Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

O EFEITO DO CONTROLE RESPIRATÓRIO EM VARIÁVEIS ELETROFISIOLÓGICAS DA ATENÇÃO

A prática do ioga tem se tornado cada vez mais popular, não apenas pelos benefícios físicos, mas principalmente pelo bem-estar psicológico trazido pela sua prática. Um dos componentes do ioga é o Prãnãyama, ou controle da respiração. A atenção e a respiração são dois mecanismos fisiológicos e involuntários requeridos para a execução do Prãnãyama. O principal objetivo desse estudo foi verificar se variáveis contínuas do EEG (potência de diferentes faixas que o compõem) seriam moduladas pelo controle respiratório, comparando-se separadamente as duas fases do ciclo respiratório (inspiração e expiração), na situação de respiração espontânea e controlada. Fizeram parte do estudo 19 sujeitos (7 homens/12 mulheres, idade média de 36,89 e DP = ± 14,46) que foram convidados a participar da pesquisa nas dependências da Faculdade de Saúde da Universidade Metodista de São Paulo. Para o registro do eletroencefalograma foi utilizado um sistema de posicionamento de cinco eletrodos Ag AgCl (FPz, Fz, Cz, Pz e Oz) fixados a uma touca de posicionamento rápido (Quick-Cap, Neuromedical Supplies®), em sistema 10-20. Foram obtidos valores de máxima amplitude de potência (espectro de potência no domínio da frequência) nas frequências teta, alfa e beta e delta e calculada a razão teta/beta nas diferentes fases do ciclo respiratório (inspiração e expiração), separadamente, nas condições de respiração espontânea e de controle respiratório. Para o registro do ciclo respiratório, foi utilizada uma cinta de esforço respiratório M01 (Pletismógrafo). Os resultados mostram diferenças significativas entre as condições de respiração espontânea e de controle com valores das médias da razão teta/beta menores na respiração controlada do que na respiração espontânea e valores de média da potência alfa sempre maiores no controle respiratório. Diferenças significativas foram encontradas na comparação entre inspiração e expiração da respiração controlada com diminuição dos valores das médias da razão teta/beta na inspiração e aumento nos valores das médias da potência alfa, sobretudo na expiração. Os achados deste estudo trazem evidências de que o controle respiratório modula variáveis eletrofisiológicas relativas à atenção refletindo um estado de alerta, porém mais relaxado do que na situação de respiração espontânea.

Robotic control systems based on bioinspired multi-agent systems: application of the principles of neuroscience to robotics

Robotics is a field that presents a large number of problems because it depends on a large number of disciplines, devices, technologies and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges, such as robots household robots or professional robots. To facilitate the rapid development of robotic systems, low cost, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems.

The American journal of psychiatry.

Official journal of the American Psychiatric Association.

The DVD animal model of schizophrenia: Effects of low maternal vitamin D on brain dopamine

Background: We have previously shown that the offspring of vitamin D3 depleted rats have enlarged ventricles and altered neurotrophin profiles (reduced NGF and GDNF). These findings enhance the biological plausibility that low prenatal vitamin D may be a risk factor for schizophrenia. Our recent behavioural studies have found that adult rats with developmental vitamin D deficiency (DVD) have a subtle increase in baseline locomotor activity and a heightened response to dopamine (DA) antagonists. The aim of this study was to investigate brain DA neurochemistry in the DVD model. Methods: We examined cerebrums and striatal tissue from neonates and a variety of brain tissues from the remaining littermates at adulthood. DA, DOPAC, HVA, serotonin and 5HIAA were analysed by HPLC. Single point comparisons for DA1, DA2 and NMDA receptors were also assessed in these tissues. Results: Significant increases in DA and HVA were found in brains from DVD deplete neonates (P=0.01). However, DA and its metabolites were not increased in either the neonate or adult striatum, however there was a trend towards increased DA and its metabolites in the accumbens (P=0.1). Receptor densities were unaffected by prenatal vitamin D levels. Conclusions: Although the effect of maternal diet appears to increase DA production and turnover in neonatal brain, this does not persist into adulthood. Thus other factors must underlie the increased locomotor activity noted in these animals. Future experiments will concentrate on monitoring accumbens and striatal DA release and turnover using microdialysis in pharmacologically challenged behavioural paradigms. References: Eyles D, Brown J; Mackay-Sim A, McGrath J, Feron F. (2003) Vitamin D3 and brain development. Neuroscience 118 (3) 641–653. Burne T, McGrath J, Eyles D, Mackay-Sim A. Behavioural characterization of vitamin D receptor knockout mice. (2005) Behavioural Brain Res: 157 299–308.

O EFEITO DO CONTROLE RESPIRATÓRIO EM VARIÁVEIS ELETROFISIOLÓGICAS DA ATENÇÃO

A prática do ioga tem se tornado cada vez mais popular, não apenas pelos benefícios físicos, mas principalmente pelo bem-estar psicológico trazido pela sua prática. Um dos componentes do ioga é o Prãnãyama, ou controle da respiração. A atenção e a respiração são dois mecanismos fisiológicos e involuntários requeridos para a execução do Prãnãyama. O principal objetivo desse estudo foi verificar se variáveis contínuas do EEG (potência de diferentes faixas que o compõem) seriam moduladas pelo controle respiratório, comparando-se separadamente as duas fases do ciclo respiratório (inspiração e expiração), na situação de respiração espontânea e controlada. Fizeram parte do estudo 19 sujeitos (7 homens/12 mulheres, idade média de 36,89 e DP = ± 14,46) que foram convidados a participar da pesquisa nas dependências da Faculdade de Saúde da Universidade Metodista de São Paulo. Para o registro do eletroencefalograma foi utilizado um sistema de posicionamento de cinco eletrodos Ag AgCl (FPz, Fz, Cz, Pz e Oz) fixados a uma touca de posicionamento rápido (Quick-Cap, Neuromedical Supplies®), em sistema 10-20. Foram obtidos valores de máxima amplitude de potência (espectro de potência no domínio da frequência) nas frequências teta, alfa e beta e delta e calculada a razão teta/beta nas diferentes fases do ciclo respiratório (inspiração e expiração), separadamente, nas condições de respiração espontânea e de controle respiratório. Para o registro do ciclo respiratório, foi utilizada uma cinta de esforço respiratório M01 (Pletismógrafo). Os resultados mostram diferenças significativas entre as condições de respiração espontânea e de controle com valores das médias da razão teta/beta menores na respiração controlada do que na respiração espontânea e valores de média da potência alfa sempre maiores no controle respiratório. Diferenças significativas foram encontradas na comparação entre inspiração e expiração da respiração controlada com diminuição dos valores das médias da razão teta/beta na inspiração e aumento nos valores das médias da potência alfa, sobretudo na expiração. Os achados deste estudo trazem evidências de que o controle respiratório modula variáveis eletrofisiológicas relativas à atenção refletindo um estado de alerta, porém mais relaxado do que na situação de respiração espontânea.

Cognitive neuroscience: the evolution of a discipline

Preface. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.

Leadership research and cognitive neuroscience:the state of this union

Advances in cognitive neuroscience and other approaches to understanding human behavior from a biological standpoint are only now beginning to filter into leadership research. The purpose of this introduction to the Leadership Quarterly Special Issue on the Biology of Leadership is to outline the organizational cognitive neuroscience approach to leadership research, and show how such an approach can fruitfully inform both leadership and neuroscientific research. Indeed, we advance the view that the further application of cognitive neuroscientific techniques to leadership research will pay great dividends in our understanding of effective leadership behaviors and as such, a future symbiosis between the two fields is a necessity.

Investigating the human mirror neuron system by means of cortical synchronization during the imitation of biological movements

The human mirror neuron system (MNS) has recently been a major topic of research in cognitive neuroscience. As a very basic reflection of the MNS, human observers are faster at imitating a biological as compared with a non-biological movement. However, it is unclear which cortical areas and their interactions (synchronization) are responsible for this behavioural advantage. We investigated the time course of long-range synchronization within cortical networks during an imitation task in 10 healthy participants by means of whole-head magnetoencephalography (MEG). Extending previous work, we conclude that left ventrolateral premotor, bilateral temporal and parietal areas mediate the observed behavioural advantage of biological movements in close interaction with the basal ganglia and other motor areas (cerebellum, sensorimotor cortex). Besides left ventrolateral premotor cortex, we identified the right temporal pole and the posterior parietal cortex as important junctions for the integration of information from different sources in imitation tasks that are controlled for movement (biological vs. non-biological) and that involve a certain amount of spatial orienting of attention. Finally, we also found the basal ganglia to participate at an early stage in the processing of biological movement, possibly by selecting suitable motor programs that match the stimulus.

Neuromarketing and consumer neuroscience:contributions to neurology

Background: 'Neuromarketing' is a term that has often been used in the media in recent years. These public discussions have generally centered around potential ethical aspects and the public fear of negative consequences for society in general, and consumers in particular. However, positive contributions to the scientific discourse from developing a biological model that tries to explain context-situated human behavior such as consumption have often been neglected. We argue for a differentiated terminology, naming commercial applications of neuroscientific methods 'neuromarketing' and scientific ones 'consumer neuroscience'. While marketing scholars have eagerly integrated neuroscientific evidence into their theoretical framework, neurology has only recently started to draw its attention to the results of consumer neuroscience.Discussion: In this paper we address key research topics of consumer neuroscience that we think are of interest for neurologists; namely the reward system, trust and ethical issues. We argue that there are overlapping research topics in neurology and consumer neuroscience where both sides can profit from collaboration. Further, neurologists joining the public discussion of ethical issues surrounding neuromarketing and consumer neuroscience could contribute standards and experience gained in clinical research.Summary: We identify the following areas where consumer neuroscience could contribute to the field of neurology:. First, studies using game paradigms could help to gain further insights into the underlying pathophysiology of pathological gambling in Parkinson's disease, frontotemporal dementia, epilepsy, and Huntington's disease.Second, we identify compulsive buying as a common interest in neurology and consumer neuroscience. Paradigms commonly used in consumer neuroscience could be applied to patients suffering from Parkinson's disease and frontotemporal dementia to advance knowledge of this important behavioral symptom.Third, trust research in the medical context lacks empirical behavioral and neuroscientific evidence. Neurologists entering this field of research could profit from the extensive knowledge of the biological foundation of trust that scientists in economically-orientated neurosciences have gained.Fourth, neurologists could contribute significantly to the ethical debate about invasive methods in neuromarketing and consumer neuroscience. Further, neurologists should investigate biological and behavioral reactions of neurological patients to marketing and advertising measures, as they could show special consumer vulnerability and be subject to target marketing. © 2013 Javor et al.; licensee BioMed Central Ltd.

Growing better brains? Pregnancy and neuroscience discourses in English social and welfare policies

In recent years, English welfare and health policy has started to include pregnancy within the foundation stage of child development. The foetus is also increasingly designated as ‘at risk’ from pregnant women. In this article, we draw on an analysis of a purposive sample of English social and welfare policies and closely related advocacy documents to trace the emergence of neuroscientific claims-making in relation to the family. In this article, we show that a specific deterministic understanding of the developing brain that only has a loose relationship with current scientific evidence is an important component in these changes. We examine the ways in which pregnancy is situated in these debates. In these debates, maternal stress is identified as a risk to the foetus; however, the selective concern with women living in disadvantage undermines biological claims. The policy claim of neurological ‘critical windows’ also seems to be influenced by social concerns. Hence, these emerging concerns over the foetus’ developing brain seem to be situated within the gendered history of policing women’s pregnant bodies rather than acting on new insights from scientific discoveries. By situating these developments within the broader framework of risk consciousness, we can link these changes to wider understandings of the ‘at risk’ child and intensified surveillance over family life.

How organizational cognitive neuroscience can deepen understanding of managerial decision-making:a review of the recent literature and future directions

There is growing interest in exploring the potential links between human biology and management and organization studies, which is bringing greater attention to bear on the place of mental processes in explaining human behaviour and effectiveness. The authors define this new field as organizational cognitive neuroscience (OCN), which is in the exploratory phase of its emergence and diffusion. It is clear that there are methodological debates and issues associated with OCN research, and the aim of this paper is to illuminate these concerns, and provide a roadmap for rigorous and relevant future work in the area. To this end, the current reach of OCN is investigated by the systematic review methodology, revealing three clusters of activity, covering the fields of economics, marketing and organizational behaviour. Among these clusters, organizational behaviour seems to be an outlier, owing to its far greater variety of empirical work, which the authors argue is largely a result of the plurality of research methods that have taken root within this field. Nevertheless, all three clusters contribute to a greater understanding of the biological mechanisms that mediate choice and decision-making. The paper concludes that OCN research has already provided important insights regarding the boundaries surrounding human freedom to act in various domains and, in turn, self-determination to influence the workplace. However, there is much to be done, and emerging research of significant interest is highlighted.

Neurophysiological investigations for the diagnosis of non-epileptic attack disorder in neuropsychiatry services:from safety standards to improved effectiveness

OBJECTIVE: The discipline of clinical neuropsychiatry currently provides specialised services for a number of conditions that cross the traditional boundaries of neurology and psychiatry, including non-epileptic attack disorder. Neurophysiological investigations have an important role within neuropsychiatry services, with video-electroencephalography (EEG) telemetry being the gold standard investigation for the differential diagnosis between epileptic seizures and non-epileptic attacks. This article reviews existing evidence on best practices for neurophysiology investigations, with focus on safety measures for video-EEG telemetry. METHODS: We conducted a systematic literature review using the PubMed database in order to identify the scientific literature on the best practices when using neurophysiological investigations in patients with suspected epileptic seizures or non-epileptic attacks. RESULTS: Specific measures need to be implemented for video-EEG telemetry to be safely and effectively carried out by neuropsychiatry services. A confirmed diagnosis of non-epileptic attack disorder following video-EEG telemetry carried out within neuropsychiatry units has the inherent advantage of allowing diagnosis communication and implementation of treatment strategies in a timely fashion, potentially improving clinical outcomes and cost-effectiveness significantly. CONCLUSION: The identified recommendations set the stage for the development of standardised guidelines to enable neuropsychiatry services to implement streamlined and evidence-based care pathways.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

A systematic review of the psychobiological burden of informal caregiving for patients with dementia: focus on cognitive and biological markers of chronic stress

As the physiological impact of chronic stress is difficult to study in humans, naturalistic stressors are invaluable sources of information in this area. This review systematically evaluates the research literature examining biomarkers of chronic stress, including neurocognition, in informal dementia caregivers. We identified 151 papers for inclusion in the final review, including papers examining differences between caregivers and controls as well as interventions aimed at counteracting the biological burden of chronic caregiving stress. Results indicate that cortisol was increased in caregivers in a majority of studies examining this biomarker. There was mixed evidence for differences in epinephrine, norepinephrine and other cardiovascular markers. There was a high level of heterogeneity in immune system measures. Caregivers performed more poorly on attention and executive functioning tests. There was mixed evidence for memory performance. Interventions to reduce stress improved cognition but had mixed effects on cortisol. Risk of bias was generally low to moderate. Given the rising need for family caregivers worldwide, the implications of these findings can no longer be neglected.