Chapter 11 No stone left unturned: the relevance of the neurosciences to infection prevention and control
Paul Elliott, Julie Storr, Annette Jeanes, Barry Professor Cookson, Benedetta Professor Allegranzi, Marilyn ADJ Professor Cruickshank in Infection Prevention and Control, 2017
The Human Connectome Project aims to build a ‘network map’ that will shed light on the anatomical and functional connectivity within the brain. Sebastian Seung, in his book Connectome: how the brain’s wiring makes us who we are,11 explains that any kind of personal change is about changing your connectomes. Seung explains that, unlike our genome, which is fixed from the moment of conception, our connectomes change throughout life. There are many unknowns on the matter but it’s largely believed that life experiences and genetics change our connectomes. Does this matter to our ultimate goals in IPC? Is there a way of influencing people’s connectomes that we just haven’t found yet? Will the outcome of the Human Connectome Project be helpful to us in the future? Seung describes the way muscles work, the axons, the synapses, contractions of fibres – muscles being the final destination of all neural pathways. This is of relevance in instilling habits. Neuroscientists explain that brain cells found where habits are formed and movement is controlled have receptors that work like computer processors to translate regular activities into habits.12
Surveying The Last 50 Years And Looking Ahead
Andrew P. Wickens in A History of the Brain, 2014
The Human Connectome Project is likely to have many benefits. For one thing, because the activity of neural networks determines our behaviour, personality, thoughts and memories, then understanding their anatomical structure and function will help more fully explain how the brain works. It should also help elucidate how genetics and environment can act to alter these connections, which affects everything we do from our ability to solve puzzles, play music or become addicted to drugs. Neural networks are also known to change over time and examination of this process will give us greater insights into neural plasticity, ageing and disease. And, perhaps most importantly, the connectome should allow a better understanding of instances where the brain is ‘miswired’ leading to various mental disorders such as autism, depression and schizophrenia – knowledge that will help treat these brain disorders. It has even been suggested that one day humans will be able to ‘upload’ their minds into computers, achieving a kind of immortality. Whether the Human Connectome project will achieve these objectives is open to question, although there is little doubt it will provide a treasure trove of information over the coming years.
Psychological Intervention
Sahar Swidan, Matthew Bennett in Advanced Therapeutics in Pain Medicine, 2020
Advances in brain imaging methodologies have allowed for the identification of neural components implicated in pain processing and examination of chronic pain-related alterations in neural processes. Imaging data consistently indicate that brain areas associated with sensory, cognitive, emotional, social, and behavioral processes make up what is now often referred to as the pain connectome.16 Specifically, key brain regions responsible for nociceptive and pain processing include the primary and secondary somatosensory cortices, primary motor and supplementary motor cortices, insular cortex, anterior cingulate cortex, prefrontal cortex, thalamus, and regions in the hippocampus, amygdala, and basal ganglia that process fear, emotion, and memory.17,18 Although similar brain regions are implicated in chronic pain processing, extant data suggest that individuals with chronic pain exhibit dysfunction in endogenous pain modulatory systems, structural (i.e. grey matter density and volume) and functional (i.e., default mode network and regional connectivity) brain differences, and alterations in neurochemical systems (i.e., glutamatergic, GABAergic, opioidergic, and dopaminergic systems).17–19
Towards a functional connectome in Drosophila
Published in Journal of Neurogenetics, 2020
Katrin Vogt
The connectome provides the complete anatomical picture of a brain, which due to its complexity can be overwhelming. We are however broadly positioned, equipped with a rich and precise genetic toolkit and ready to take on the challenge of understanding the function of the whole brain. We already know about pitfalls, such as non-predicted connectivity due to neuromodulation, but unbiased approaches such as transcriptomics will help to overcome these obstacles. The humble fruit fly, particularly the larva, seems to be an excellent candidate to bring us a step closer to understanding functional brain connectivity. In the future, comparing whole brain circuit architecture and function within and across model organisms (Lo & Chiang, 2016; White, Southgate, Thomson & Brenner, 1986), might reveal new global principles of neural processing.
Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now?
Published in Expert Review of Neurotherapeutics, 2019
Benedetta Vai, Carlotta Bertocchi, Francesco Benedetti
In the last decade, the prefrontal-limbic disrupted connectivity was proposed as a promising biomarker of the pathophysiology and maintenance of BD [5,6]. The research perspective shifted from segregation as the principle of human brain organization [7] towards the investigation of brain integration, addressing the question of how highly specialized areas are integrated into functional and structural networks. This led to a massive development of connectome techniques and their application to functional, effective and structural connectivity. Structural connectivity (SC) is commonly defined in terms of macro-fiber pathways, especially white matter (WM) tracts [8]. Functional connectivity (FC) indicates the temporal correlation between the activation of different brain regions [9]. Effective connectivity (EC) evaluates the direct or indirect influence that one neural system exerts over another and it can be estimated directly from fMRI signals or based on a priori model [7]. EC and FC are estimated on both fMRI data, during both task execution or resting state (RS).
Deep brain stimulation in essential tremor: targets, technology, and a comprehensive review of clinical outcomes
Published in Expert Review of Neurotherapeutics, 2020
Joshua K. Wong, Christopher W. Hess, Leonardo Almeida, Erik H. Middlebrooks, Evangelos A. Christou, Erin E. Patrick, Aparna Wagle Shukla, Kelly D. Foote, Michael S. Okun
Connectivity profiling has been increasingly used to assist with DBS targeting [55]. One study created connectivity profiles using structural and functional connectivity data of a normative connectome database to predict ‘optimal connectivity.’ Structural connectivity profiles were created by utilizing MRI diffusion tensor imaging (DTI) data and reconstructing approximations of axonal connections using computational tractography algorithms. Functional connectivity profiles were created by analyzing the real-time fluctuations of oxygen simultaneously throughout the brain in the absence of external stimuli via a technique known as blood-oxygen-level dependent resting state fMRI (BOLD rs-fMRI) [56]. Connectivity data from the entire brain network were mapped into a statistical model and correlated with clinical outcomes. The statistical model was combined with volume of tissue activation (VTA) analysis to map tremor suppression with brain tissue and somatotopy between the hand and head. VTA analysis involves approximating the electric field from the DBS electrode based on the specific input of programming parameters [57]. This technique, when combined with 3D imaging data of the brain, can estimate the region of neuronal tissue stimulated by the DBS system. The authors identified optimal connectivity for tremor suppression at the region of the inferior-posterior border of the VIM and the dorsal border of the ZI.
Related Knowledge Centers
- Cognition
- Genetic Code
- Genome
- Nervous System
- Synapse
- Brain
- Neural Circuit
- Neuron
- Single-Unit Recording
- Functional Neuroimaging