The ability to decode letters into language sounds is essential for reading success, and accurate identification of children at high risk for decoding impairment is critical for reducing the frequency and severity of reading impairment. We examined the utility of behavioral (standardized tests), and functional and structural neuroimaging measures taken with children at the beginning of a school year for predicting their decoding ability at the end of that school year. Specific patterns of brain activation during phonological processing and morphology, as revealed by voxel-based morphometry (VBM) of gray and white matter densities, predicted later decoding ability. Further, a model combining behavioral and neuroimaging measures predicted decoding outcome significantly better than either behavioral or neuroimaging models alone. Results were validated using cross-validation methods. These findings suggest that neuroimaging methods may be useful in enhancing the early identification of children at risk for poor decoding and reading skills.

Individuals with Williams syndrome (WS) demonstrate an abnormally positive social bias. However, the neural substrates of this hypersociability, i.e., positive attribution bias and increased drive toward social interaction, have not fully been elucidated. Methods: We performed an event-related functional magnetic resonance imaging study while individuals with WS and typically developing controls (TD) matched positive and negative emotional faces. WS compared to TD showed reduced right amygdala activation during presentation of negative faces, as in the previous literature. In addition, WS showed a unique pattern of right orbitofrontal cortex activation. While TD showed medial orbitofrontal cortex activation in response to positive, and lateral orbitofrontal cortex activation to negative, WS showed the opposite pattern. In light of the general notion of a medial/lateral gradient of reward/punishment processing in the orbitofrontal cortex, these findings provide an additional biological explanation for, or correlate of positive attribution bias and hypersociability in WS.

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Aims:The remission rates for patients with major depressive disorder (MDD) during algorithm-guided treatment (AGT), which consisted of four treatment strategy steps were prospectively compared with treatment as usual (TAU). Methods:The remission rates of patients with mild or moderate MDD during AGT (n = 83) were compared with TAU (n = 127). Results:The remission rate in the AGT group (60.2%) was approximately 10% greater than that in the TAU group (49.7%). The median number of days to achieve remission in the AGT group (93 days) was half as long as that in the TAU group (191 days). The hazard ratio of remission was 1.5 (95% confidence interval: = 1.2-1.8). A higher rate of lithium augmentation in the AGT group (20.5%) compared to theTAU (4.7%) may have led to the greater remission rate. Most participants who did not achieve remission either during the initial or second treatment steps dropped out from AGT.Conclusions: AGT may be superior to TAU for patients with mild or moderate MDD, based on the remission rates achieved. The later treatment steps in the AGT, however, were rarely utilized because participants who did not receive any benefit dropped out early.Key words: algorithm-guided treatment, clinical outcome, lithium augmentation, major depression, treatment as usual. P HYSICIANS SHOULD TRY to treat patients with psychiatric disorders logically or on the basis of scientific evidence. Physicians often prescribe medication, however, based on their personal experience rather than evidence-based information. As a consequence there is much individual variation among physicians, and some strategies may be ineffective and therefore prolong symptoms that otherwise might disappear if proven medications were adopted. This is especially true for the treatment of major depressive disorder (MDD) because of the complexity of the symptoms and the clinical course. A possible method for overcoming these problems may be a systematic treatment algorithm with the optimal application, sequencing, and appropriate decision making based on scientific evidence.Treatment algorithms provide three types of guidance: (i) strategies as to what treatments to use; (ii) tactics on how to implement the treatments; and (iii) treatment steps in a prescribed order to implement the different treatments.1 The critical decision points are defined in the course of treatment, at which time the therapeutic response is to be assessed. On the basis of this assessment, specific treatment revisions are recommended according to the preset if-then rules. Treatment algorithms should determine for how long a single treatment is maintained and how rapidly the treatment plan can be revised.1 Toofrequent decision points bear the risk of not allowing enough time for a specific treatment to show