Complexity Measures in Magnetoencephalography: Measuring "Disorder" in Schizophrenia
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{"title"=>"Complexity measures in magnetoencephalography: Measuring \"disorder\" in schizophrenia", "type"=>"journal", "authors"=>[{"first_name"=>"Matthew J.", "last_name"=>"Brookes", "scopus_author_id"=>"8599486300"}, {"first_name"=>"Emma L.", "last_name"=>"Hall", "scopus_author_id"=>"37093012100"}, {"first_name"=>"Siân E.", "last_name"=>"Robson", "scopus_author_id"=>"56004009900"}, {"first_name"=>"Darren", "last_name"=>"Price", "scopus_author_id"=>"56822218300"}, {"first_name"=>"Lena", "last_name"=>"Palaniyappan", "scopus_author_id"=>"23010205500"}, {"first_name"=>"Elizabeth B.", "last_name"=>"Liddle", "scopus_author_id"=>"25628234500"}, {"first_name"=>"Peter F.", "last_name"=>"Liddle", "scopus_author_id"=>"7007085436"}, {"first_name"=>"Stephen E.", "last_name"=>"Robinson", "scopus_author_id"=>"35592805900"}, {"first_name"=>"Peter G.", "last_name"=>"Morris", "scopus_author_id"=>"55521421200"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84928778562", "doi"=>"10.1371/journal.pone.0120991", "pui"=>"604107138", "pmid"=>"25886553", "scopus"=>"2-s2.0-84928778562", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"f9f7e5a8-3b32-3a54-9810-db88a60bea60", "abstract"=>"This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).", "link"=>"http://www.mendeley.com/research/complexity-measures-magnetoencephalography-measuring-disorder-schizophrenia", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>5, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>5, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>5, "Agricultural and Biological Sciences"=>1, "Medicine and Dentistry"=>2, "Neuroscience"=>3, "Physics and Astronomy"=>3, "Psychology"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"Netherlands"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2025833"], "description"=>"<p>Flow chart summarising the overall data analysis pipeline.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383465, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flow_chart_summarising_the_overall_data_analysis_pipeline_/1383465", "title"=>"Flow chart summarising the overall data analysis pipeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025836"], "description"=>"<p>Schematic diagram detailing the calculation of Rank Vector Entropy.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383468, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g002", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_diagram_detailing_the_calculation_of_Rank_Vector_Entropy_/1383468", "title"=>"Schematic diagram detailing the calculation of Rank Vector Entropy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025837"], "description"=>"<p>The unfilled squares indicate points that match the template points to form sequences of length m or m+1 (here m = 2). The template points are moved sequentially through the time-series, and the total number of matches of length m, and length m+1 are calculated.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383469, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_simulated_time_series_is_shown_in_blue_the_template_points_are_shown_in_solid_squares_and_the_shaded_grey_areas_indicate_the_points_that_are_within_177_r_tolerance_of_these_template_points_/1383469", "title"=>"A simulated time-series is shown in blue, the template points are shown in solid squares, and the shaded grey areas indicate the points that are within ± r (tolerance) of these template points.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2026092", "https://ndownloader.figshare.com/files/2026093", "https://ndownloader.figshare.com/files/2026094", "https://ndownloader.figshare.com/files/2026095", "https://ndownloader.figshare.com/files/2026096", "https://ndownloader.figshare.com/files/2026097", "https://ndownloader.figshare.com/files/2026098", "https://ndownloader.figshare.com/files/2026099", "https://ndownloader.figshare.com/files/2026100", "https://ndownloader.figshare.com/files/2026101", "https://ndownloader.figshare.com/files/2026102", "https://ndownloader.figshare.com/files/2026103", "https://ndownloader.figshare.com/files/2026104", "https://ndownloader.figshare.com/files/2026105", "https://ndownloader.figshare.com/files/2026106", "https://ndownloader.figshare.com/files/2026107", "https://ndownloader.figshare.com/files/2026108", "https://ndownloader.figshare.com/files/2026109", "https://ndownloader.figshare.com/files/2026110", "https://ndownloader.figshare.com/files/2026111", "https://ndownloader.figshare.com/files/2026112", "https://ndownloader.figshare.com/files/2026113", "https://ndownloader.figshare.com/files/2026114", "https://ndownloader.figshare.com/files/2026115"], "description"=>"<div><p>This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of ‘disorder’ in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).</p></div>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383628, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0120991.s001", "https://dx.doi.org/10.1371/journal.pone.0120991.s002", "https://dx.doi.org/10.1371/journal.pone.0120991.s003", "https://dx.doi.org/10.1371/journal.pone.0120991.s004", "https://dx.doi.org/10.1371/journal.pone.0120991.s005", "https://dx.doi.org/10.1371/journal.pone.0120991.s006", "https://dx.doi.org/10.1371/journal.pone.0120991.s007", "https://dx.doi.org/10.1371/journal.pone.0120991.s008", "https://dx.doi.org/10.1371/journal.pone.0120991.s009", "https://dx.doi.org/10.1371/journal.pone.0120991.s010", "https://dx.doi.org/10.1371/journal.pone.0120991.s011", "https://dx.doi.org/10.1371/journal.pone.0120991.s012", "https://dx.doi.org/10.1371/journal.pone.0120991.s013", "https://dx.doi.org/10.1371/journal.pone.0120991.s014", "https://dx.doi.org/10.1371/journal.pone.0120991.s015", "https://dx.doi.org/10.1371/journal.pone.0120991.s016", "https://dx.doi.org/10.1371/journal.pone.0120991.s017", "https://dx.doi.org/10.1371/journal.pone.0120991.s018", "https://dx.doi.org/10.1371/journal.pone.0120991.s019", "https://dx.doi.org/10.1371/journal.pone.0120991.s020", "https://dx.doi.org/10.1371/journal.pone.0120991.s021", "https://dx.doi.org/10.1371/journal.pone.0120991.s022", "https://dx.doi.org/10.1371/journal.pone.0120991.s023", "https://dx.doi.org/10.1371/journal.pone.0120991.s024"], "stats"=>{"downloads"=>104, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Complexity_Measures_in_Magnetoencephalography_Measuring_Disorder_in_Schizophrenia_/1383628", "title"=>"Complexity Measures in Magnetoencephalography: Measuring \"Disorder\" in Schizophrenia", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025845"], "description"=>"<p>Regions identified include the visual cortex, the left and right motor cortices, cingulo-insula cortex, pre-motor cortex, the left and right fronto-parietal networks, the left and right temporo-parietal junction and the left and right insula cortices. This represents a unique method to parcellate the cortex, based upon the temporal signature of entropy. Images are displayed in radiological convention.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383477, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_signatures_of_12_independent_components_derived_from_ICA_applied_to_entropic_transformation_of_MEG_data_concatenated_across_all_subjects_and_tasks_/1383477", "title"=>"Spatial signatures of 12 independent components, derived from ICA applied to entropic transformation of MEG data concatenated across all subjects, and tasks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025851"], "description"=>"<p>The upper panel shows results for the Sternberg task and the lower panel shows results for the RM task. For entropic timecourses, the blue line shows change in entropy from baseline level; the shaded region shows standard error across subjects. Time-frequency spectrograms show deviation from resting state oscillatory amplitude, with red and blue showing increased and decreased oscillatory amplitude respectively. Note that both tasks elicit transient changes in signal entropy. Note also the differences in temporal profile of entropy across brain regions.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383479, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g005", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Entropic_time_courses_and_time_frequency_spectrograms_are_given_for_the_visual_left_motor_left_fronto_parietal_and_left_insula_regions_/1383479", "title"=>"Entropic time-courses and time-frequency spectrograms are given for the visual, left motor, left fronto-parietal and left insula regions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025852"], "description"=>"<p>Note the general trend that entropy exhibits a negative correlation with alpha and beta oscillations and a positive correlation with gamma oscillations. Inset images show the spatial maps of the regions used. The mean correlation across subjects is shown and error bars show standard deviation across subjects.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383480, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g006", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relationship_between_neural_oscillatory_amplitude_and_signal_entropy_in_the_visual_motor_and_insula_cortices_/1383480", "title"=>"The relationship between neural oscillatory amplitude and signal entropy in the visual, motor and insula cortices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025853"], "description"=>"<p>Results are presented for schizophrenia patients (red) and healthy control subjects (blue) in the relevant and irrelevant phase of the RM task, and the Sternberg task. Bar charts show averaged entropy change during task, compared to rest, collapsed across both tasks. Note that in the cingulo-insula cortex, a significant (corrected p < 0.05) increase in entropy in patients, relative to control subjects is observed. Note also that the difference between patients and controls changes as a function of time, highlighting the importance of dynamic assessment of entropy.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383481, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g007", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_courses_in_the_visual_cortex_A_cingulo_insula_cortex_B_left_motor_cortex_C_and_right_motor_cortex_D_showing_task_induced_change_in_baseline_entropy_/1383481", "title"=>"Time-courses in the visual cortex (A), cingulo-insula cortex (B), left motor cortex (C) and right motor cortex (D) showing task induced change in baseline entropy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2025854"], "description"=>"<p>Graphs show change in MSE from rest in patients (red) and controls (blue) for the RM and Sternberg tasks. Bar charts show specific cases for scales 1 and 2. Note that there is general agreement between RVE (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120991#pone.0120991.g004\" target=\"_blank\">Fig 4</a>) and MSE (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120991#pone.0120991.g005\" target=\"_blank\">Fig 5</a>) in showing an increased entropy difference in patients relative to controls. Note also that this difference depends on temporal scale, and is maximum when entropy is measured on the very short timescale (i.e. scales of 1 and 2). * indicates p<0.05 corrected.</p>", "links"=>[], "tags"=>["brain regions", "entropy", "characterised", "method", "meg"], "article_id"=>1383482, "categories"=>["Biological Sciences"], "users"=>["Matthew J. Brookes", "Emma L. Hall", "Siân E. Robson", "Darren Price", "Lena Palaniyappan", "Elizabeth B. Liddle", "Peter F. Liddle", "Stephen E. Robinson", "Peter G. Morris"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0120991.g008", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multi_scale_entropy_measured_in_the_visual_cortex_A_cingulo_insula_cortex_B_left_motor_cortex_C_and_right_motor_cortex_D_/1383482", "title"=>"Multi-scale-entropy, measured in the visual cortex (A), cingulo-insula cortex (B), left motor cortex (C) and right motor cortex (D).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-17 02:58:45"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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