Contributions of Subsurface Cortical Modulations to Discrimination of Executed and Imagined Grasp Forces through Stereoelectroencephalography
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{"title"=>"Contributions of subsurface cortical modulations to discrimination of executed and imagined grasp forces through stereoelectroencephalography", "type"=>"journal", "authors"=>[{"first_name"=>"Brian A.", "last_name"=>"Murphy", "scopus_author_id"=>"35567044300"}, {"first_name"=>"Jonathan P.", "last_name"=>"Miller", "scopus_author_id"=>"24458337000"}, {"first_name"=>"Kabilar", "last_name"=>"Gunalan", "scopus_author_id"=>"26027492500"}, {"first_name"=>"A.", "last_name"=>"Bolu Ajiboye", "scopus_author_id"=>"6506081313"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84962517492", "sgr"=>"84962517492", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0150359", "pui"=>"609060001"}, "id"=>"f495b80b-0060-3388-8918-e61cb6c07026", "abstract"=>"Stereoelectroencephalographic (SEEG) depth electrodes have the potential to record neural activity from deep brain structures not easily reached with other intracranial recording technologies. SEEG electrodes were placed through deep cortical structures including central sulcus and insular cortex. In order to observe changes in frequency band modulation, participants performed force matching trials at three distinct force levels using two different grasp configurations: a power grasp and a lateral pinch. Signals from these deeper structures were found to contain information useful for distinguishing force from rest trials as well as different force levels in some participants. High frequency components along with alpha and beta bands recorded from electrodes located near the primary motor cortex wall of central sulcus and electrodes passing through sensory cortex were found to be themost useful for classification of force versus rest although one participant did have significant modulation in the insular cortex. This study electrophysiologically corroborates with previous imaging studies that show force-related modulation occurs inside of central sulcus and insular cortex. The results of this work suggest that depth electrodes could be useful tools for investigating the functions of deeper brain structures as well as showing that central sulcus and insular cortex may contain neural signals that could be used for control of a grasp force BMI.", "link"=>"http://www.mendeley.com/research/contributions-subsurface-cortical-modulations-discrimination-executed-imagined-grasp-forces-through", "reader_count"=>12, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>1, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>1, "Neuroscience"=>1, "Sports and Recreations"=>1, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Italy"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4832038", "https://ndownloader.figshare.com/files/4832041", "https://ndownloader.figshare.com/files/4832044", "https://ndownloader.figshare.com/files/4832047", "https://ndownloader.figshare.com/files/4832050", "https://ndownloader.figshare.com/files/4832053"], "description"=>"<div><p>Stereoelectroencephalographic (SEEG) depth electrodes have the potential to record neural activity from deep brain structures not easily reached with other intracranial recording technologies. SEEG electrodes were placed through deep cortical structures including central sulcus and insular cortex. In order to observe changes in frequency band modulation, participants performed force matching trials at three distinct force levels using two different grasp configurations: a power grasp and a lateral pinch. Signals from these deeper structures were found to contain information useful for distinguishing force from rest trials as well as different force levels in some participants. High frequency components along with alpha and beta bands recorded from electrodes located near the primary motor cortex wall of central sulcus and electrodes passing through sensory cortex were found to be the most useful for classification of force versus rest although one participant did have significant modulation in the insular cortex. This study electrophysiologically corroborates with previous imaging studies that show force-related modulation occurs inside of central sulcus and insular cortex. The results of this work suggest that depth electrodes could be useful tools for investigating the functions of deeper brain structures as well as showing that central sulcus and insular cortex may contain neural signals that could be used for control of a grasp force BMI.</p></div>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109252, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0150359.s001", "https://dx.doi.org/10.1371/journal.pone.0150359.s002", "https://dx.doi.org/10.1371/journal.pone.0150359.s003", "https://dx.doi.org/10.1371/journal.pone.0150359.s004", "https://dx.doi.org/10.1371/journal.pone.0150359.s005", "https://dx.doi.org/10.1371/journal.pone.0150359.s006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Contributions_of_Subsurface_Cortical_Modulations_to_Discrimination_of_Executed_and_Imagined_Grasp_Forces_through_Stereoelectroencephalography/3109252", "title"=>"Contributions of Subsurface Cortical Modulations to Discrimination of Executed and Imagined Grasp Forces through Stereoelectroencephalography", "pos_in_sequence"=>1, "defined_type"=>4, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832056"], "description"=>"<p>Participants had stereotactic depth electrodes placed for clinical epilepsy monitoring and were asked to perform force matching tasks in two different grasp configurations (power grasp and lateral pinch grasp–inset). Participants were prompted by a screen showing them the desired force level as well as their delivered level of force each represented as the color and size of a ball being squeezed on the screen. SEEG signals were split between the clinical monitoring system and the research neural recording system. Frequency band powers were calculated offline with a short-time-Fourier transform and used for discrete state classification.</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109255, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Experimental_Setup_/3109255", "title"=>"Experimental Setup.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832059"], "description"=>"<p>Each row corresponds to an individual participant (A-D). <i>Left</i>: Participant specific cortical surface reconstructions (red: motor cortex, yellow: primary sensory cortex, green: premotor cortex, blue: supplementary motor cortex, purple: insular cortex) and electrode penetration sites (white circles). <i>Middle</i>: Oblique views of sagittal and axial MRI slices showing the electrode trajectories. Electrode contacts are color-coded according to the cortical region they pass by or through. The MRI slices shown are located as close to the electrodes as possible without obscuring any of the contacts. <i>Right</i>: Sagittal views showing the actual depths of the electrodes.</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109258, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Locations_of_electrodes_/3109258", "title"=>"Locations of electrodes.", "pos_in_sequence"=>3, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832062"], "description"=>"<p>Panel A shows average z-score modulation over time on one motor cortex (MC) channel and one insular cortex (IC) channel in the alpha, beta and highest gamma features during hard force tasks. The solid vertical line corresponds to force onset with the dotted vertical representing the average target cue time. Shaded regions indicate confidence intervals for the signal means of each frequency band. Panel B shows the mean ± standard deviation depth of modulation (change in z-score from baseline) for statistically significant MC (red bars) and IC (purple bars) channels, organized by participant (rows) and by frequency band (x-axis) for each of the two grasping configurations. All participants showed significant modulation in the both motor and insular cortices (excluding D who did not have electrodes placed in the insula).</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109261, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Signal_Modulation_during_Force_/3109261", "title"=>"Signal Modulation during Force.", "pos_in_sequence"=>4, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832065"], "description"=>"<p>Histograms show the distribution of feature classification accuracies for force versus rest classification. These bar values represent the single feature classification accuracies across 10 repeats of the cross-validation and are organized by brain region (— = White matter tracts, IC = Insular Cortex, MC = Motor Cortex, PM = Premotor Cortex, S1 = Primary Sensory Cortex, and SM = Supplementary Motor Area) and feature (LMP = Local Motor Potential, δ/θ = 0–6 Hz, α = 6–12 Hz, β = 12-30Hz, γ = 30–50 Hz, γ+ = 70–110 Hz, γ++ = 130–170 Hz). Colors correspond to each participant (blue = A, red = B, green = C, and purple = D) with pinch and power grasp configurations combined. Dotted line (52.67%) shows the high end of the 95% confidence interval for chance classification. All participants had motor cortical features that could be used for discrimination of force versus rest in contrast to other brain regions which were more participant specific.</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109264, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Classification_density_for_single_features_/3109264", "title"=>"Classification density for single features.", "pos_in_sequence"=>5, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832068"], "description"=>"<p>Confusion matrices of light versus hard versus rest classification for each participant (columns) in the lateral pinch and power configurations (rows) using sequential feature selection and two levels of cross-validation. Features were averaged across a time window of -400 ms to +400 ms centered on force onset or 1 second after the rest cue. All participants showed the ability to differentially modulate neural activity between rest and force with high accuracy, but were less adept at differentially modulating neural activity between force levels. * denote significance above chance classification (1 in 3) using false-discovery rate adjusted p-values < 0.05 while ~ denotes a more stringent significance from 50% to see if light vs hard forces were significantly different.</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109267, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Rest_versus_Light_versus_Hard_Classification_Accuracies_/3109267", "title"=>"Rest versus Light versus Hard Classification Accuracies.", "pos_in_sequence"=>6, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832071"], "description"=>"<p>Panel A shows the mean ± standard deviation depth of modulation (change in z-score from baseline) for statistically significant MC (red bars) and IC (purple bars) channels, organized by participant (rows) and by frequency band (x-axis) for each of the two imagined force grasping configurations. Modulation was lower as a whole during imagination compared to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150359#pone.0150359.g003\" target=\"_blank\">Fig 3B</a> with the exception of the alpha band for participant A. Panel B shows single feature classification distributions for imagined force versus rest trials. Participant A is shown in blue and participant B is shown in red. The chance level is shown as the dotted line. Histograms are arranged by frequency band (rows) and by brain region (columns).</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109270, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Imagined_single_feature_modulation_/3109270", "title"=>"Imagined single feature modulation.", "pos_in_sequence"=>7, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/4832074"], "description"=>"<p>Bars show the average classification performance across 10 sets of cross-validation. The dotted horizontal line shows prior probability level (50%). The SVM was able to achieve significant classification of imagined force versus rest for both participants (A = black, B = white bars) and in both grasp configurations (pinch and power).</p>", "links"=>[], "tags"=>["insular cortex", "SEEG", "brain structures", "intracranial recording technologies", "Subsurface Cortical Modulations", "BMI", "depth electrodes", "force levels", "Imagined Grasp Forces", "motor cortex wall", "participant", "study electrophysiologically corroborates", "sulcu", "frequency band modulation", "High frequency components"], "article_id"=>3109273, "categories"=>["Space Science", "Cell Biology", "Neuroscience", "Physiology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Science Policy"], "users"=>["Brian A. Murphy", "Jonathan P. Miller", "Kabilar Gunalan", "A. Bolu Ajiboye"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150359.g007", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Average_classification_of_imagined_force_versus_rest_/3109273", "title"=>"Average classification of imagined force versus rest.", "pos_in_sequence"=>8, "defined_type"=>1, "published_date"=>"2016-03-10 09:17:21"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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