White matter damage and cognitive impairment after traumatic brain injury

White matter disruption is an important determinant of cognitive impairment after brain injury, but conventional neuroimaging underestimates its extent. In contrast, diffusion tensor imaging provides a validated and sensitive way of identifying the impact of axonal injury. The relationship between cognitive impairment after traumatic brain injury and white matter damage is likely to be complex. We applied a flexible technique—tract-based spatial statistics—to explore whether damage to specific white matter tracts is associated with particular patterns of cognitive impairment. The commonly affected domains of memory, executive function and information processing speed were investigated in 28 patients in the post-acute / chronic phase following traumatic brain injury and in 26 age-matched controls. Analysis of fractional anisotropy and diffusivity maps revealed widespread differences in white matter integrity between the groups. Patients showed large areas of reduced fractional anisotropy, as well as increased mean and axial diffusivities, compared with controls, despite the small amounts of cortical and white matter damage visible on standard imaging. A stratified analysis based on the presence or absence of microbleeds (a marker of diffuse axonal injury) revealed diffusion tensor imaging to be more sensitive than gradient-echo imaging to white matter damage. The location of white matter abnormality predicted cognitive function to some extent. The structure of the fornices was correlated with associative learning and memory across both patient and control groups, whilst the structure of frontal lobe connections showed relationships with executive function that differed in the two groups. These results highlight the complexity of the relationships between white matter structure and cognition. Although widespread and, sometimes, chronic abnormalities of white matter are identifiable following traumatic brain injury, the impact of these changes on cognitive function is likely to depend on damage to key pathways that link nodes in the distributed brain networks supporting high-level cognitive functions

Default mode network connectivity predicts sustained attention deficits following traumatic brain injury

Traumatic brain injury (TBI) frequently produces impairments of attention in humans. These can result in a failure to maintain consistent goal-directed behavior. A predominantly right-lateralized frontoparietal network is often engaged during attentionally demanding tasks. However, lapses of attention have also been associated with increases in activation within the default mode network (DMN). Here, we study TBI patients with sustained attention impairment, defined on the basis of the consistency of their behavioral performance over time. We show that sustained attention impairments in patients are associated with an increase in DMN activation, particularly within the precuneus and posterior cingulate cortex. Furthermore, the interaction of the precuneus with the rest of the DMN at the start of the task, i.e., its functional connectivity, predicts which patients go on to show impairments of attention. Importantly, this predictive information is present before any behavioral evidence of sustained attention impairment, and the relationship is also found in a subgroup of patients without focal brain damage. TBI often results in diffuse axonal injury, which produces cognitive impairment by disconnecting nodes in distributed brain networks. Using diffusion tensor imaging, we demonstrate that structural disconnection within the DMN also correlates with the level of sustained attention. These results show that abnormalities in DMN function are a sensitive marker of impairments of attention and suggest that changes in connectivity within the DMN are central to the development of attentional impairment after TBI

Bupropion for the treatment of apathy in Huntington's disease:A multicenter, randomised, double-blind, placebo-controlled, prospective crossover trial

OBJECTIVE:To evaluate the efficacy and safety of bupropion in the treatment of apathy in Huntington's disease (HD). METHODS:In this phase 2b multicentre, double-blind, placebo-controlled crossover trial, individuals with HD and clinical signs of apathy according to the Structured Clinical Interview for Apathy-Dementia (SCIA-D), but not depression (n = 40) were randomized to receive either bupropion 150/300mg or placebo daily for 10 weeks. The primary outcome parameter was a significant change of the Apathy Evaluation Scale (AES) score after ten weeks of treatment as judged by an informant (AES-I) living in close proximity with the study participant. The secondary outcome parameters included changes of 1. AES scores determined by the patient (AES-S) or the clinical investigator (AES-C), 2. psychiatric symptoms (NPI, HADS-SIS, UHDRS-Behavior), 3. cognitive performance (SDMT, Stroop, VFT, MMSE), 4. motor symptoms (UHDRS-Motor), 5. activities of daily function (TFC, UHDRS-Function), and 6. caregiver distress (NPI-D). In addition, we investigated the effect of bupropion on brain structure as well as brain responses and functional connectivity during reward processing in a gambling task using magnetic resonance imaging (MRI). RESULTS:At baseline, there were no significant treatment group differences in the clinical primary and secondary outcome parameters. At endpoint, there was no statistically significant difference between treatment groups for all clinical primary and secondary outcome variables. Study participation, irrespective of the intervention, lessened symptoms of apathy according to the informant and the clinical investigator. CONCLUSION:Bupropion does not alleviate apathy in HD. However, study participation/placebo effects were observed, which document the need for carefully controlled trials when investigating therapeutic interventions for the neuropsychiatric symptoms of HD. TRIAL REGISTRATION:ClinicalTrials.gov 01914965

Quantifying motivation with effort-based decision-making paradigms in health and disease

Motivation can be characterized as a series of cost–benefit valuations, in which we weigh the amount of effort we are willing to expend (the cost of an action) in return for particular rewards (its benefits). Human motivation has traditionally been measured with self-report and questionnaire-based tools, but an inherent limitation of these methods is that they are unable to provide a mechanistic explanation of the processes underlying motivated behavior. A major goal of current research is to quantify motivation objectively with effort-based decision-making paradigms, by drawing on a rich literature from nonhuman animals. Here, we review this approach by considering the development of these paradigms in the laboratory setting over the last three decades, and their more recent translation to understanding choice behavior in humans. A strength of this effort-based approach to motivation is that it is capable of capturing the wide range of individual differences, and offers the potential to dissect motivation into its component elements, thus providing the basis for more accurate taxonomic classifications. Clinically, modeling approaches might provide greater sensitivity and specificity to diagnosing disorders of motivation, for example, in being able to detect subclinical disorders of motivation, or distinguish a disorder of motivation from related but separate syndromes, such as depression. Despite the great potential in applying effort-based paradigms to index human motivation, we discuss several caveats to interpreting current and future studies, and the challenges in translating these approaches to the clinical setting.30 page(s

Individual differences in premotor brain systems underlie behavioral apathy

Lack of physical engagement, productivity, and initiative—so-called “behavioral apathy”—is a common problem with significant impact, both personal and economic. Here, we investigate whether there might be a biological basis to such lack of motivation using a new effort and reward-based decision-making paradigm, combined with functional and diffusion-weighted imaging. We hypothesized that behavioral apathy in otherwise healthy people might be associated with differences in brain systems underlying either motivation to act (specifically in effort and reward-based decision-making) or in action processing (transformation of an intention into action). The results demonstrate that behavioral apathy is associated with increased effort sensitivity as well as greater recruitment of neural systems involved in action anticipation: supplementary motor area (SMA) and cingulate motor zones. In addition, decreased structural and functional connectivity between anterior cingulate cortex (ACC) and SMA were associated with increased behavioral apathy. These findings reveal that effort sensitivity and translation of intentions into actions might make a critical contribution to behavioral apathy. We propose a mechanism whereby inefficient communication between ACC and SMA might lead to increased physiological cost—and greater effort sensitivity—for action initiation in more apathetic people

Psychedelics as potential catalysts of scientific creativity and insight

Creativity, that is the creation of ideas or objects considered both novel and valuable, is among the most important and highly valued of human traits, and a fundamental aspect of the sciences. Dreams and hypnagogic states have been highly influential in promoting scientific creativity and insight, contributing to some important scientific breakthroughs. Phenomenologically, the latter states of consciousness share a great deal of overlap with the psychedelic state, which has also been associated with facilitating scientific creativity on occasion. The current article proposes that the dream, hypnagogic and psychedelic states share common features that make them conducive to supporting some aspects of scientific creativity and examines the putative underlying neurophenomenological and cognitive processes involved. In addition, some notable occurrences of scientific insights that have emerged from these types of altered states are reviewed and shared common features are presented, providing a ground for future research. The psychedelic state may have its own characteristic features making it amenable to creativity enhancement, such as brain hyperconnectivity, meta-cognitive awareness, access to a more dependable and sustained altered state experience, and potential for eliciting sustained shifts in trait openness. The contextual factors which may contribute to enhancement of scientific creativity and insight will be evaluated. While research in this area is limited, further work to elucidate how psychedelics may best contribute to scientific creativity enhancement is warranted

Soft x-ray spectroscopic investigation of Zn doped CuCl produced by pulsed dc magnetron sputtering

We report on a systematic investigation of the electronic properties of UV-light emitting Zn doped CuCl thin films implemented using near edge x-ray absorption fine structures (NEXAFS) and high-resolution x-ray photoemission spectroscopy. A clear shift of the valence band maximum towards higher binding energy by 0.2 ± 0.1 eV was observed in Zn doped CuCl as compared to undoped CuCl. This shift is in correlation with the increase in conductivity measured by the Hall effect measurements. A decrease in the optical band gap of CuCl film is also observed as a function of Zn doping. The profound changes in the full width at half maximum and the gradual disappearance of satellite features of Cu 2p core level photoemission as a function of Zn dopant are attributed to the reduced presence of the surface layer of Cu2+ species with d9 configuration in the doped films. These investigations help us to understand the doping mechanisms and underlying physics. The reduced presence of the Cu2+ related surface layer as a function of Zn doping is also verified using NEXAFS

Individual prediction of white matter injury following traumatic brain injury.

OBJECTIVE: Traumatic brain injury (TBI) often results in traumatic axonal injury (TAI). This can be difficult to identify using conventional imaging. Diffusion tensor imaging (DTI) offers a method of assessing axonal damage in vivo, but has previously mainly been used to investigate groups of patients. Machine learning techniques are increasingly used to improve diagnosis based on complex imaging measures. We investigated whether machine learning applied to DTI data can be used to diagnose white matter damage after TBI and to predict neuropsychological outcome in individual patients. METHODS: We trained pattern classifiers to predict the presence of white matter damage in 25 TBI patients with microbleed evidence of TAI compared to neurologically healthy age-matched controls. We then applied these classifiers to 35 additional patients with no conventional imaging evidence of TAI. Finally, we used regression analyses to predict indices of neuropsychological outcome for information processing speed, executive function, and associative memory in a group of 70 heterogeneous patients. RESULTS: The classifiers discriminated between patients with microbleeds and age-matched controls with a high degree of accuracy, and outperformed other methods. When the trained classifiers were applied to patients without microbleeds, patients having likely TAI showed evidence of greater cognitive impairment in information processing speed and executive function. The classifiers were also able to predict the extent of impairments in information processing speed and executive function. INTERPRETATION: The work provides a proof of principle that multivariate techniques can be used with DTI to provide diagnostic information about clinically significant TAI

Anxiety, depression, and decision making: a computational perspective

In everyday life, the outcomes of our actions are rarely certain. Further, we often lack the information needed to precisely estimate the probability and value of potential outcomes as well as how much effort will be required by the courses of action under consideration. Under such conditions of uncertainty, individual differences in the estimation and weighting of these variables, and in reliance on model-free versus model-based decision making, have the potential to strongly influence our behavior. Both anxiety and depression are associated with difficulties in decision making. Further, anxiety is linked to increased engagement in threat-avoidance behaviors and depression is linked to reduced engagement in reward-seeking behaviors. The precise deficits, or biases, in decision making associated with these common forms of psychopathology remain to be fully specified. In this article, we review evidence for which of the computations supporting decision making are altered in anxiety and depression and consider the potential consequences for action selection. In addition, we provide a schematic framework that integrates the findings reviewed and will hopefully be of value to future studies