Search

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).

Background: Depression and anxiety are prevalent in older adults with cancer but are often undertreated. Older adults are also at increased risk of chemotherapy toxicity (CT). This study evaluated the impact of depression and anxiety symptoms on severe CT risk in older adults with cancer. Methods: This is a secondary analysis of a randomized trial (2:1) evaluating geriatric assessment–driven intervention (GAIN) versus standard of care (SOC) to reduce grade 3+ CT in older adults with cancer. Mental health was assessed via the Mental Health Inventory 13. CT was graded by National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. Results: A total of 605 patients enrolled (402 GAIN; 203 SOC). Overall, 35% were depressed and 47% were anxious. Patients with depression had increased CT in the SOC arm (70.7% vs. 54.3%; p =.02) but not in the GAIN arm (54.3% vs. 48.5%; p =.27). CT was more likely in SOC patients with depression (odds ratio [OR], 2.03; 95% CI, 1.10–3.72). This association persisted after adjusting for Cancer and Aging Research Group toxicity score (OR, 1.98; 95% CI, 1.07–3.65) and for demographic, disease, and treatment factors (OR, 2.00; 95% CI, 1.03–3.85). Depression and CT were not associated in the GAIN arm (OR, 1.26; 95% CI, 0.84–1.91). Anxiety and CT were not associated in either arm. Conclusions: Elevated depression symptoms are associated with increased risk of severe CT in older adults with cancer, which was mitigated with GAIN. This suggests that treating depression symptoms may lower toxicity risk. Future studies are needed to confirm and investigate the impact of depression‐specific interventions on outcomes. Depression symptoms are associated with increased risk of severe chemotherapy toxicities in older adults with cancer. This risk is mitigated in those who receive geriatric assessment–driven interventions. [ABSTRACT FROM AUTHOR]

Objective: Neurocognitive evaluations are commonly integrated with clinical assessment to evaluate adult Attention Deficit Hyperactivity Disorder (ADHD). Study goal is to identify measures most strongly related to ADHD diagnosis and to determine their utility in screening processes. Participants: 230 students who were evaluated at the Vanderbilt University Psychological and Counseling Center between July 2013 and October 2015. Methods: We retrospectively examined charts, including clinical diagnosis, family history, childhood parental reported and current self-reported ADHD symptoms, psychiatric comorbidities, and continuous performance test (CPT). Result: Positive report of childhood and current ADHD symptoms, and lack of comorbid psychiatric symptoms were strongly associated with clinical diagnosis. CPT results were not associated with an ADHD diagnosis. The absence of reported childhood and current ADHD symptoms may serve as a contradictory marker for ADHD diagnosis. Conclusion: Clinical assessment of ADHD symptoms and ADHD childhood history, but not CPT, contributes to an accurate diagnosis of ADHD in college-aged adults.

Background: Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks. Methods: Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks. Results: In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity. Conclusions: Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis. Registration: ClinicalTrials.gov NCT02332291 [ABSTRACT FROM AUTHOR]