Cognitive Reserve Relates to Functional Network Efficiency in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, with no means of cure or prevention. The presence of abnormal disease-related proteins in the population is, in turn, much more common than the incidence of dementia. In this context, the cognitive reserve (CR) hypothesis has been proposed to explain the discontinuity between pathophysiological and clinical expression of AD, suggesting that CR mitigates the effects of pathology on clinical expression and cognition. fMRI studies of the human connectome have recently reported that AD patients present diminished functional efficiency in resting-state networks, leading to a loss in information flow and cognitive processing. No study has investigated, however, whether CR modifies the effects of the pathology in functional network efficiency in AD patients. We analyzed the relationship between CR, pathophysiology and network efficiency, and whether CR modifies the relationship between them. Fourteen mild AD, 28 amnestic mild cognitive impairment (aMCI) due to AD, and 28 controls were enrolled. We used education to measure CR, cerebrospinal fluid (CSF) biomarkers to evaluate pathophysiology, and graph metrics to measure network efficiency. We found no relationship between CR and CSF biomarkers; CR was related to higher network efficiency in all groups; and abnormal levels of CSF protein biomarkers were related to more efficient networks in the AD group. Education modified the effects of tau-related pathology in the aMCI and mild AD groups. Although higher CR might not protect individuals from developing AD pathophysiology, AD patients with higher CR are better able to cope with the effects of pathology—presenting more efficient networks despite pathology burden. The present study highlights that interventions focusing on cognitive stimulation might be useful to slow age-related cognitive decline or dementia and lengthen healthy aging

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Depression and anxiety symptoms are associated to disruption of Default Mode Network in subacute ischemic stroke

Orientador: Li Li MinDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: Sintomas de depressão e ansiedade são comuns após um acidente vascular cerebral (AVC) e estão associados à redução na qualidade de vida e pior recuperação física e social. A Default Mode Network (DMN) é uma rede cerebral que desempenha papel importante nos processos emocionais. O presente trabalho objetivou investigar se sintomas de depressão e ansiedade no primeiro mês após AVC isquêmico estão associados a alterações na conectividade funcional da DMN. Trinta e quatro pacientes com AVC isquêmico subagudo foram submetidos a: 1) avaliação comportamental através do Inventário de Depressão de Beck (BDI), Inventário de Ansiedade de Beck (BAI) e Entrevista Clínica Estruturada para o DSM-IV - versão clínica (SCID-CV); 2) avaliação cognitiva através do Mini Exame do Estado Mental (MEEM) e Montreal Cognitive Assessment (MoCA); 3) avaliação do comprometimento funcional através do Índice de Barthel e da Escala de Rankin modificada; 4) aquisição de imagens em Ressonância Magnética de 3T (Philips Achieva®). Os pacientes com sintomas de depressão e/ou ansiedade após o AVC mostraram aumento da conectividade funcional da DMN no lúbulo parietal inferior esquerdo, no lóbulo frontal médio direito e núcleos da base esquerdo, quando comparados com pacientes apenas com AVC. Os resultados da correlação específica entre os escores do BDI/BAI e a conectividade funcional da DMN indicaram que os sintomas de depressão estão associados com o lóbulo parietal inferior esquerdo, enquanto os sintomas de ansiedade estão correlacionados com o cerebelo direito e esquerdo, tronco encefálico direito e esquerdo e lóbulo frontal médio direito. O estudo fornece novas perspectivas sobre os mecanismos subjacentes envolvidos nos sintomas de depressão e ansiedade pós-AVC, sugerindo uma explicação alternativa que não lesões estruturais após o evento isquêmico, mas que estes sintomas psiquiátricos estão relacionados a disfunções em redes cerebraisAbstract: Depression and anxiety symptoms are common after stroke and associated to reduction in quality of life and poor physical and social outcomes. The Default Mode Network (DMN) is a brain network that plays an important role in the emotional processing. We investigated whether these psychiatric symptoms are associated to a disruption of DMN functional connectivity in the first month after stroke. Thirty-four subacute ischemic stroke patients were submitted to: 1) behavioral assessment through Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI) and Structured Clinical Interview for DSM Disorders (SCID-CV); 2) cognitive assessment using Mini Mental State Examination (MMSM) and Montreal Cognitive Assessment (MoCA); 3) assessment of functional impairment using Barthel Index and modified Rankin Scale; 4) resting state functional magnetic resonance imaging acquisition using a 3T scanner (Philips Achieva®). Patients with depression and/or anxiety symptoms showed an increased DMN functional connectivity in left inferior parietal lobule, right middle frontal lobule and left basal nuclei, when compared to stroke controls. Specific correlation between BDI/BAI scores and DMN functional connectivity indicated that depression symptoms are correlated with increased functional connectivity in left inferior parietal lobule, while anxiety symptoms are correlated with increased functional connectivity in left and right cerebellum, left and right brainstem and right middle frontal lobule. Our study provides new insights into the underlying mechanisms of post stroke depression and anxiety symptoms, suggesting an alternate explanation other than regional structural damage following ischemic event, that these psychiatric symptoms are related to brain network dysfunctionMestradoFisiopatologia MédicaMestra em Ciências2013/23183-3FAPES

Depression and anxiety symptoms are associated to disruption of default mode network in subacute ischemic stroke

Depression and anxiety symptoms are common after stroke and associated to reduction in quality of life and poor physical and social outcomes. The Default Mode Network (DMN) plays an important role in the emotional processing. We investigated whether these symptoms are associated to a disruption of DMN functional connectivity in the first month after stroke. Thirty-four subacute ischemic stroke patients were submitted to: 1) behavioral assessment through Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI) and Structured Clinical Interview for DSM Disorders; 2) neuropsychological assessment using Mini Mental State Examination and Montreal Cognitive Assessment; 3) resting state functional magnetic resonance imaging acquisition using a 3 T scanner (Philips Achieva). Patients with depression and/or anxiety symptoms showed an increased DMN functional connectivity in left inferior parietal gyrus and left basal nuclei, when compared to stroke controls. Specific correlation between BDI/BAI scores and DMN functional connectivity indicated that depression symptoms are correlated with increased functional connectivity in left inferior parietal gyrus, while anxiety symptoms are correlated with increased functional connectivity in cerebellum, brainstem and right middle frontal gyrus. Our study provides new insights into the underlying mechanisms of post stroke depression and anxiety, suggesting an alternate explanation other than regional structural damage following ischemic event, that these psychiatric symptoms are related to brain network dysfunction11615711580FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/07559-3; 2013/23183-3; 2015/06163-