Post-lesional cerebral reorganisation: Evidence from functional neuroimaging and transcranial magnetic stimulation

Reorganisation of cerebral representations has been hypothesised to underlie the recovery from ischaemic brain infarction. The mechanisms can be investigated non-invasively in the human brain using functional neuroimaging and transcranial magnetic stimulation (TMS). Functional neuroimaging showed that reorganisation is a dynamic process beginning after stroke manifestation. In the acute stage, the mismatch between a large perfusion deficit and a smaller area with impaired water diffusion signifies the brain tissue that potentially enables recovery subsequent to early reperfusion as in thrombolysis. Single-pulse TMS showed that the integrity of the cortico-spinal tract system was critical for motor recovery within the first four weeks, irrespective of a concomitant affection of the somatosensory system. Follow-up studies over several months revealed that ischaemia results in atrophy of brain tissue adjacent to and of brain areas remote from the infarct lesion. In patients with hemiparetic stroke activation of premotor cortical areas in both cerebral hemispheres was found to underlie recovery of finger movements with the affected hand. Paired-pulse TMS showed regression of perilesional inhibition as well as intracortical disinhibition of the motor cortex contralateral to the infarction as mechanisms related to recovery. Training strategies can employ post-lesional brain plasticity resulting in enhanced perilesional activations and modulation of large-scale bihemispheric circuits.     

Large-scale automated synthesis of human functional neuroimaging data

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.     

Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.     

A new penumbra: transitioning from injury into repair after stroke

The penumbra is an area of brain tissue that is damaged but not yet dead after focal ischemia. The existence of a penumbra implies that therapeutic salvage is theoretically possible after stroke. The first decade of penumbral science investigated the ischemic regulation of electrophysiology, cerebral blood flow and metabolism. The second decade advanced our understanding of molecular mechanisms that mediate penumbral cell death. And the third decade saw the rapid development of clinical neuroimaging tools that are now increasingly applied in stroke patients. But how can we look ahead as we move into the fourth decade of penumbra research? This author speculates that a paradigm shift is needed. Most molecular targets for therapy have biphasic roles in stroke pathophysiology. During the acute phase, these targets mediate injury. During the recovery phase, the same mediators contribute to neurovascular remodeling. It is this boundary zone that comprises the new penumbra, and future investigations should dissect where, when and how damaged brain makes the transition from injury into repair.     

Surface-based and probabilistic atlases of primate cerebral cortex

Brain atlases play an increasingly important role in neuroimaging, as they are invaluable for analysis, visualization, and comparison of results across studies. For both humans and macaque monkeys, digital brain atlases of many varieties are in widespread use, each having its own strengths and limitations. For studies of cerebral cortex there is particular utility in hybrid atlases that capitalize on the complementary nature of surface and volume representations, are based on a population average rather than an individual brain, and include measures of variation as well as averages. Linking different brain atlases to one another and to online databases containing a growing body of neuroimaging data will enable powerful forms of data mining that accelerate discovery and improve research efficiency.     

Targeted Lipid Profiling Discovers Plasma Biomarkers of Acute Brain Injury

Prior efforts to identify a blood biomarker of brain injury have relied almost exclusively on proteins; however their low levels at early time points and poor correlation with injury severity have been limiting. Lipids, on the other hand, are the most abundant molecules in the brain and readily cross the blood-brain barrier. We previously showed that certain sphingolipid (SL) species are highly specific to the brain. Here we examined the feasibility of using SLs as biomarkers for acute brain injury. A rat model of traumatic brain injury (TBI) and a mouse model of stroke were used to identify candidate SL species though our mass-spectrometry based lipid profiling approach. Plasma samples collected after TBI in the rat showed large increases in many circulating SLs following injury, and larger lesions produced proportionately larger increases. Plasma samples collected 24 hours after stroke in mice similarly revealed a large increase in many SLs. We constructed an SL score (sum of the two SL species showing the largest relative increases in the mouse stroke model) and then evaluated the diagnostic value of this score on a small sample of patients (n = 14) who presented with acute stroke symptoms. Patients with true stroke had significantly higher SL scores than patients found to have non-stroke causes of their symptoms. The SL score correlated with the volume of ischemic brain tissue. These results demonstrate the feasibility of using lipid biomarkers to diagnose brain injury. Future studies will be needed to further characterize the diagnostic utility of this approach and to transition to an assay method applicable to clinical settings.     

Deciphering the role of docosahexaenoic acid in brain maturation and pathology with magnetic resonance imaging

Animal studies have found that deficits in brain docosahexaenoic acid (DHA, 22:6n-3) accrual during perinatal development leads to transient and enduring abnormalities in brain development and function. Determining the relevance of this evidence to brain disorders in humans has been hampered by an inability to determine antimortem brain DHA levels and limitations associated with a postmortem approach. Accordingly, there is a need for alternate or complementary approaches to better understand the role of DHA in cortical function and pathology, and conventional magnetic resonance imaging (MRI) techniques may be ideally suited for this application. A major advantage of neuroimaging is that it permits prospective evaluation of the effects of manipulating DHA status on both clinical and neuroimaging variables. Emerging evidence from MRI studies suggest that greater DHA status is associated with cortical structural and functional integrity, and suggest that reduced DHA status and abnormalities in cortical function observed in psychiatric disorders may be interrelated phenomenon. Preliminary evidence from animal MRI studies support a critical role of DHA in normal brain development. Neuroimaging research in both human and animals therefore holds tremendous promise for developing a better understanding of the role of DHA status in cortical function, as well as for elucidating the impact of DHA deficiency on neuropathological processes implicated in the etiology and progression of neurodevelopmental and psychiatric disorders.     

Discovering Relations Between Mind,Brain, and Mental Disorders Using Topic Mapping

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.     

Experiences of Self-Management Support Following a Stroke: A Meta-Review of Qualitative Systematic Reviews

Background

Supporting self-management in stroke patients improves psychological and functional outcomes but evidence on how to achieve this is sparse. We aimed to synthesise evidence from systematic reviews of qualitative studies in an overarching meta-review to inform the delivery and development of self-management support interventions.

Methods

We systematically searched eight electronic databases including MEDLINE, EMBASE and CINAHL for qualitative systematic reviews (published January 1993 to June 2012). We included studies exploring patients’, carers’ or health care professionals’ experiences relevant to self-management support following a stroke, including studies describing the lived experience of surviving a stroke. We meta-synthesised the included review findings using a meta-ethnographic framework.

Results

Seven reviews, reporting 130 unique studies, were included. Themes emerging from the reviews were pertinent, consistent and showed data saturation; though explicit mention of self-management support was rare. Our meta-review highlighted the devastating impact of stroke on patients’ self-image; the varying needs for self-management support across the trajectory of recovery; the need for psychological and emotional support throughout recovery particularly when physical recovery plateaus; the considerable information needs of patients and carers which also vary across the trajectory of recovery; the importance of good patient-professional communication; the potential benefits of goal-setting and action-planning; and the need for social support which might be met by groups for stroke survivors.

Conclusions

The observed data saturation suggests that, currently, no further qualitative research simply describing the lived experience of stroke is needed; we propose that it would be more useful to focus on qualitative research informing self-management support interventions and their implementation. Our findings demonstrate both the on-going importance of self-management support and the evolving priorities throughout the stages of recovery following a stroke. The challenge now is to ensure these findings inform routine practice and the development of interventions to support self-management amongst stroke survivors.     

The Prognosis of Allocentric and Egocentric Neglect: Evidence from Clinical Scans

We contrasted the neuroanatomical substrates of sub-acute and chronic visuospatial deficits associated with different aspects of unilateral neglect using computed tomography scans acquired as part of routine clinical diagnosis. Voxel-wise statistical analyses were conducted on a group of 160 stroke patients scanned at a sub-acute stage. Lesion-deficit relationships were assessed across the whole brain, separately for grey and white matter. We assessed lesions that were associated with behavioural performance (i) at a sub-acute stage (within 3 months of the stroke) and (ii) at a chronic stage (after 9 months post stroke). Allocentric and egocentric neglect symptoms at the sub-acute stage were associated with lesions to dissociated regions within the frontal lobe, amongst other regions. However the frontal lesions were not associated with neglect at the chronic stage. On the other hand, lesions in the angular gyrus were associated with persistent allocentric neglect. In contrast, lesions within the superior temporal gyrus extending into the supramarginal gyrus, as well as lesions within the basal ganglia and insula, were associated with persistent egocentric neglect. Damage within the temporo-parietal junction was associated with both types of neglect at the sub-acute stage and 9 months later. Furthermore, white matter disconnections resulting from damage along the superior longitudinal fasciculus were associated with both types of neglect and critically related to both sub-acute and chronic deficits. Finally, there was a significant difference in the lesion volume between patients who recovered from neglect and patients with chronic deficits. The findings presented provide evidence that (i) the lesion location and lesion size can be used to successfully predict the outcome of neglect based on clinical CT scans, (ii) lesion location alone can serve as a critical predictor for persistent neglect symptoms, (iii) wide spread lesions are associated with neglect symptoms at the sub-acute stage but only some of these are critical for predicting whether neglect will become a chronic disorder and (iv) the severity of behavioural symptoms can be a useful predictor of recovery in the absence of neuroimaging findings on clinical scans. We discuss the implications for understanding the symptoms of the neglect syndrome, the recovery of function and the use of clinical scans to predict outcome.     

Applications of fMRI in translational medicine and clinical practice

Functional MRI (fMRI) has had a major impact in cognitive neuroscience. fMRI now has a small but growing role in clinical neuroimaging, with initial applications to neurosurgical planning. Current clinical research has emphasized novel concepts for clinicians, such as the role of plasticity in recovery and the maintenance of brain functions in a broad range of diseases. There is a wider potential for clinical fMRI in applications ranging from presymptomatic diagnosis, through drug development and individualization of therapies, to understanding functional brain disorders. Realization of this potential will require changes in the way clinical neuroimaging services are planned and delivered.     

Communication and the primate brain: insights from neuroimaging studies in humans, chimpanzees and macaques

Considerable knowledge is available on the neural substrates for speech and language from brain-imaging studies in humans, but until recently there was a lack of data for comparison from other animal species on the evolutionarily conserved brain regions that process species-specific communication signals. To obtain new insights into the relationship of the substrates for communication in primates, we compared the results from several neuroimaging studies in humans with those that have recently been obtained from macaque monkeys and chimpanzees. The recent work in humans challenges the longstanding notion of highly localized speech areas. As a result, the brain regions that have been identified in humans for speech and nonlinguistic voice processing show a striking general correspondence to how the brains of other primates analyze species-specific vocalizations or information in the voice, such as voice identity. The comparative neuroimaging work has begun to clarify evolutionary relationships in brain function, supporting the notion that the brain regions that process communication signals in the human brain arose from a precursor network of regions that is present in nonhuman primates and is used for processing species-specific vocalizations. We conclude by considering how the stage now seems to be set for comparative neurobiology to characterize the ancestral state of the network that evolved in humans to support language.     

Learning related interactions among neuronal systems involved in memory processes

Functional neuroimaging techniques using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided new insights in our understanding of brain function from the molecular to the systems level. While subtraction strategy based data analyses have revealed the involvement of distributed brain regions in memory processes, covariance analysis based data analysis strategies allow functional interactions between brain regions of a neuronal network to be assessed. The focus of this chapter is to (1) establish the functional topography of episodic and working memory processes in young and old normal volunteers, (2) to assess functional interactions between modules of networks of brain regions by means of covariance based analyses and systems level modelling and (3) to relate neuroimaging data to the underpinning neural networks. Male normal young and old volunteers without neurological or psychiatric illness participated in neuroimaging studies (PET, fMRI) on working and episodic memory. Distributed brain areas are involved in memory processes (episodic and working memory) in young volunteers and show much of an overlap with respect to the network components. Systems level modelling analyses support the hypothesis of bihemispheric, asymmetric networks subserving memory processes and revealed both similarities in general and differences in the interactions between brain regions during episodic encoding and retrieval as well as working memory. Changes in memory function with ageing are evident from studies in old volunteers activating more brain regions compared to young volunteers and revealing more and stronger influences of prefrontal regions. We finally discuss the way in which the systems level models based on PET and fMRI results have implications for the understanding of the underlying neural network functioning of the brain.     

The impact of the 1991 Gulf War on the mind and brain: findings from neuropsychological and neuroimaging research

Many veterans of the 1991 Gulf War (GW) have complained of somatic and cognitive symptoms that may be neurological in nature. However, whether or not changes in brain function are associated with GW service continues to be debated. Studies of GW veterans using objective, performance-based neuropsychological measures have yielded inconsistent findings, with those indicating deficits among GW veterans typically revealing only relatively mild levels of neuropsychological impairment. Further, performances on objective neuropsychological tasks show little correspondence to subjective perceptions of cognitive functioning. Although preliminary magnetic resonance spectroscopy (MRS) studies demonstrate reduced N-acetylaspartate-to-creatine (NAA/Cr) ratio in select brain regions among GW veterans who report health concerns, this work requires further replication with larger, more representative samples. There is no evidence from neuroimaging studies of a non-specific effect of GW service or of changes in brain structure or function related to health status when conventional radiological methods are used. Owing to the paucity of objective exposure, baseline health data, and the now significant time elapsed since the GW, aetiological issues may never be fully resolved. Therefore, research addressing clinical management of GW veterans with neuropsychological dysfunction and neuroimaging abnormalities may prove more fruitful than exclusive focus on aetiology.     

Remote Neurodegeneration: Multiple Actors for One Play

Remote neurodegeneration significantly influences the clinical outcome in many central nervous system (CNS) pathologies, such as stroke, multiple sclerosis, and traumatic brain and spinal cord injuries. Because these processes develop days or months after injury, they are accompanied by a therapeutic window of opportunity. The complexity and clinical significance of remote damage is prompting many groups to examine the factors of remote degeneration. This research is providing insights into key unanswered questions, opening new avenues for innovative neuroprotective therapies. In this review, we evaluate data from various remote degeneration models to describe the complexity of the systems that are involved and the importance of their interactions in reducing damage and promoting recovery after brain lesions. Specifically, we recapitulate the current data on remote neuronal degeneration, focusing on molecular and cellular events, as studied in stroke and brain and spinal cord injury models. Remote damage is a multifactorial phenomenon in which many components become active in specific time frames. Days, weeks, or months after injury onset, the interplay between key effectors differentially affects neuronal survival and functional outcomes. In particular, we discuss apoptosis, inflammation, oxidative damage, and autophagy—all of which mediate remote degeneration at specific times. We also review current findings on the pharmacological manipulation of remote degeneration mechanisms in reducing damage and sustaining outcomes. These novel treatments differ from those that have been proposed to limit primary lesion site damage, representing new perspectives on neuroprotection.     

Tensor response quantile regression with neuroimaging data

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor response methods.     

Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates

Summary Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA‐GMM) approach to estimate marginal regression models for imaging datasets that contain time‐varying, spatially related responses and some time‐varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject’s response image at each time point) as many current neuroimaging analysis techniques do, this method “adaptively smoothes” neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA‐GMM’s development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer’s Disease Neuroimaging Initiative are used to assess the performance of MA‐GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer’s Disease Neuroimaging Initiative.     

Urinary proteomics to support diagnosis of stroke

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (-0.30 to 1.25) in cases compared to -1.04 (IQR -1.86 to -0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.     

Magnetic resonance imaging for chronic pain: diagnosis,manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.     

G-CSF (Granulocyte-Colony Stimulating Factor) in the Central Nervous System

G-CSF (Granulocyte-colony stimulating factor) is a hematopoietic growth factor that has been known for 20 years, and has been named for its role in the proliferation and differentiation of cells of the myeloic lineage. We have uncovered a novel spectrum of activities of G-CSF in the central nervous system. G-CSF and its receptor are expressed by neurons in many brain regions, and are upregulated upon experimental stroke. In neurons, G-CSF acts anti-apoptotically by activating several protective pathways. In vivo, G-CSF decreases infarct volumes in acute stroke models in rodents. Moreover, G-CSF stimulates neuronal differentiation of adult neural stem cells in the brain, and improves long-term recovery in more chronic stroke models. Thus, G-CSF is a novel neurotrophic factor, and a highly attractive candidate for the treatment of neurodegenerative conditions. Here we discuss this new property of G-CSF in contrast to its known functions in the hematopoietic system, summarize data from other groups on G-CSF’s actions in cerebral ischemia, compare G-CSF to Erythropoietin (EPO) in the CNS and highlight clinical implications.