865 Matching Annotations
  1. Dec 2017
    1. Jetstream may be used for prototyping, for creating tailored workflows to either use at smaller scale with a handful of CPUs or to port to larger environments after doing your proof of concept work at a smaller level.

      Function

    2. Jetstream is meant primarily for interactive research, small scale processing on demand, or as the backend to science gateways to send research jobs to other HPC or HTC resources.

      Function

    3. Jetstream is an NSF-funded (NSF-1445604), user-friendly cloud environment designed to give researchers access to interactive computing and data analysis resources on demand, whenever and wherever they want to analyze their data.

      Function

    4. It provides a library of virtual machines designed to do discipline specific scientific analysis.

      Function

  2. Nov 2017
  3. Oct 2017
  4. Sep 2017
    1. Run

      I get some warnings, that I presume is OK to ignore: 2017-09-08 17:21:57,916 interface:WARNING AFNI is outdated, detected version Debian-16.2.07~dfsg.1-5~nd16.04+1 and AFNI_17.2.12 is available. 2017-09-08 17:21:58,400 interface:WARNING AFNI is outdated, detected version Debian-16.2.07~dfsg.1-5~nd16.04+1 and AFNI_17.2.12 is available.

  5. Aug 2017
  6. Jun 2017
  7. May 2017
    1. Containerization technology takes the hassle out of setting up software and can boost the reproducibility of data-driven research.
    1. automatic quality control on the measurements computed by FreeSurfer by identifying outliers; if >25% of the used morphometric variables exhibited values that were more than two SDs away from the population mean, we deemed that subject an outlier and discarded it.

      Automatic outlier detection

    2. 3,800 unique individuals spanning nine large-scale studies: the Harvard/Massachusetts General Hospital Brain Genomic Superstruct Project (GSP) (26), the Human Connectome Project (HCP) (27), the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (28), the Attention-Deficit Hyperactivity Disorder (ADHD 200) sample (29), the Open Access Series of Imaging Studies (OASIS) cross-sectional sample (30), the Center for Biomedical Research Excellence (COBRE) schizophrenia sample (31), the MIND Clinical Imaging Consortium (MCIC) schizophrenia sample (32), the Autism Brain Imaging Data Exchange (ABIDE) (33), and the Parkinson Progression Marker Initiative (PPMI) (34).

      Datasets

    3. FreeSurfer (20), a freely available, widely used, and extensively validated brain MRI analysis software package, to automatically process structural MRI scans and obtain a vector of volumetric measurements across subcortical structures and cortical thickness measurements across the entire cortical mantle, which constitute a comprehensive description of the structural neuroanatomy.

      Method: RRID:SCR_001847

  8. Apr 2017
  9. Mar 2017
  10. Feb 2017
  11. Jan 2017
    1. concern for the purpose and ambitions of science, and their role in strengthening the health and resilience of the societies they so depend upon

      Refocus of publishers

    2. The defi ning quality of publishing is judgment

      Whose judgement?

    3. The purpose of publishing should be to make sense of the world in which we live

      Purpose statement.

    4. atabases are not designed to improve either communication or understanding

      Well, we better make better databases then!

  12. Dec 2016
    1. depend on the specific version used

      And operating system. See, for example, Glatard T, Lewis LB, Ferreira da Silva R, Adalat R, Beck N, Lepage C, Rioux P, Rousseau ME, Sherif T, Deelman E, Khalili-Mahani N, Evans AC. Reproducibility of neuroimaging analyses across operating systems. Front Neuroinform. 2015 Apr 24;9:12. doi: 10.3389/fninf.2015.00012. PubMed PMID: 25964757; PubMed Central PMCID: PMC4408913. and others.

  13. Nov 2016
    1. $25,000 (for entire degree-granting institutions)

      This is the statement that is corrected in the Corrigendum. It should read: "The University of California EZID service offers annual DOI creation at $835 (for non-degree granting departments) to $2500 (for entire degree-granting institutions) per 1 million DOIs minted."

      See http://ezid.cdlib.org/learn/ for the ezid details.

      See http://journal.frontiersin.org/article/10.3389/fninf.2016.00043/full for the Corrigendum

  14. Sep 2016
  15. Aug 2016
    1. Find neuroimaging tools here

      NITRC is a really useful resource

  16. Jul 2016
    1. 2016 International Conference on Brain Informatics & Health October 13-16, 2016 in Omaha, Nebraska, USA

      Date: October 13-16, 2016 Location: Omaha, Nebraska, USA

    1. Identifiers.org Identifiers.org is a system providing resolvable persistent URIs used to identify data for the scientific community, with a current focus on the Life Sciences domain. The provision of a resolvable identifiers (URLs) fits well with the Semantic Web vision, and the Linked Data initiative.
    1. A curated, searchable portal of inter-related data standards, databases, and policies in the life, environmental and biomedical sciences
    1. right insula (peak: 43,−13,−1)
      ID: 008
      Variable: gray matter density
      Groups: SMD, BD, HV
      Model: VBM
      AnatomicLocation: right insula
      PeakLocation: 43,−13,−1
      
    2. right globus pallidus (peak: 16,−2,−7)
      ID: 007
      Variable: gray matter density
      Groups: SMD, BD, HV
      Model: VBM
      AnatomicLocation: right globus pallidus
      PeakLocation: 16,−2,−7
      
    3. globus pallidus differences were driven by increased GM volume in BD compared to both HV and SMD.
      ID: 007
      Interpretation: increased GM volume in BD compared to both HV and SMD
      
    4. Post-hoc analyses indicated that between-group differences in the cortical clusters were driven mainly by increased GM volume in HV compared to both BD and SMD,
      ID: 006
      Interpretation: increased GM volume in HV compared to both BD and SMD
      
      ID: 008
      Interpretation: increased GM volume in HV compared to both BD and SMD
      
      ID: 009
      Interpretation: increased GM volume in HV compared to both BD and SMD
      
    5. right dorsolateral prefrontal cortex (DLPFC, BA 9/46, peak: 41,52,16)
      ID: 009
      Variable: gray matter density
      Groups: SMD, BD, HV
      Model: VBM
      AnatomicLocation: right dorsolateral prefrontal cortex
      PeakLocation: 41,52,16
      
    6. bilateral pre-supplementary motor area (pre-SMA, BA 6/8, peak: 4,26,53)
      ID: 006
      Variable: gray matter density
      Groups: SMD, BD, HV
      Model: VBM
      AnatomicLocation: bilateral pre-supplementary motor 
      PeakLocation: 4,26,53
      
    7. voxel-based morphometry

      Scope: VBM

    8. trend difference in TBV (p=0.09), driven by a trend for larger TBV in HV than BD (p=0.08)
      ID:004
      Variable: TBV
      Groups: SMD, BD, HV
      P: 0.09
      Model: ANOVA
      Interpretation: TBV trend different between groups
      
      ID: 005
      Variable: TBV
      Groups: HV, BD
      P: 0.08
      Model: T-test
      Interpretation: TBV trend larger in HV compared to BD
      
    9. Age at scan differed between groups (p=0.001); the SMD group was younger than the BD (p<0.01) and HV (p=0.02) groups.
      ID: 001
      Variable: Age
      Groups: SMD, BD, HV
      P: 0.001
      Model: ANOVA
      Interpretation: Age Differed between groups
      
      ID: 002
      Variable: Age
      Groups: SMD, BD
      P: <0.01
      Model: T-test
      Interpretation: SMD younger than BD
      
      ID: 003
      Variable: Age
      Groups: SMD, HV
      P: 0.02
      Model: T-test
      Interpretation: SMD younger than HV
      
    10. Cross-sectional and longitudinal abnormalities in brain structure in children with severe mood dysregulation or bipolar disorder
    1. functional connectivity

      Scope = Functional Connectivity

    2. Aberrant amygdala intrinsic functional connectivity distinguishes youths with bipolar disorder from those with severe mood dysregulation
    1. Image analysis was done on Sun Microsystems, Inc. (Mountainview, CA) workstations using Cardviews software (Caviness et al. 1996).

      ID: VolumeAnalysis Method: MethodURL: Software: Cardviews

    1. Open Science Prize

      Initiative to promote and award open science

  17. Jun 2016
    1. DATA CITATION WORKSHOP: DEVELOPING POLICY AND PRACTICE

      Title: DATA CITATION WORKSHOP: DEVELOPING POLICY AND PRACTICE Date: Tuesday, 12 July 2016 from 8:00 AM to 5:00 PM (EDT) Location: National Academies of Sciences, Engineering & Medicine - Keck Center, 500 Fifth St., NW, Room 100, Washington, DC, United States

    1. MINDS Schema

      Minimal Information for Neuroscience Data Standard INCF - Dataspace effort

    1. INCF Short course 2016

      TItle: INCF Short course 2016 Dates: 31 August – 1 September, 2016 Location: Penta Hotel, Oxford Road, Reading RG1 7RH, United Kingdom

    1. Advanced Scientific Programming in Python

      Title: Advanced Scientific Programming in Python Dates: September 5—11, 2016. Location: Reading, UK

    1. Title: Wold-Scale Personalized Learning through Crowdsourcing and Algorithms 

      Date: June 1, 2016 Time: 3:30pm - 4:30pm EST

  18. May 2016
    1. Graph Based Analysis of Biomedical BigData

      Title: Graph Based Analysis of Biomedical BigData Date: Thursday July 7 - Friday July 8, 2016 Location: University of District of Columbia CC, Washington DC Type: 2 Day Hands-on Training

  19. Mar 2016
    1. A series of Analyses of Variance (ANOVA) were performed on CC1 through CC7 and total CC as dependent variables with sex, age, and TCV as covariates to compare CC volumes and CC midsagittal areas between youths with BPD and HC to determine if there were group differences.

      ID: ANOVAvol Variable: CC1vol Variable: CC2vol Variable: CC3vol Variable: CC4vol Variable: CC5vol Variable: CC6vol Variable: CC7vol Variable: CCvol Variable: age Variable:sex Variable:TCV

      ID: ANOVAarea Variable: CC1area Variable: CC2area Variable: CC3area Variable: CC4area Variable: CC5area Variable: CC6area Variable: CC7area Variable: CCarea Variable: age Variable:sex Variable:TCV

    2. Equality of groups on demographic and clinical variables was evaluated by t-tests for continuous variables and chi-square tests for categorical variables

      ID: Ttest Variable:

      ID: chi-square Variable:

    3. Total cerebral volume (TCV) was defined as all gray and white matter in the cerebrum and did not include CSF, cerebellum or brainstem.

      ID: StructuralVolumes Measure: TCV AnalysisWorkflow: VolumeAnalysis Data:

    4. volumetric measures of the CC, we utilized a comprehensive white matter parcellation method to subdivide the cerebral WM into peripheral and deep divisions based upon a set of topographic relationships and geometric constraints related to cortical and subcortical structures as guided by known generalized white matter organizational principles (Makris et al. 1999; Meyer et al. 1999)
      ID: CCvolumes 
      Measure: CCvol, CC1vol, CC2vol, CC3vol, CC4vol, CC5vol, CC6vol, CC7vol 
      AnalysisWorkflow: VolumeAnalysis 
      Data:
      
    5. cross-sectional area measurements were obtained for total CC and the seven subregions based on the subdivisions described by Witelson

      ID: CCareas Measure: CCarea, CC1area, CC2area, CC3area, CC4area, CC5area, CC6area, CC7area AnalysisWorkflow: VolumeAnalysis Data:

    6. SPSS 15.0 for Windows (SPSS, Inc., Chicago, IL) was used for statistical analysis.
      ID: VolumeAnalysis
      URL:
      Software: SPSS 15.0 for Windows (SPSS, Inc., Chicago, IL)
      Observation: CCVolumes 
      Model: ANOVA 
      
    7. The acquisitions included a 3-D inversion recovery-prepped spoiled gradient recalled echo coronal series, which was used for structural analysis (124 slices, prep=300 msec, TE=1 min, flip angle=25°, FOV= 24 cm2, slice thickness 1.5 mm, acquisition matrix 256×192, number of excitations=2).

      ID: SPGR AcquisitionInstrument: MRIScanner Type: SPGR

    8. Structural imaging was performed at the McLean Hospital Brain Imaging Center on a 1.5 Tesla Scanner (Signa; GE Medical Systems, Milwaukee, WI).

      ID: MRIScanner Type: MRI Location: McLean Hospital Brain Imaging Center Field: 1.5 Tesla Manufacturer: General Electric Model: Signa

    9. Significant effects for TCV (F=18.1, p<0.01) and for age group-by-diagnosis interaction term (F=6.97, p<0.01) for the CC4 volumetric measurements were found
    10. Significant effects of TCV (F=19.4, p<0.01) and for age group-by-diagnosis interaction term (F=4.60, p=0.01) for the volumetric measurements of total CC were found
      ObsID: 002
      MeasureID: CC vol
      GroupID: HC_young, HC_old, BPD_young, BPD_old
      CovariateID: TCV
      StatID: ANOVA
      F: 19.4
      P: <0.01
      
      ObsID:003
      MeasureID: CC vol
      GroupID: HC, BPD
      CovariateID: age group-by-diagnosis interaction term
      StatID: ANOVA
      F: 4.60
      P: 0.01
      
    11. For the area measurement of the total CC, significant effects were also found for TCV (F=5.15, p=0.03) and age group-by-diagnosis interaction term (F=3.08, p=0.05).
      ObsID: 004
      MeasureID: CC area
      GroupID: HC, BPD
      CovariateID: TCV
      StatID: ANOVA
      F: 5.15
      P: 0.03
      
      ObsID:005
      MeasureID: CC area
      GroupID: HC, BPD
      CovariateID: age group-by-diagnosis interaction term
      StatID: ANOVA
      F: 3.08
      P: 0.05
      
    12. For CC2, significant effects were found for TCV in CC2 volume (F=12.64, p<0.01) and area (F=5.18, p=0.03) measurements, respectively
      ObsID: 014
      MeasureID: CC2 vol
      GroupID: HC, BPD
      CovariateID: TCV
      StatID: ANOVA
      F: 12.64
      P: <0.01
      
      ObsID: 015
      MeasureID: CC2 area
      GroupID: HC, BPD
      CovariateID: TCV
      StatID: ANOVA
      F: 5.18
      P: 0.03
      
    13. For CC1, area measurement found age (F=5.28, p=0.03) to be a significant covariate.
      ObsID: 013
      MeasureID: CC1 area
      GroupID: HC, BPD
      CovariateID: age
      StatID: ANOVA
      F: 5.28
      P: 0.03
      
    14. There was no significant difference between the younger BPD the younger HC.
      ObsID: 012
      ObsType: GroupComparison
      GroupID: BPD_young, HC_young
      MeasureID: CC Vol
      StatID: TTEST
      P: not significant
      
    15. Volumetric and area measurements found that the older HC (15.5 cc) had significantly larger total CC than the younger HC group (13.1 cc), whereas there was not a significant difference among the BPD age groups (13.6 and 13.7 cc).
      ObsID: 006
      MeasureID: CC vol
      GroupID: HC_old, HC_young
      CovariateID: Group
      StatID: TTEST
      P: significant
      
      ObsID: 007
      MeasureID: CC vol
      GroupID: HC_old
      MeanValue: 15.5
      Units: cc
      
      ObsID: 008
      MeasureID: CC vol
      GroupID: HC_young
      MeanValue: 13.1
      Units: cc
      
      ObsID: 009
      MeasureID: CC Vol
      GroupID: BPD_old, BPD_young
      CovariateID: Group
      StatID: TTEST
      P: not significant
      
      ObsID: 010
      MeasureID: CC area
      GroupID: BPD_old
      MeanValue: 13.6
      Units: cc
      
      ObsID: 011
      MeasureID: CC vol
      GroupID: BPD_young
      MeanValue: 13.7
      Units: cc
      
    16. The youths with BPD had a mean MRS score of 20.8±9.5 (range 0–38)
      ObsID: 001
      GroupID: BPD
      MeasureID: MRS
      StatID: Descriptive
      Mean: 20.8
      Std: 9.5
      RangeMin: 0
      RangeMax: 38
      
    17. the CC is divided into seven subregions which include: rostrum (CC1), genu (CC2), anterior body (CC3), midbody (CC4), posterior body (CC5), isthmus (CC6) and splenium (CC7) using anterior and posterior definitions as described in Witelson
      AnalysisMethodID: CC area
      Inputs: MRI
      Software: proc_cc
      OutputVariables: CC area, CC1 area, CC2 area, CC3 area, CC4 area, CC5 area, CC6 area, CC7 area
      MeasurementType: Regional area
      MeasurementUnits: cm2 (square centimeters)
      
    18. Volu-metric assessment of the CC is provided as a distinct subset of regions within this WM parcellation system.
      AnalysisMethodID: CC Volume
      Inputs: MRI
      Software: WM_Parc
      OutputVariables: CC Vol, CC1 Vol, CC2 Vol, CC3 Vol, CC4 Vol, CC5 Vol, CC6 Vol, CC7 Vol
      MeasurementType: Regional Volume
      MeasurementUnits: cc (cubic centimeters)
      
    19. Data from 66 participants (44 children with DSM-IV BPD, age 10.6±3.0 years (mean ± SD) and 22 HC, age 10.5±3.1 years (mean ± SD) are included in this report
      GroupID: BPD
      N: 44
      AgeMean: 10.6
      AgeStd: 3.0
      
      GroupID: HC
      N: 22
      AgeMean: 10.5
      AgeStd: 3.1
      
    20. Measures of current psychopathology were obtained using the Mania Rating Scale (MRS) (Young et al. 1978) and Global Assessment of Functioning scale (GAF

      MeasureID: MRS
      MeasureDescription: Mania Rating Scale (MRS)
      MeasureDomain: current psychopathology
      MeasureReference: Young et al. 1978

      MeasureID: GAF MeasureDescription: Global Assessment of Functioning scale (GAF) MeasureDomain: current psychopathology MeasureReference: American Psychiatric Association 1994)

    21. Individuals with BPD were diagnosed using DSM-IV criteria in semi-structured and clinical interviews; HC participants had no DSM-IV Axis I diagnoses or a family history of mood or psychotic disorders in first-degree relatives, based on parental interview.
      GroupID: BPD
      Dx: Bipolar Disorder
      DxStandard: DSM-IV
      DxMethod: semi-structured and clinical interviews
      
      GroupID: HC
      GroupCharacteristic: no DSM-IV Axis I diagnoses or a family history of mood or psychotic disorders in first-degree relatives, based on parental interview
      
      
    22. youths with BPD were hypothesized to have reduced callosal growth in posterior regions.
    23. We hypothesized that (1) posterior callosal volumes would be reduced in youths with BPD given that these structures are rapidly maturing during childhood and adolescence and as a result may be more vulnerable during this time of significant brain myelination and pruning, particularly around the time of illness onset; (2) consistent with the hypothesized role of the prefrontal cortex in mood dysregulation and cognitive abnormalities in BPD (Soares and Mann 1997; Wilder-Willis et al. 2001), genual volume was expected to be abnormal (i.e., smaller) in youths with BPD; and (3) consistent with Brambilla and colleagues’ findings, there would be an absence of typical age-related changes in specific callosal volumes in youths with BPD (Brambilla et al. 2003).
    24. The present study represents the first application of a comprehensive set of measures that permit the comparison of total CC and callosal subregion areas and volumes in youths with BPD and HC.
    1. Significant diagnostic differences were seen in the left and right cerebral volumes in interaction with sex (right: F3,93 = 2.9, P = .04; left: F3,93 = 3.1, P = .04).
      ObservationID: 001
      ObservationDepVar: Diagnostic Groups
      ObervationIndVar: Left Cerebral volume
      ObservationStat: what test was run? Pointer to StatisticMethod
      ObservationStatP: 0.04
      ObservationStatF: 3.1
      ObservationStatFDOF: 3
      ObservationStatFN: 93
      LinktoSourcedata: ???
      LinktoStatExec: ???
      
      ObservationID: 002
      ObservationDepVar: Diagnostic Groups
      ObervationIndVar: Right Cerebral volume
      ObservationStat: what test was run? Pointer to StatisticMethod
      ObservationStatP: 0.04
      ObservationStatF: 2.9
      ObservationStatFDOF: 3
      ObservationStatFN: 93
      
    2. McLean Hospital Brain Imaging Center on a 1.5 Tesla General Electric Signa Scanner
      AcquisitionType: MRI
      Location: McLean Hospital Brain Imaging Center
      MRField: 1.5 Tesla
      Manufacturer: General Electric 
      Model: Signa
      
  20. Feb 2016
    1. ouths with BPD without psychosis had a significant inverse correlation between the MRS score and amygdala volumes (right: r = –0.411, P = .02; left: r = –0.379, P = .004). No significant correlations were found in the BPD with psychosis group.

      result

    2. In the youths with SZ, there was a significant inverse correlation between GAS score and left amygdala volume (r = –0.634, P = .011). Also, there was a significant correlation between MRS scores and the right NA (r = 0.634, P = .03).

      result

    3. HCs had increasing volumes with age in the thalamus (right: r = 0.38, P = .04; left: r = 0.36, P = .06). In addition, the right amygdala volume correlated with GAS scores in the HCs (r = 0.470, P = .01).

      result

    4. significant sex differences were observed in bilateral cerebrum and pallidum volumes across groups, with females having significantly smaller volumes than males.

      result

    5. The asymmetry indices for all structures also did not differ significantly between groups.

      result

    6. There were no between-group differences in the amygdala; however, there was significant diagnostic-by-sex interaction in the left amygdala (F3,93 = 3.0, P = .04). SZ males had the smallest left amygdala volume (effect size relative to other males = 0.65–1.23); this structure was actually enlarged relative to HC in the BPD groups

      ObservationID: ObservationDepVar: ObervationIndVar: ObservationType: ObservationQualitative: LinktoSourcedata:

    7. For the subcortical structures, the omnibus statistics showed no diagnostic differences in the hippocampus but did show a trend for diagnostic-by-sex differences in the left hippocampus (F3,93 = 2.3, P = .08); post hoc analyses showed that the diagnostic reduction was particularly marked in the female patient groups

      ObservationID: ObservationDepVar: ObervationIndVar: ObservationType:<br> ObservationQualitative:<br> LinktoSourcedata:

    8. Post hoc comparisons showed that both bipolar groups (with and without psychosis) had significantly smaller left and right cerebral volumes than HCs; this difference was even more marked in the female BPD groups. The SZ group did not differ significantly from the other groups.

      ObservationID: 003 ObservationDepVar: BPDwoPSY vs. HC ObervationIndVar: Right Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 004 ObservationDepVar: BPDwPSY vs. HC ObervationIndVar: Right Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 005 ObservationDepVar: BPDwoPSY vs. HC ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 006 ObservationDepVar: BPDwPSY vs. HC ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 007 ObservationDepVar: Female BPDwoPSY vs. Female HC ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 008 ObservationDepVar: Female BPDwPSY vs. Female HC ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: smaller volume LinktoSourcedata:

      ObservationID: 009 ObservationDepVar: SZ vs. HC ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

      ObservationID: 010 ObservationDepVar: SZ vs. BPDwoPSY ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

      ObservationID: 011 ObservationDepVar: SZ vs. BPDwPSY ObervationIndVar: Left Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

      ObservationID: 012 ObservationDepVar: SZ vs. HC ObervationIndVar: Right Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

      ObservationID: 013 ObservationDepVar: SZ vs. BPDwoPSY ObervationIndVar: Right Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

      ObservationID: 014 ObservationDepVar: SZ vs. BPDwPSY ObervationIndVar: Right Cerebral volume ObservationType: Post hoc ObservationQualitative: same LinktoSourcedata:

    9. 35 youths with BPD I without psychosis (mean age = 10.4 ± 3.0 years), 19 with BPD I with psychosis (mean age = 11.6 ± 2.6 years), 20 with SZ or schizoaffective disorder (mean age = 13.5 ± 2.9 years), and 29 HCs (mean age = 10.5 ± 2.9 years). The proportion of males in each group ranged from 47.4% to 58.8%

      SubjectGroup: BPDwoPSY N: 35 Diag: BPD I without psychosis MeanAge: 10.4 AgeSTD: 3.0

      SubjectGroup: BPDwPSY Diag: BPD I with psychosis N: 19 MeanAge: 11.6 AgeSTD: 2.6

      SubjectGroup: SZ Diag: SZ or schizoaffective disorder N: 20 MeanAge: 13.5 AgeSTD: 2.9

      SubjectGroup: HC Diag: Healthy Control N: 29 MeanAge: 10.5 AgeSTD: 2.9

    10. Differences in right and left subcortical brain volumes were evaluated using 2-way (diagnosis, sex) univariate analyses covarying for TCV and age. Similar models were also evaluated on the asymmetry index for each structure, which was calculated as (right volume−left volume)/(right volume + left volume)÷ 2. Post hoc between-group tests were corrected for multiple comparisons using the Tukey-Cramer honestly significant difference method. Differences in demographic and clinical variables between groups were assessed using analyses of variance for continuous variables and chi-square tests for categorical variables. In addition, within-group Pearson and Spearman correlations were performed on clinical variables and those structures which were found to be significantly different between diagnostic groups. These clinical variables included MRS and GAF scores, age at onset of illness, duration of illness, and chlorpromazine equivalents. In an effort to be conservative, we report only clinical correlations that reached significance on both Spearman and Pearson tests; the r and P value for the Pearson correlations are reported. Effect sizes were calculated and interpreted using Cohen d statistic. All statistical tests were 2 sided with alpha = .05. JMP 7 for Mac (SAS Institute, Cary, NC) was used for statistical analysis.

      Statistical Method - Steve to add formalisms... But, I guess it might include:_

      StatSoftare: JMP StatSoftwareOS: Mac StatSoftwareVersion: 7 StatSoftwareManufacturer: SAS Institute StatSoftwareManufacturerLocation: Cary, NC

    11. The caudate was measured in its entirety (head, body, tail superior to ventricular trigone, and ventral striatum), defined superomedially by the interface with the lateral ventricles, inferiorly by the interface with the adjacent rostral peduncle of the thalamus (when present), and otherwise by the interface with adjacent white matter; putamen was defined medially by the external medullary lamina of the globus pallidus, laterally by the external capsule, and otherwise by adjacent white matter; globus pallidus was defined superomedially by the interface with the internal capsule, inferiorly by the anterior commissure, ansa lenticularis, or nucleus basalis, when present, and laterally by the external medullary lamina.43 The NA was separated from putamen and caudate superiorly by a segmentation line that connects the inferiormost tip of the lateral ventricle to the most ventral point of the internal capsule at the level of the ventral putamen. From this last point, a vertical line is drawn to define the lateral border with the putamen.45

      Anatomic analysis method details - caudate

    12. The amygdala and hippocampus were defined as a continuous gray matter structure in the primary segmentation. The hippocampus was then separated from the amygdala at the rostral-coronal plane, where the hippocampus first appears. The segmentation of the amygdala was performed manually in its entirety. The cross-referencing capability of Cardviews was used to outline the amygdala in axial and sagittal views, allowing a reliable preliminary separation of the amygdala from surrounding gray structures. The anterior portion of the amygdala was segmented because it appears beneath the medial temporal cortex. The choroidal fissure was used as the superior border of the amygdala along with the gray-white matter contrast between the amygdala and the surrounding white matter. The lateral border was defined using the gray-white matter contrast between the amygdala and the surrounding temporal white matter and the gray-CSF contrast between the amygdala and temporal horn of the lateral ventricle. The medial borders consisted of the parahippocampal cortex, the brain exterior at the inferior lip of the choroidal fissure, and partially the hippocampus. Finally, the inferior border consisted of the gray-white matter contrast between the amygdala and the surrounding temporal white matter and the alveus of the hippocampus and temporal horn of the lateral ventricle.

      Anatomic analysis method details - amygdala and hippocampus

    13. Segmentation of the thalamus traced the trajectory of the hypothalamic fissure in the sagittal plane to separate the thalamus proper from the ventral diencephalon. The structure was bounded medially by the third ventricle and laterally by the internal capsule. The superior border was the body of the lateral ventricle, and the inferior border was the hypothalamic fissure.

      Anatomic analysis method details - thalamus

    14. using Cardviews software.

      OutcomeType: Volume AnalysisSoftware: Cardviews AnalysisSoftwareLink: http:...