Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Modeling transcranial magnetic stimulation from the induced electric fields to the membrane potentials along tractography-based white matter fiber tracts

Objective. Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well. Approach. The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics. Main results. This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient- and case-specific results. Significance. The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter- and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions

Neuronal glucose transporter isoform 3 deficient mice demonstrate features of autism spectrum disorders.

Neuronal glucose transporter (GLUT) isoform 3 deficiency in null heterozygous mice led to abnormal spatial learning and working memory but normal acquisition and retrieval during contextual conditioning, abnormal cognitive flexibility with intact gross motor ability, electroencephalographic seizures, perturbed social behavior with reduced vocalization and stereotypies at low frequency. This phenotypic expression is unique as it combines the neurobehavioral with the epileptiform characteristics of autism spectrum disorders. This clinical presentation occurred despite metabolic adaptations consisting of an increase in microvascular/glial GLUT1, neuronal GLUT8 and monocarboxylate transporter isoform 2 concentrations, with minimal to no change in brain glucose uptake but an increase in lactate uptake. Neuron-specific glucose deficiency has a negative impact on neurodevelopment interfering with functional competence. This is the first description of GLUT3 deficiency that forms a possible novel genetic mechanism for pervasive developmental disorders, such as the neuropsychiatric autism spectrum disorders, requiring further investigation in humans

Neurological prognostication of outcome in patients in coma after cardiac arrest.

Management of coma after cardiac arrest has improved during the past decade, allowing an increasing proportion of patients to survive, thus prognostication has become an integral part of post-resuscitation care. Neurologists are increasingly confronted with raised expectations of next of kin and the necessity to provide early predictions of long-term prognosis. During the past decade, as technology and clinical evidence have evolved, post-cardiac arrest prognostication has moved towards a multimodal paradigm combining clinical examination with additional methods, consisting of electrophysiology, blood biomarkers, and brain imaging, to optimise prognostic accuracy. Prognostication should never be based on a single indicator; although some variables have very low false positive rates for poor outcome, multimodal assessment provides resassurance about the reliability of a prognostic estimate by offering concordant evidence

The BOLD signal and neurovascular coupling in autism

BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) is commonly used to study differences in neuronal activity between human populations. As the BOLD response is an indirect measure of neuronal activity, meaningful interpretation of differences in BOLD responses between groups relies upon a stable relationship existing between neuronal activity and the BOLD response across these groups. However, this relationship can be altered by changes in neurovascular coupling or energy consumption, which would lead to problems in identifying differences in neuronal activity. In this review, we focus on fMRI studies of people with autism, and comparisons that are made of their BOLD responses with those of control groups. We examine neurophysiological differences in autism that may alter neurovascular coupling or energy use, discuss recent studies that have used fMRI to identify differences between participants with autism and control participants, and explore experimental approaches that could help attribute between-group differences in BOLD signals to either neuronal or neurovascular factors