuronal information measures scale more naturally with the logarithm of time. For the types of inter-spike interval distributions that best describe neuronal activity, the logarithm of time enables fewer bins to capture the salient features of the distributions. Thus, discretizing the logarithm of inter-spike intervals, as compared to the inter-spike intervals themselves, yields histograms that enable more accurate entropy and information estimates for fewer bins and less data. Additionally, as distribution parameters vary, the entropy and information calculated from the logarithm of the inter-spike intervals are substantially better behaved, e.g., entropy is independent of mean rate, and information is equally affected by rate gains and divisions. Thus, when compiling neuronal data for subsequent information analysis, the logarithm of the inter-spike intervals is preferred, over the untransformed inter-spike intervals, because it yields better information estimates and is likely more similar to the construction used by nature herself.
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Affiliation(s)
- Alan D. Dorval
- Department of Bioengineering and the Brain Institute, University of Utah, Salt Lake City, UT 84108, USA
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Ince RA, Senatore R, Arabzadeh E, Montani F, Diamond ME, Panzeri S. Information-theoretic methods for studying population codes. Neural Netw 2010; 23:713-27. [DOI: 10.1016/j.neunet.2010.05.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2009] [Revised: 05/14/2010] [Accepted: 05/14/2010] [Indexed: 11/28/2022]
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Ince RAA, Mazzoni A, Petersen RS, Panzeri S. Open source tools for the information theoretic analysis of neural data. Front Neurosci 2010; 4:62. [PMID: 20730105 PMCID: PMC2891486 DOI: 10.3389/neuro.01.011.2010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2009] [Accepted: 12/11/2009] [Indexed: 11/28/2022] Open
Abstract
The recent and rapid development of open source software tools for the analysis of neurophysiological datasets consisting of simultaneous multiple recordings of spikes, field potentials and other neural signals holds the promise for a significant advance in the standardization, transparency, quality, reproducibility and variety of techniques used to analyze neurophysiological data and for the integration of information obtained at different spatial and temporal scales. In this review we focus on recent advances in open source toolboxes for the information theoretic analysis of neural responses. We also present examples of their use to investigate the role of spike timing precision, correlations across neurons, and field potential fluctuations in the encoding of sensory information. These information toolboxes, available both in MATLAB and Python programming environments, hold the potential to enlarge the domain of application of information theory to neuroscience and to lead to new discoveries about how neurons encode and transmit information.
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Affiliation(s)
- Robin A. A. Ince
- Faculty of Life Sciences, University of ManchesterManchester, UK
| | - Alberto Mazzoni
- Robotics, Brain and Cognitive Sciences Department, Italian Institute of TechnologyGenoa, Italy
- Division of Statistical Physics, Institute for Scientific InterchangeTurin, Italy
| | | | - Stefano Panzeri
- Robotics, Brain and Cognitive Sciences Department, Italian Institute of TechnologyGenoa, Italy
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Chen J, Reitzen SD, Kohlenstein JB, Gardner EP. Neural representation of hand kinematics during prehension in posterior parietal cortex of the macaque monkey. J Neurophysiol 2009; 102:3310-28. [PMID: 19793876 DOI: 10.1152/jn.90942.2008] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Studies of hand manipulation neurons in posterior parietal cortex of monkeys suggest that their spike trains represent objects by the hand postures needed for grasping or by the underlying patterns of muscle activation. To analyze the role of hand kinematics and object properties in a trained prehension task, we correlated the firing rates of neurons in anterior area 5 with hand behaviors as monkeys grasped and lifted knobs of different shapes and locations in the workspace. Trials were divided into four classes depending on the approach trajectory: forward, lateral, and local approaches, and regrasps. The task factors controlled by the animal-how and when he used the hand-appeared to play the principal roles in modulating firing rates of area 5 neurons. In all, 77% of neurons studied (58/75) showed significant effects of approach style on firing rates; 80% of the population responded at higher rates and for longer durations on forward or lateral approaches that included reaching, wrist rotation, and hand preshaping prior to contact, but only 13% distinguished the direction of reach. The higher firing rates in reach trials reflected not only the arm movements needed to direct the hand to the target before contact, but persisted through the contact, grasp, and lift stages. Moreover, the approach style exerted a stronger effect on firing rates than object features, such as shape and location, which were distinguished by half of the population. Forty-three percent of the neurons signaled both the object properties and the hand actions used to acquire them. However, the spread in firing rates evoked by each knob on reach and no-reach trials was greater than distinctions between different objects grasped with the same approach style. Our data provide clear evidence for synergies between reaching and grasping that may facilitate smooth, coordinated actions of the arm and hand.
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Affiliation(s)
- Jessie Chen
- Department of Physiology and Neuroscience, New York University School of Medicine, New York, NY 10016, USA
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Magri C, Whittingstall K, Singh V, Logothetis NK, Panzeri S. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci 2009; 10:81. [PMID: 19607698 PMCID: PMC2723115 DOI: 10.1186/1471-2202-10-81] [Citation(s) in RCA: 152] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2009] [Accepted: 07/16/2009] [Indexed: 11/10/2022] Open
Abstract
Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.
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Affiliation(s)
- Cesare Magri
- Italian Institute of Technology, Department of Robotics, Brain and Cognitive Sciences, I-16163 Genoa, Italy.
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