Computerized Cognitive Training and 24-Month Mortality in Heart Failure.

BACKGROUND: Cognitive dysfunction predicts mortality in heart failure (HF). Computerized cognitive training (CCT) has shown preliminary efficacy in improving cognitive function. However, the relationship between CCT and mortality is unclear. Aims were to evaluate (1) long-term efficacy of CCT in reducing 24-month mortality and (2) age, HF severity, global cognition, memory, working memory, depressive symptoms, and health-related quality of life as predictors of 24-month mortality among patients with HF. METHODS: In this prospective longitudinal study, 142 patients enrolled in a 3-arm randomized controlled trial were followed for 24 months. Logistic regression was used to achieve the aims. RESULTS: Across 24 months, 16 patients died (CCT, 8.3%; control groups, 12.8%). Computerized cognitive training did not predict 24-month mortality (odds ratio [OR], 0.65). Older age (OR, 1.08), worse global cognition (OR, 0.73), memory (OR, 0.81), and depressive symptoms (OR, 1.10) at baseline predicted 24-month mortality. CONCLUSIONS: Efficacious interventions are needed to improve global cognition, memory, and depressive symptoms and reduce mortality in HF.

Atrial Fibrillation and Older Age Predict Serum Brain-Derived Neurotrophic Factor Levels Among Patients With Heart Failure.

BACKGROUND: Predictors have not been determined of serum brain-derived neurotrophic factor (BDNF) levels among patients with heart failure (HF). OBJECTIVE: The primary purpose was to evaluate history of atrial fibrillation, age, gender, and left ventricular ejection fraction as predictors of serum BDNF levels at baseline, 10 weeks, and 4 and 8 months after baseline among patients with HF. METHODS: This study was a retrospective cohort analyses of 241 patients with HF. Data were retrieved from the patients' health records (coded history of atrial fibrillation, left ventricular ejection fraction), self-report (age, gender), and serum BDNF. Linear multiple regression analyses were conducted. RESULTS: One hundred three patients (42.7%) had a history of atrial fibrillation. History of atrial fibrillation was a significant predictor of serum BDNF levels at baseline (ß = -0.16, P = .016), 4 months (ß = -0.21, P = .005), and 8 months (ß = -0.19, P = .015). Older age was a significant predictor at 10 weeks (ß = -0.17, P = .017) and 4 months (ß = -0.15, P = .046). CONCLUSIONS: Prospective studies are needed to validate these results. Clinicians need to assess patients with HF for atrial fibrillation and include treatment of it in management plans.

Dimension of Activity in Random Neural Networks.

Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires knowledge of the structure of this coordinated activity, information contained, for example, in cross covariances between units. Self-consistent dynamical mean field theory (DMFT) has elucidated several features of random neural networks-in particular, that they can generate chaotic activity-however, a calculation of cross covariances using this approach has not been provided. Here, we calculate cross covariances self-consistently via a two-site cavity DMFT. We use this theory to probe spatiotemporal features of activity coordination in a classic random-network model with independent and identically distributed (i.i.d.) couplings, showing an extensive but fractionally low effective dimension of activity and a long population-level timescale. Our formulas apply to a wide range of single-unit dynamics and generalize to non-i.i.d. couplings. As an example of the latter, we analyze the case of partially symmetric couplings.

Influence of amyloid and diagnostic syndrome on non-traditional memory scores in early-onset Alzheimer's disease.

INTRODUCTION: The Rey Auditory Verbal Learning Test (RAVLT) is a useful neuropsychological test for describing episodic memory impairment in dementia. However, there is limited research on its utility in early-onset Alzheimer's disease (EOAD). We assess the influence of amyloid and diagnostic syndrome on several memory scores in EOAD. METHODS: We transcribed RAVLT recordings from 303 subjects in the Longitudinal Early-Onset Alzheimer's Disease Study. Subjects were grouped by amyloid status and syndrome. Primacy, recency, J-curve, duration, stopping time, and speed score were calculated and entered into linear mixed effects models as dependent variables. RESULTS: Compared with amyloid negative subjects, positive subjects exhibited effects on raw score, primacy, recency, and stopping time. Inter-syndromic differences were noted with raw score, primacy, recency, J-curve, and stopping time. DISCUSSION: RAVLT measures are sensitive to the effects of amyloid and syndrome in EOAD. Future work is needed to quantify the predictive value of these scores. HIGHLIGHTS: RAVLT patterns characterize various presentations of EOAD and EOnonAD Amyloid impacts raw score, primacy, recency, and stopping time Timing-based scores add value over traditional count-based scores.

Randomized Controlled Trial of a Cognitive Intervention to Improve Memory in Heart Failure.

BACKGROUND: The objective of this 3-arm randomized controlled trial was to evaluate the efficacy of computerized cognitive training (CCT) in improving primary outcomes of delayed-recall memory and serum brain-derived neurotrophic factor (BDNF) levels; and the secondary outcomes were working memory, instrumental activities of daily living (IADLs) and health-related quality of life (HRQL) in patients with heart failure (HF). METHODS AND RESULTS: Patients (n = 256) were randomly assigned to 8 weeks of CCT using BrainHQ, computerized crossword puzzles active control intervention, and usual care. All patients received weekly nurse-enhancement interventions. Data were collected at enrollment and baseline visits and at 10 weeks and 4 and 8 months. In mixed effects models, there were no statistically significant group or group-by-time differences in outcomes. There were statistically significant differences over time in all outcomes in all groups. Patients improved over time on measures of delayed-recall memory, working memory, IADLs, and HRQL and had decreased serum BDNF. CONCLUSIONS: CCT did not improve outcomes compared with the active control intervention and usual care. Nurse-enhancement interventions may have led to improved outcomes over time. Future studies are needed to test nurse-enhancement interventions in combination with other cognitive interventions to improve memory in persons with HF.

Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease.

Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.

Sequencing and analysis of gerbera daisy leaf transcriptomes reveal disease resistance and susceptibility genes differentially expressed and associated with powdery mildew resistance.

BACKGROUND: RNA sequencing has been widely used to profile genome-wide gene expression and identify candidate genes controlling disease resistance and other important traits in plants. Gerbera daisy is one of the most important flowers in the global floricultural trade, and powdery mildew (PM) is the most important disease of gerbera. Genetic improvement of gerbera PM resistance has become a crucial goal in gerbera breeding. A better understanding of the genetic control of gerbera resistance to PM can expedite the development of PM-resistant cultivars. RESULTS: The objectives of this study were to identify gerbera genotypes with contrasting phenotypes in PM resistance and sequence and analyze their leaf transcriptomes to identify disease resistance and susceptibility genes differentially expressed and associated with PM resistance. An additional objective was to identify SNPs and SSRs for use in future genetic studies. We identified two gerbera genotypes, UFGE 4033 and 06-245-03, that were resistant and susceptible to PM, respectively. De novo assembly of their leaf transcriptomes using four complementary pipelines resulted in 145,348 transcripts with a N50 of 1124 bp, of which 67,312 transcripts contained open reading frames and 48,268 were expressed in both genotypes. A total of 494 transcripts were likely involved in disease resistance, and 17 and 24 transcripts were up- and down-regulated, respectively, in UFGE 4033 compared to 06-245-03. These gerbera disease resistance transcripts were most similar to the NBS-LRR class of plant resistance genes conferring resistance to various pathogens in plants. Four disease susceptibility transcripts (MLO-like) were expressed only or highly expressed in 06-245-03, offering excellent candidate targets for gene editing for PM resistance in gerbera. A total of 449,897 SNPs and 19,393 SSRs were revealed in the gerbera transcriptomes, which can be a valuable resource for developing new molecular markers. CONCLUSION: This study represents the first transcriptomic analysis of gerbera PM resistance, a highly important yet complex trait in a globally important floral crop. The differentially expressed disease resistance and susceptibility transcripts identified provide excellent targets for development of molecular markers and genetic maps, cloning of disease resistance genes, or targeted mutagenesis of disease susceptibility genes for PM resistance in gerbera.

Parts-Prospecting for a High-Efficiency Thiamin Thiazole Biosynthesis Pathway.

Plants synthesize the thiazole precursor of thiamin (cThz-P) via THIAMIN4 (THI4), a suicide enzyme that mediates one reaction cycle and must then be degraded and resynthesized. It has been estimated that this THI4 turnover consumes 2% to 12% of the maintenance energy budget and that installing an energy-efficient alternative pathway could substantially increase crop yield potential. Available data point to two natural alternatives to the suicidal THI4 pathway: (i) nonsuicidal prokaryotic THI4s that lack the active-site Cys residue on which suicide activity depends, and (ii) an uncharacterized thiazole synthesis pathway in flowers of the tropical arum lily Caladium bicolor that enables production and emission of large amounts of the cThz-P analog 4-methyl-5-vinylthiazole (MVT). We used functional complementation of an Escherichia coli ΔthiG strain to identify a nonsuicidal bacterial THI4 (from Thermovibrio ammonificans) that can function in conditions like those in plant cells. We explored whether C. bicolor synthesizes MVT de novo via a novel route, via a suicidal or a nonsuicidal THI4, or by catabolizing thiamin. Analysis of developmental changes in MVT emission, extractable MVT, thiamin level, and THI4 expression indicated that C. bicolor flowers make MVT de novo via a massively expressed THI4 and that thiamin is not involved. Functional complementation tests indicated that C. bicolor THI4, which has the active-site Cys needed to operate suicidally, may be capable of suicidal and - in hypoxic conditions - nonsuicidal operation. T. ammonificans and C. bicolor THI4s are thus candidate parts for rational redesign or directed evolution of efficient, nonsuicidal THI4s for use in crop improvement.

Patient and Caregiver Assessment of the Benefits From the Clinical Use of Amyloid PET Imaging.

INTRODUCTION: Few studies to date have explored patient and caregiver views on the clinical use of amyloid positron emission tomography (PET). METHODS: A 7-item questionnaire assessing patient and caregiver views (510 total respondents) toward amyloid PET imaging was advertised broadly through alz.org/trialmatch. RESULTS: We received 510 unique responses from 48 US states, 2 Canadian provinces, the Dominican Republic, and Greece. Both patients and caregivers indicated that they would want to receive amyloid imaging if offered the opportunity. Over 88% of respondents had a positive response (∼10% with neutral and 2% with negative responses) to whether amyloid PET should be offered routinely and be reimbursed. Such information was felt to be useful for long-term legal, financial, and health care planning. Respondents identifying with early age cognitive decline (younger than 65 y) were more likely to explore options for disability insurance (P=0.03). Responders from the Midwest were more likely to utilize information from amyloid imaging for legal planning (P=0.02), disability insurance (P=0.02), and life insurance (P=0.04) than other US regions. DISCUSSION: Patients and caregivers supported the use of amyloid PET imaging in clinical practice and felt that the information would provide significant benefits particularly in terms of future planning.

Structure of activity in multiregion recurrent neural networks.

Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational flexibility. However, the nature of such multiregion dynamics and the underlying synaptic connectivity patterns remain poorly understood. Here, we study the dynamics of recurrent neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. Motivated by experimental evidence of communication subspaces between cortical areas, these networks have low-rank connectivity between regions, enabling selective routing of activity. These networks exhibit two interacting forms of dynamics: high-dimensional fluctuations within regions and low-dimensional signal transmission between regions. To characterize this interaction, we develop a dynamical mean-field theory to analyze such networks in the limit where each region contains infinitely many neurons, with cross-region currents as key order parameters. Regions can act as both generators and transmitters of activity, roles that we show are in conflict. Specifically, taming the complexity of activity within a region is necessary for it to route signals to and from other regions. Unlike previous models of routing in neural circuits, which suppressed the activities of neuronal groups to control signal flow, routing in our model is achieved by exciting different high-dimensional activity patterns through a combination of connectivity structure and nonlinear recurrent dynamics. This theory provides insight into the interpretation of both multiregion neural data and trained neural networks.