Purpose The aim of this study was to describe imaging markers of decision-making under uncertain conditions in normal individuals, in order to provide baseline activity to compare to impaired decision-making in pathological states. Methods In this cross-sectional study, 19 healthy subjects ages 18–35 completed a novel decision-making card-matching task using a Phillips T3 Scanner and a 32-channel head coil. Functional data were collected in six functional runs. In one condition of the task, the participant was certain of the rule to apply to match the cards; in the other condition, the participant was uncertain. We performed cluster-based comparison of the two conditions using FSL fMRI Expert Analysis Tool and network-based analysis using MATLAB. Results The uncertain > certain comparison yielded three clusters—a midline cluster that extended through the midbrain, the thalamus, bilateral prefrontal cortex, the striatum, and bilateral parietal/occipital clusters. The certain > uncertain comparison yielded bilateral clusters in the insula, parietal and temporal lobe, as well as a medial frontal cluster. A larger, more connected functional network was found in the uncertain condition. Conclusion The involvement of the insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and orbitofrontal cortex of the certain condition reinforces the notion that certainty is inherently rewarding. For the uncertain condition, the involvement of the prefrontal cortex, parietal cortex, striatum, thalamus, amygdala, and hippocampal involvement was expected, as these are areas involved in resolving uncertainty and rule updating. The involvement of occipital cortical involvement and midbrain involvement may be attributed to increased visual attention and increased motor control.

Purpose To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI). Materials and methods This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer’s Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts. Results The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi. Conclusions The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer’s disease dementia.

BACKGROUND: Opioid-dependent patients often have co-occurring chronic illnesses requiring medications that interact with methadone. Methadone maintenance treatment (MMT) is typically provided separately from medical care. Hence, coordination of medical care and substance use treatment is important to preserve patient safety. OBJECTIVE:To identify potential safety risks among MMT patients engaged in medical care by evaluating the frequency that opioid dependence and MMT documentation are missing in medical records and characterizing potential medication-methadone interactions. METHODS:Among patients from a methadone clinic who received primary care from an affiliated, but separate, medical center, we reviewed electronic medical records for documentation of methadone, opioid dependence, and potential drug-methadone interactions. The proportions of medical records without opioid dependence and methadone documentation were estimated and potential medication-methadone interactions were identified. RESULTS:Among the study subjects (n=84), opioid dependence documentation was missing from the medical record in 30% (95% CI, 20%-41%) and MMT documentation was missing from either the last primary care note or the last hospital discharge summary in 11% (95% CI, 5%-19%). Sixty-nine percent of the study subjects had at least 1 medication that potentially interacted with methadone; 19% had 3 or more potentially interacting medications.CONCLUSION: Among patients receiving MMT and medical care at different sites, documentation of opioid dependence and MMT in the medical record occurs for the majority, but is missing in a substantial number of patients. Most of these patients are prescribed medications that potentially interact with methadone. This study highlights opportunities for improved coordination between medical care and MMT.

While the relationship between diffusion tensor imaging (DTI) measurements and training effects is explored by Voelker et al. (this issue), a cursory discussion of functional magnetic resonance imaging (fMRI) measurements categorizes increased activation with findings of greater white matter integrity. Evidence of the relationship between fMRI activation and white matter integrity is conflicting, as is the relationship between fMRI activation and training effects. An examination of the changes in fMRI activation in response to training is helpful, but the relationship between DTI and fMRI activation, particularly in the context of white matter changes, must be examined further before general conclusions can be drawn.

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