The relationship between age, anxiety, and depression in older adults with cancer.

OBJECTIVE: In older men with prostate cancer, aging is associated with reduced anxiety and increased depression. The purpose of this study was to examine the association among age, anxiety, and depression in a cohort of older adults receiving chemotherapy. METHODS: This is a secondary analysis of a prospective longitudinal study investigating chemotherapy toxicity in older adults with cancer. Baseline data (pre-chemotherapy) included: age, sociodemographics, tumor and treatment factors, functional status, comorbidities, psychological state (measured by the Hospital Anxiety and Depression Scale), and social support. Univariate and multiple regression analyses were conducted to test the relationship between age, anxiety, and depression. RESULTS: The average age of the 500 patients (56% females) was 73.1. The majority had late stage disease: 22% Stage III and 61% stage IV. Clinically significant depression was reported in 12.6%. Clinically significant anxiety was reported in 20.9%. In univariate analyses, there was no association between anxiety and age, or depression and age. In multivariable analyses, older age (p=0.05) was associated with decreased anxiety, as well as lack of social support (p<0.01) and increased number of comorbidities (p<0.01). In multivariable analysis, depression was associated with lack of social support (p<0.01), increased number of comorbidities (p<0.01), and advanced stage (p<0.01). CONCLUSIONS: This study supports previous research that anxiety decreases with age in older adults with cancer. However, depression remained constant with increasing age. Greater resources and attention to identifying and treating the psychological sequelae of cancer in older adults are warranted.

Toxicity of bevacizumab in combination with chemotherapy in older patients.

BACKGROUND: Bevacizumab leads to improved survival for patients with metastatic colorectal cancer (CRC) or non-small cell lung cancer (NSCLC) when added to chemotherapy. Little is known about factors associated with receipt of bevacizumab, or whether bevacizamab is associated with increased toxicity when added to chemotherapy. PATIENTS AND METHODS: We conducted a prospective study of patients aged ≥65 years, which evaluated the association between geriatric assessment (GA) metrics and chemotherapy toxicity. We examined differences in characteristics and outcomes of patients with CRC and NSCLC cancers who received bevacizumab with chemotherapy versus chemotherapy alone. RESULTS: From a total of 207 patients, 27 (13%) received bevacizumab plus chemotherapy and 180 (87%) received chemotherapy alone. Groups were similar in sociodemographic and cancer characteristics. There were no baseline differences in GA domains except that patients with heart disease were less likely to receive bevacizumab (4% vs. 26%, p = .01). Seventy-eight percent of patients who had bevacizumab had grade 3-5 toxicity compared to only 57% who received chemotherapy alone (p = .06). Patients receiving bevacizumab were more likely to develop grade 3 hypertension than those who received chemotherapy alone (15% vs. 2%, p < .01). In multivariable analysis, factors associated with grade 3 or more toxicity included: bevacizumab (OR: 2.86, p = .04), CRC (OR: 2.54, p < .01), and baseline anemia (OR: 2.58, p = .03). CONCLUSION: Heart disease was more common in those who did not receive bevacizumab. Older patients who receive bevacizumab with chemotherapy have a higher odds of developing a grade 3-5 toxicity compared with those who receive chemotherapy alone.

Neurofibromin regulates corticostriatal inhibitory networks during working memory performance.

Neurofibromatosis type I (NF1) is one of the most common single-gene causes of learning disabilities. Here, we use behavioral working memory probes and electrophysiological studies in a mouse model of NF1 (Nf1 heterozygous null mutants; Nf1(+/-)) to demonstrate that (i) Neurofibromin regulates prefrontal and striatal inhibitory networks, specifically activity-dependent GABA release and (ii) is required for working memory performance, with inhibition-dependent working memory deficits seen in Nf1(+/-) mice. We find that increased inhibition in medial prefrontal cortex (mPFC) is sufficient to alter persistent activity in a biophysical model of an mPFC microcircuit, suggesting a possible mechanism for Nf1(+/-) working memory deficits. Accordingly, working memory assays applied during functional MRI (fMRI) studies in human subjects with NF1 reveal hypoactivation of corticostriatal networks, which is associated with impaired working memory performance. Collectively, these integrative mouse and human studies reveal molecular and cellular mechanisms contributing to working memory deficits in NF1.

Activating mutations of TOR (target of rapamycin).

Mammalian target of rapamycin (mTOR) is a key regulator of eukaryotic cell growth. In particular, mTORC1, one of the two complexes that contain mTOR, is involved in the regulation of protein synthesis, proliferation, cell cycle and autophagy. Hyperactivation of the mTOR signaling pathway is observed in human cancer. A variety of approaches including deletion analysis, yeast genetic screens and mining of human cancer genome databases were taken that resulted in the identification of activating mutations of TOR. These studies suggest that the FAT, FRB and kinase domains are the three regions of TOR where activating mutations can be identified. Within the kinase domain, the mutations are clustered in three hot spots that are all located in the kinase active site that was deduced by the alignment with PI3K. One of the hot spots corresponds to the region where PI3K oncogenic mutations have been identified. These results are beginning to provide important insights into the mechanism of activation of mTOR.

Remember and know judgments during recognition in chronic schizophrenia.

Deficits in learning and memory are among the most robust correlates of schizophrenia. It has been hypothesized that these deficits are in part due to reduced conscious recollection and increased reliance on familiarity assessment as a basis for retrieval. The Remember-Know (R-K) paradigm was administered to 35 patients with chronic schizophrenia and 35 healthy controls. In addition to making "remember" and "know" judgments, the participants were asked to make forced-choice recognition judgments with regard to details about the learning episode. Analyses comparing response types showed a significant reduction in "remember" responses and a significant increase in "know" responses in schizophrenia patients relative to controls. Both patients and controls recalled more details of the learning episode for "remember" compared to "know" responses, although, in particular for "remember" responses, patients recalled fewer details compared with controls. Notably, patients recognized fewer inter-item but not intra-item stimulus features compared with controls. These findings suggest deficits in organizing and integrating relational information during the learning episode and/or using relational information for retrieval. A Dual-Process Signal Detection interpretation of these findings suggests that recollection in chronic schizophrenia is significantly reduced, while familiarity is not. Additionally, a unidimensional Signal Detection Theory interpretation suggests that chronic schizophrenia patients show a reduction in memory strength, and an altered criterion on the memory strength distribution for detecting new compared with old stimuli but not for detecting stimuli that are remembered versus familiar. Taken together, these findings are consistent with a deficit in recollection and increased reliance on familiarity in making recognition memory judgments in chronic schizophrenia.

Language network dysfunction as a predictor of outcome in youth at clinical high risk for psychosis.

OBJECTIVES: Language processing abnormalities are a hallmark feature of schizophrenia. Yet, no study to date has investigated underlying neural networks associated with discourse processing in adolescents at clinical high risk (CHR) for developing psychosis(1). METHODS: Forty CHR youth and 24 demographically comparable healthy controls underwent functional magnetic resonance imaging while performing a naturalistic discourse processing paradigm. We assessed differences in blood oxygenation level-dependent (BOLD) activity between task conditions (Topic Maintenance vs. Reasoning) and between groups. Furthermore, we examined the association of regional brain activity with symptom severity and social outcome at follow-up, 6 to 24 months after the scan. RESULTS: Relative to controls, CHR participants showed increased neural activity in a network of language-associated brain regions, including the medial prefrontal cortex bilaterally, left inferior frontal (LIFG; BA44/45, 47) and middle temporal gyri, and the anterior cingulate (BA24 and 32). Further, increased activity in the superior temporal gyrus (STG), caudate, and LIFG distinguished those who subsequently developed psychosis. Within the CHR sample, severity of positive formal thought disorder at follow-up was positively correlated with signal change in the LIFG, superior frontal gyrus, and inferior/middle temporal gyri, whereas social outcome was inversely correlated with signal change in the LIFG and anterior cingulate. CONCLUSIONS: These findings are consistent with a neural inefficiency hypothesis in those at greatest risk for psychosis, and additionally suggest that baseline activation differences may predict symptomatic and functional outcome. These results highlight the need to further investigate the neural systems involved in conversion to psychosis, and how language disruption changes over time in at-risk adolescents.

Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms.

BACKGROUND: No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data. METHODS: Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects. RESULTS: Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation. CONCLUSIONS: These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.