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Mapping the Injured Brain
Published in Yu Chen, Babak Kateb, Neurophotonics and Brain Mapping, 2017
Chandler Sours, Jiachen Zhuo, Rao P. Gullapalli
In addition, a groundbreaking work on a severe TBI population has recently linked this failure to deactivate the DMN with structural damage within WM tracts connecting the right anterior insula as measured using DTI (Bonnelle et al., 2012). This is particularly intriguing considering that the proposed role of the salience network, which includes the insula, is to detect external sensory inputs and modulate the balance between internal focus represented by activation of the DMN and external attention represented by activation of TPN (Menon and Uddin, 2010; Seeley et al., 2007). These findings provide an invaluable link between altered structural integrity and reduced functional activation on a network level.
Are episodic memories special? On the sameness of remembered and imagined event simulation
Published in Journal of the Royal Society of New Zealand, 2018
Outside of the hippocampus, some DMN regions are thought to mediate specific types of representational content comprising simulations, such as general and autobiographical semantic knowledge (lateral temporal cortex; Binder et al. 2009), schemas including self-schemas (medial prefrontal cortex; Gilboa & Moscovitch 2017), spatial and contextual information (retrosplenial and parahippocampal cortices; Bar & Aminoff 2003) and the DMN also couples with other networks depending on content, including but not limited to the visual network (e.g. cuneus, fusiform and lingual gyri; Binder et al. 2009). Other DMN regions are thought to be more process-oriented: the precuneus supports episodic and visuospatial imagery (Cavanna & Trimble 2006), while the inferior lateral parietal cortex (e.g. angular gyrus) mediates the retrieval and binding of details into complex simulations (e.g. Thakral, Madore et al. 2017) and/or attention to these inner representations (Cabeza et al. 2012). Additionally, specific task demands may require DMN connectivity with the frontoparietal control and dorsal attention networks, such as when using simulations for autobiographical planning (Spreng et al. 2010) and problem solving (Gerlach et al. 2011); and with the ventral attention (salience) network when simulations are emotionally- or cognitively-salient (e.g. theory of mind; Spreng & Grady 2010: e.g. creative cognition; Ellamil et al. 2012; Beaty et al. 2015; Madore et al. 2017).
The New Zealand Genetic Frontotemporal Dementia Study (FTDGeNZ): a longitudinal study of pre-symptomatic biomarkers
Published in Journal of the Royal Society of New Zealand, 2023
Brigid Ryan, Ashleigh O’Mara Baker, Christina Ilse, Kiri L. Brickell, Hannah M. Kersten, Joanna M. Williams, Donna Rose Addis, Lynette J. Tippett, Maurice A. Curtis
In all analyses, we take a whole-brain approach, complemented where appropriate with region-of-interest (ROI) analyses focusing on regions affected in bvFTD (e.g. salience network nodes; Zhang et al. 2022) as well as regions recruited by the autobiographical tasks (e.g. default mode network nodes; Benoit and Schacter 2015). Given the small Ns, a case-study approach will be employed (Streese and Tranel 2021) such that each carrier will be statistically compared to the non-carrier control group using Crawford’s modified t-tests (Crawford and Howell 2010) unless otherwise stated. Standard pre-processing and analysis pipelines will be used for all MRI data using standard software packages. fMRI data will be preprocessed using fmriprep (Esteban et al. 2019). fMRI data will be de-noised using ICA-AROMA (Pruim et al. 2015), and confounds (e.g. motion, signal drift etc.) regressed from fMRI time-series. Resting-state time-series will additionally be band-pass filtered (.01-.1 Hz) to isolate low-frequency signal fluctuations. For resting-state analyses, the conn toolbox (Whitfield-Gabrieli and Nieto-Castanon 2012, implemented in SPM12, https://www.fil.ion.ucl.ac.uk/spm/) will be used to extract time-series from nodes in seven whole-brain networks (Yeo et al. 2011). Correlation coefficients between all nodes within a network (except for self-connections) will be averaged to index within-network connectivity strength, and compared between each carrier and the non-carrier control group. Individual connections will be tested in networks that differ significantly, and/or in a priori ROIs. For task-related fMRI analyses, a multivariate technique – spatiotemporal partial least squares (PLS; McIntosh et al. 2004) – will be used to identify the whole-brain activation patterns associated with the autobiographical tasks (relative to the control task) as well as the regions functionally connected with a priori seed ROIs (e.g. hippocampus) during the tasks (for discussion of application of PLS to autobiographical tasks and patient groups, see Hach et al. 2014). These whole brain patterns related to task and/or seed regions will first be identified in the non-carrier control group. Brain scores – a weighted average denoting the degree to which any given participant expresses the brain pattern – will then be derived and compared between carriers and non-carriers, as will percent signal change extracted from a priori ROIs.
All bursts are equal, but some are more equal (to burst firing): burstDR stimulation versus Boston burst stimulation
Published in Expert Review of Medical Devices, 2020
Dirk De Ridder, Tim Vancamp, Steven M. Falowski, Sven Vanneste
Burst firing can exert both a negative [37] as well as a positive [35] effect on postsynaptic responses, and is therefore proposed as a wake-up call from the thalamus [30]. As mentioned, this is related to the non-linear buildup of high frequency sodium spikes that ride on a calcium plateau. Burst firing is predominant in unmyelinated C-fibers, a body wide system of behavioral relevance (= salience) detecting and transmitting fibers [38], ending in the salience network, encompassing the rostral anterior cingulate gyrus, anterior insula, dorsomedial thalamus, amygdala, hypothalamus and periaqueductal gray [39]. This network is important in hedonic homeostasis and allostasis [40], by generating feelings of unpleasantness and pleasantness, as drivers of motivational salience [38]. As such, it has been proposed that burst firing represents the detection of a salient change in the internal or external environment, whereas the content of that change is subsequently transmitted in tonic mode [30,41]. In burst mode, a neuron transmits only one bit of information corresponding to the absence or presence of a salient stimulus, i.e. with low fidelity, whereas in tonic mode a neuron attempts to faithfully relay the sensory input with as many bits as are available, i.e. with high fidelity [41] This burst-detect and tonic-transmit framework can significantly increase the fidelity of information relay by dynamically allocating bandwidth to the most salient areas of the sensory field [41]. In order to respond to the most behaviorally relevant novel stimuli burst firing has to be able to override ongoing tonic firing14. Burst firing has some characteristics that help perform this task. Except for its non-linear buildup resulting in a kind of super action potential, it has a higher signal to noise ratio than tonic firing [29], and it permits selective routing and multiplexing of information via separate pathways [42], which is evident in spinal cord stimulation for the treatment of pain. BurstDRTM stimulation is capable of modulating the medial pathway, in contrast to tonic stimulation [7,43–46], analogous to what is seen with burst stimulation in the auditory system [8,47]. Burst firing can result in recruitment of functionally connected areas through retrograde dendrodendritic transmission of subthreshold calcium mediated oscillations via gap junctions [48]. Sodium spikes can ride on these subthreshold calcium oscillations to create synchronous activity in segregated, spatially restricted, but functionally connected areas [48,49].