Effect of L-DOPA on mood and nausea.

<p>(Means±SEM).</p><p>A = significantly different from placebo, B = significantly different from baseline, p<0.05, C = significantly different from 100 mg L-DOPA group at T4.</p

The effect of dopamine-enhancing agents on positive mood states in healthy humans.

<p>For the purpose of this table, measures of positive mood include the ARCI MBG subscale, POMS “Elated” subscale, and the VAS items “High,” “Rush,” “Euphoria,” “Contentedness,” “Like Drug,” and “Good Effects.” Abbreviations: AMS, Adjective Mood Scale. ARCI, Addiction Research Center Inventory. NR, not reported. 0, No change. PANAS, Positive and Negative Affect Scales. POMS, Profile of Mood States. VAS, visual analog scales. STAI, State Trait Anxiety Inventory.</p

Demographic and Mean Response Times, Error Rate, and Event-Related Potential Trial Summary Data.

<p>Estimates for behavioral and stimulus-locked measures contain data from 12 males; estimates for response-locked measures contain data from 11 males. Congru ent and incongruent trials retained and correct and error trials retained indicate the number of trials retained for averaging following artifact correction and rejection. APTD = acute phenylalanine and tyrosine depletion; BAL = balance amino acid mixture; RT = response time (in msec)</p><p>Demographic and Mean Response Times, Error Rate, and Event-Related Potential Trial Summary Data.</p

Event-Related Potential Amplitude (μV) and Latency (msec) Summary Data.

<p>Note. Estimates for behavioral and stimulus-locked measures contain data from 12 males; estimates for response-locked measures contain data from 11 males. APTD = acute phenylalanine and tyrosine depletion; BAL = balance amino acid mixture; conflict SP = conflict slow potential; CRN = correct-related negativity; ERN = error-related negativity; Pc = correct positivity; Pe = error positivity</p><p>Event-Related Potential Amplitude (μV) and Latency (msec) Summary Data.</p

The Effects of Acute Dopamine Precursor Depletion on the Cognitive Control Functions of Performance Monitoring and Conflict Processing: An Event-Related Potential (ERP) Study - Fig 2

<p>Grand averaged A) N450 and B) conflict slow potential (SP) waveforms of stimulus-locked congruent and incongruent trials averaged across fronto-medial electrode sites for the N450 and parietal electrode sites for the conflict SP. C) error-related negativity (ERN) and D) error positivity (Pe) waveforms of response-locked correct and incorrect trials averaged across fronto-medial electrode sites for the ERN and centro-parietal electrode sites for the Pe.</p

Percentage of baseline A) amino acid and B) prolactin levels during APTD and BAL conditions.

<p>Percentage of baseline A) amino acid and B) prolactin levels during APTD and BAL conditions.</p

Effect of Acute Phenylalanine and Tyrosine Depletion (APTD) on Non-Transformed (A) and Log Transformed (B) Progressive Ratio (PR) Exercise Breakpoint Scores in Individuals Recovered from Anorexia Nervosa (AN REC, N = 17) and Healthy Controls (HC, N = 15).

<p>Relative to the balanced condition (BAL), APTD resulted in a significant decrease in PR breakpoint scores among HC only (HC BAL: 1.18 ± 0.76; HC APTD: 0.39 ± 0.68). Relative to HC, AN REC performed significantly more on the PR task during both BAL (1.70 ± 0.61) and APTD (1.74 ± 0.44). In AN REC, raw PR exercise breakpoint scores for were 113.53 ± 200.56 during BAL and 107.94 ± 200.76 during APTD. In HC, they were 36.87 ± 26.38 during BAL and 8.00 ± 14.49 during APTD. Data are expressed as Means ± SD. **<i>P ≤ 0</i>.<i>01</i> *<i>P ≤ 0</i>.<i>05</i>. ANOVA: analysis of variance. SD: standard deviation.</p

Overview of the time course of an experimental session of Study 1.

<p>All participants performed two sessions: in one session they ingested 600 mg sulpiride, in the other one a placebo. In each session, participants performed pre- and post-drug testing, separated by a 3.5h waiting period.</p

Replication using alternative parcellation.

The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity (“connectivity modes”) derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes—namely correlated gene expression and receptor similarity—that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.</div

Contributions of connectivity modes to disease vulnerability.

Abnormal cortical thickness profiles for 13 neurological, psychiatric, and neurodevelopmental disorders were collected from the ENIGMA consortium (cortex plots shown in panel b; N = 21,000 patients, N = 26,000 controls [79,80]). (a) Given a specific disorder and connectivity mode, dj represents the abnormal cortical thickness of region j, and cij represents the edge weight (similarity) between regions i and j. For every region i, we calculate the average abnormal cortical thickness of all other regions j≠i in the network, weighted by the edge strength (“disease exposure”; note that we omit negative connections, such that Ni represents the number of positive connections made by region i). Next, we correlate disease exposure and regional abnormal cortical thickness across cortical regions (scatter plot; points represent cortical regions). We show the connectivity profiles of 2 example regions (highlighted in purple in the left brain network and orange in the right brain network). (b) The analytic workflow presented in panel (a) is repeated for each disorder and connectivity mode, and we visualize Spearman correlations in a heatmap. (c) This analysis is repeated for weighted structural connectivity (where we only consider structurally connected regions), and Euclidean distance (where we always consider all regions in the network). We also repeat this analysis for the fused network (see Fusing connectivity modes), and results are shown in S6 Fig. The data underlying this figure can be found at https://github.com/netneurolab/hansen_many_networks. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; ENIGMA, Enhancing Neuroimaging Genetics through Meta-Analysis; MDD, major depressive disorder; OCD, obsessive-compulsive disorder.</p