2-class Multivariate and Conventional Maps.

<p>A. Multivariate discrimination weight map for ADHD vs. Controls (unthresholded). Gaussian Process Classification classified ADHD patients and healthy controls with 82.8% and 75.9% sensitivity, respectively; leading to an overall accuracy of 79.3%. Multivariate discrimination weight-map –intensity values illustrate the relative positive weight distributions (ADHD; orange) and negative weight distributions (controls; light blue). Within each colour code, the lighter colors (i.e., light orange-yellow, light blue) indicate strongest weights for the GPC analyses and for the conventional mass-univariate case-control comparison lighter colors indicate higher p-values of structural differences. B. Multivariate discrimination weight map (thresholded). The map only shows voxels with a weight value above 40% of the maximum weight value C). Conventional mass-univariate t-statistic map. Controls had increased grey matter relative to patients, thresholded at cluster-wise p<0.001 uncorrected. No areas showed increased grey matter in ADHD relative to controls.</p

2-class multivariate weight maps.

<p>A) Multivariate discrimination weight map for ADHD vs. Controls (unthresholded). Gaussian Process Classification classified ADHD patients and healthy controls with 82.8% and 75.9% sensitivity, respectively; leading to an overall accuracy of 79.3%. Multivariate discrimination weight-map –intensity values illustrate the relative positive weight distributions (ADHD; orange) and negative weight distributions (controls; blue). Within each colour code, the lighter colors (i.e., light orange-yellow, light blue) indicate strongest weights for the GPC analyses. B) Multivariate discrimination weight map for ADHD vs. non-ADHD (unthresholded). Gaussian Process Classification classified ADHD patients and non-ADHD with 79.3% and 77.1% sensitivity, respectively; leading to an overall accuracy of 78.2%. Multivariate discrimination weight-map–intensity values illustrate the relative positive weight distributions (ADHD; orange) and negative weight distributions (non-ADHD; violet). Within each colour code, the lighter colors (i.e., light orange-yellow, light violet) indicate strongest weights for the GPC analyses. C) Multivariate discrimination weight map for ADHD vs. ASD (unthresholded). Gaussian Process Classification classified ADHD patients and ASD patients with 93.1% and 68.4% sensitivity, respectively; leading to an overall accuracy of 80.8%. Multivariate discrimination weight-map–intensity values illustrate the relative positive weight distributions (ADHD; orange) and negative weight distributions (ASD; green). Within each colour code, the lighter colors (i.e., light orange-yellow, light green) indicate strongest weights for the GPC analyses.</p

Mean predictive probabilities (with standard error to the mean) for the comparison between healthy bipolar offspring with and without future onset of Axis I disorder, which in this sample was major depressive disorder and anxiety disorders.

<p>Mean predictive probabilities (with standard error to the mean) for the comparison between healthy bipolar offspring with and without future onset of Axis I disorder, which in this sample was major depressive disorder and anxiety disorders.</p

Predictive Probabilities for the Gaussian Process Classifier discriminating ADHD and Controls.

<p>The x-axis describes the probability with which each subject is predicted to be an ADHD patient (equal to 1- the probability of being a control).</p

Demographic and Clinical Data for Participants.

<p>Data expressed as mean (SD). Abbreviations: IQ = intelligence quotient as assessed with the Wechsler Abbreviated Scale of Intelligence; ADHD = Attention Deficit Hyperactivity Disorder; ASD = Autism Spectrum Disorder; CPRS = Conners’ Parent Rating Scale; SDQ = Strengths and Difficulties Questionnaire; ADOS = Autism Diagnostic Observation Schedule; ADI = Autism Diagnostic Interview. *Post-hoc t-tests were Bonferroni corrected.</p

Global volume group differences in ADHD and controls.

<p>Data expressed as mean (SD). ADHD: Attention Deficit Hyperactivity Disorder;</p><p>GM: grey matter; WM: white matter; CSF: cerebrospinal fluid; TIV: total intracranial volume ( =  GM+WM+CSF volumes).</p

We used the predictive probabilities from the classifier at-risk adolescents vs. controls as a score for the at-risk adolescents.

<p>An ROC curve was used to evaluate if this score could be used to predict which of at-risk adolescents developed a future mood disorder. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). The area under the ROC curve (AUC) was 0.78 (p<0.05).</p

Summary of results from pattern recognition analyses.

<p>A. Decision boundary and individual predictive probabilities. B. GPC weights overlaid on an anatomical template. The color code shows the relative weight of each voxel for the decision boundary (red scales: higher weights for healthy bipolar offspring and blue scales: higher weights for healthy controls). The discriminating pattern included clusters with higher weights for healthy bipolar offspring in the superior temporal sulcus (STS; x, y, z: -50, 11, -5) and in a posterior region of the ventromedial prefrontal cortex (VMPFC(p); x, y, z,: 0, 29, -14) and a cluster with higher weights for healthy controls in the anterior region of the ventromedial prefrontal cortex (VMPFC (a); x, y, z: -2, 51, -19) (x, y, z, are in Talairach coordinates).</p

Demographic and Clinical Characteristics of Healthy Offspring Having a Parent with Bipolar Disorder and Age- and Sex- Matched Control Offspring of Healthy Parents.

<p>Abbreviations: HBO = healthy offspring having a parent diagnosed with bipolar disorder; HC = healthy control offspring of healthy parents; MFQ, Mood and Feelings Questionnaire (range, 0–68); SCARED, Screen for Childhood Anxiety and Related Disorders (range, 0–82); CALS, Child Affect Lability Scale (range, 0–80).</p

Summary of pattern recognition analyses.

<p>(1) Feature Extraction: the information from the beta images were transformed into an input vector. (2) Nested leave one out (LOO) Approach. We employed a nested (3-way) cross-validation, where we first excluded one matched pair of subjects to comprise the test set (test loop in light blue). We then performed a second split (validation loop in dark blue), where we removed 5000 voxels each iteration and repeatedly repartitioned the remaining 15 subject pairs into a validation set (1 pair) and training set (14 pairs) to compute the mean accuracy on the validation set. This procedure (removing voxels and computing mean accuracy) was repeated until all voxels were removed. We then selected the number of voxels that produced maximal accuracy on the validation set before applying it to the test set. The final accuracy was the mean accuracy over all test subjects (outer test loop in light blue). (3) We then generated a map training the GPC with all subjects and removing voxels until we obtained the mean number of voxels.</p