Adjusting Brain Dynamics in Schizophrenia by Means of Perceptual and Cognitive Training
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{"title"=>"Adjusting brain dynamics in schizophrenia by means of perceptual and cognitive training", "type"=>"journal", "authors"=>[{"first_name"=>"Tzvetan", "last_name"=>"Popov", "scopus_author_id"=>"24537647800"}, {"first_name"=>"Brigitte", "last_name"=>"Rockstroh", "scopus_author_id"=>"7005647884"}, {"first_name"=>"Nathan", "last_name"=>"Weisz", "scopus_author_id"=>"6602346810"}, {"first_name"=>"Thomas", "last_name"=>"Elbert", "scopus_author_id"=>"7006641802"}, {"first_name"=>"Gregory A.", "last_name"=>"Miller", "scopus_author_id"=>"35555904700"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365274667", "sgr"=>"84864015485", "issn"=>"19326203", "pmid"=>"22815697", "scopus"=>"2-s2.0-84864015485", "doi"=>"10.1371/journal.pone.0039051", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"4d6a0b6b-c613-3110-859a-64541943868a", "abstract"=>"BACKGROUND: In a previous report we showed that cognitive training fostering auditory-verbal discrimination and working memory normalized magnetoencephalographic (MEG) M50 gating ratio in schizophrenia patients. The present analysis addressed whether training effects on M50 ratio and task performance are mediated by changes in brain oscillatory activity. Such evidence should improve understanding of the role of oscillatory activity in phenomena such as M50 ratio, the role of dysfunctional oscillatory activity in processing abnormalities in schizophrenia, and mechanisms of action of cognitive training.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Time-locked and non-time-locked oscillatory activity was measured together with M50 ratio in a paired-click design before and after a 4-week training of 36 patients randomly assigned to specific cognitive exercises (CE) or standard (comparison) cognitive training (CP). Patient data were compared to those of 15 healthy controls who participated in two MEG measurements 4 weeks apart without training. Training led to more time-locked gamma-band response and more non-time-locked alpha-band desynchronization, moreso after CE than after CP. Only after CE, increased alpha desynchronization was associated with normalized M50 ratio and with improved verbal memory performance. Thus, both types of cognitive training normalized gamma activity, associated with improved stimulus encoding. More targeted training of auditory-verbal discrimination and memory additionally normalized alpha desynchronization, associated with improved elaborative processing. The latter presumably contributes to improved auditory gating and cognitive function.\\n\\nCONCLUSIONS/SIGNIFICANCE: Results suggest that dysfunctional interplay of ocillatory activity that may contribute to auditory processing disruption in schizophrenia can be modified by targeted training.", "link"=>"http://www.mendeley.com/research/adjusting-brain-dynamics-schizophrenia-means-perceptual-cognitive-training", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>14, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>14, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>3, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>6, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>7, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>11, "Philosophy"=>1, "Neuroscience"=>2, "Sports and Recreations"=>1, "Psychology"=>25, "Social Sciences"=>2, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>11}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>2}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Psychology"=>{"Psychology"=>25}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>7}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"United Kingdom"=>2, "Australia"=>1}, "group_count"=>5}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/607447"], "description"=>"<p>Panel <b>A</b> illustrates significant cluster of MEG sensors (p<sub>cluster</sub><0.01) obtained after correction for multiple comparisons. Sensors belonging to the significant cluster are marked with black circles. Color bar indicates range of correlation coefficients. <b>B</b>: Relationship between change in verbal memory (VM) performance (abscissa, positive values indicate more improvement) and pre-to-post-training change in alpha power decrease (ordinate, negative values indicate more alpha power decrease, thus, improvement) by patient training group based on the ROI in 5A.</p>", "links"=>[], "tags"=>["alpha"], "article_id"=>277944, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g005", "stats"=>{"downloads"=>11, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topography_of_the_relationship_between_verbal_memory_performance_and_alpha_power_decrease_/277944", "title"=>"Topography of the relationship between verbal memory performance and alpha power decrease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:19:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/607366"], "description"=>"<p>Panel A illustrates significant cluster of MEG sensors (p<sub>cluster</sub><0.05) obtained after correction for multiple comparisons. Sensors belonging to the significant cluster are marked with black circles. Color bar indicates range of correlation coefficients. B: Relationship between change in alpha power decrease (abscissa, negative values indicate improvement) and pre-to-post-training change in M50 gating ratio (ordinate, positive values indicate changes towards normal, that is, smaller gating ratio) by patient training group based on the ROI in 4A.</p>", "links"=>[], "tags"=>["alpha", "m50", "gating"], "article_id"=>277859, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topography_of_the_relationship_between_the_change_in_alpha_power_decrease_and_the_change_in_M50_gating_ratio_/277859", "title"=>"Topography of the relationship between the change in alpha power decrease and the change in M50 gating ratio.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:19:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/607274"], "description"=>"<p>Panel A depicts a decrease around S2 (300–800 ms after S1, dashed rectangles) before vs. after training, averaged across respective clusters defined in 3B. B: Clusters of significant training effects displayed separately for CE and CP patient groups, scored for the ROIs defined after cluster-based permutation analysis.</p>", "links"=>[], "tags"=>["plots", "illustrating", "alpha"], "article_id"=>277765, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_frequency_plots_illustrating_a_training_effect_on_alpha_power_/277765", "title"=>"Time-frequency plots illustrating a training effect on alpha power.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:18:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/607584"], "description"=>"<p>Note: Variables (except gender) are expressed in mean and standard deviation. LQ = Handedness lateralization quotient. Statistical effects are listed in the bottom row, *p<.05, **p<.01.</p>", "links"=>[], "tags"=>["variables", "schizophrenia", "patients", "controls"], "article_id"=>278073, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_variables_for_schizophrenia_patients_SZ_and_health_controls_HC_/278073", "title"=>"Demographic variables for schizophrenia patients (SZ) and health controls (HC).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:20:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/607085"], "description"=>"<p>Panel A depicts an increase 100–400 ms after S1 onset (dashed squares) before vs. after training, averaged across the sensor cluster defined in 2B. B: Sensor cluster showing significant CE training effect on gamma power increase defined by cluster-based permutation analysis.</p>", "links"=>[], "tags"=>["plots", "illustrating", "gamma"], "article_id"=>277576, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_frequency_plots_illustrating_a_training_effect_on_gamma_power_/277576", "title"=>"Time-frequency plots illustrating a training effect on gamma power.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:17:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/607516"], "description"=>"<p>Panel <b>A</b> illustrates significant cluster of MEG sensors (p<sub>cluster</sub><0.02) obtained after correction for multiple comparisons. Sensors in the cluster are marked with black circles. Color bar indicates range of correlation coefficients. <b>B</b>: Relationship between change in Global Assessment of Functioning (GAF, abscissa: positive values indicate improvement) and pre-to-post-training change of alpha power decrease (ordinate: negative values indicate more alpha power decrease, thus improvement) by patient training group based on the ROI in A.</p>", "links"=>[], "tags"=>["alpha"], "article_id"=>278009, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topography_of_the_relationship_between_global_function_rating_and_alpha_power_/278009", "title"=>"Topography of the relationship between global function rating and alpha power.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:20:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/606941"], "description"=>"<p>Time-frequency plots showing group differences in change from pre-S1 baseline (A), averaged across respective sensor clusters in 1B. Red indicates larger power increase from pre-S1 baseline in controls than in schizophrenia patients, and blue indicates larger power decrease in controls than in patients. Dashed rectangles indicate time-frequency window of significant group differences. Color bar indicates percent change from pre-S1 baseline. B: Clusters of sensors showing significant group differences in gamma increase (left panel, 60–80 Hz, 100–400 ms after S1) and alpha decrease (right panel, 8–10 Hz, 300–800 ms after S1). Sensors belonging to a significant cluster marked with circles. Color bar indicates size of group difference represented in t-values.</p>", "links"=>[], "tags"=>["differences"], "article_id"=>277435, "categories"=>["Science Policy", "Mental Health", "Neuroscience"], "users"=>["Tzvetan Popov", "Brigitte Rockstroh", "Nathan Weisz", "Thomas Elbert", "Gregory A. Miller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0039051.g001", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Group_differences_prior_to_training_/277435", "title"=>"Group differences prior to training.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:16:56"}

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[322, 550, 671, 773, 864, 955, 1048, 1135, 1223, 1308, 1387, 1465, 1534, 1602, 1673, 1744, 1813, 1885, 1955, 2026, 2093, 2160, 2228, 2290, 2349]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[352, 587, 696, 809, 901, 989, 1072, 1156, 1257, 1334, 1422, 1486, 1555, 1647, 1714, 1780, 1844, 1919, 1997, 2051, 2138, 2198, 2267, 2324, 2391]}, {"subject_area"=>"/Computer and information sciences/Neural networks", "average_usage"=>[385, 629, 754, 840, 925, 1006, 1120, 1211, 1283, 1380, 1441, 1509, 1565, 1639, 1703, 1737, 1808, 1859, 1995, 2078, 2148, 2197, 2234, 2286, 2362]}, {"subject_area"=>"/Medicine and health sciences/Mental health and psychiatry", "average_usage"=>[341, 608, 746, 856, 954, 1057, 1164, 1270, 1368, 1453, 1563, 1640, 1735, 1813, 1893, 1982, 2056, 2134, 2213, 2310, 2366, 2434, 2496, 2563, 2624]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[304, 506, 616, 712, 799, 879, 968, 1052, 1134, 1212, 1284, 1357, 1427, 1494, 1557, 1621, 1689, 1756, 1823, 1883, 1944, 1997, 2056, 2118, 2171]}]}
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