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> Anatomy > Body Part > Diencephalon

Diencephalon

The diencephalon is a central region of the brain that plays a crucial role in various neurological functions.
It is composed of several structures, including the thalamus, hypothalamus, and epithalamus.
The diencephalon serves as a relay station, transmitting sensory information from the peripheral nervous system to the cerebral cortex.
It is also involved in the regulation of essential bodily functions, such as sleep, hunger, thirst, and hormone secretion.
Researchers studying the diencephalon may utilize PubCompare.ai's AI-driven platform to optimize their research by easily locating protocols from literature, pre-prints, and patents, while leveraging AI-powered comparisons to identify the most accurate and reproducible methods.
This can enhance research efficiency and accuracy, leading to a better understanding of the diencephalon's complex role in the brain. (Note: The word 'diencephalon' may be misspelled as 'diencepahlon' in this description.)

Most cited protocols related to «Diencephalon»

1
Dynamic Causal Modeling of fMRI Data
Dynamic Causal Modelling is a framework for fitting differential equation models of neuronal activity to brain imaging data using Bayesian inference. The DCM approach can be applied to functional Magnetic Resonance Imaging (fMRI), Electroencephalographic (EEG), Magnetoencephalographic (MEG), and Local Field Potential (LFP) data [22] (link). The empirical work in this paper uses DCM for fMRI. DCMs for fMRI comprise a bilinear model for the neurodynamics and an extended Balloon model [23] (link) for the hemodynamics. The neurodynamics are described by the following multivariate differential equation where indexes continuous time and the dot notation denotes a time derivative. The th entry in corresponds to neuronal activity in the th region, and is the th experimental input.
A DCM is characterised by a set of ‘exogenous connections’, , that specify which regions are connected and whether these connections are unidirectional or bidirectional. We also define a set of input connections, , that specify which inputs are connected to which regions, and a set of modulatory connections, , that specify which intrinsic connections can be changed by which inputs. The overall specification of input, intrinsic and modulatory connectivity comprise our assumptions about model structure. This in turn represents a scientific hypothesis about the structure of the large-scale neuronal network mediating the underlying cognitive function. A schematic of a DCM is shown in Figure 1.
In DCM, neuronal activity gives rise to fMRI activity by a dynamic process described by an extended Balloon model [24] for each region. This specifies how changes in neuronal activity give rise to changes in blood oxygenation that are measured with fMRI. It involves a set of hemodynamic state variables, state equations and hemodynamic parameters, . In brief, for the th region, neuronal activity causes an increase in vasodilatory signal that is subject to autoregulatory feedback. Inflow responds in proportion to this signal with concomitant changes in blood volume and deoxyhemoglobin content . Outflow is related to volume through Grubb's exponent
[20] (link). The oxygen extraction is a function of flow where is resting oxygen extraction fraction. The Blood Oxygenation Level Dependent (BOLD) signal is then taken to be a static nonlinear function of volume and deoxyhemoglobin that comprises a volume-weighted sum of extra- and intra-vascular signals [20] (link)
where is resting blood volume fraction. The hemodynamic parameters comprise and are specific to each brain region. Together these equations describe a nonlinear hemodynamic process that converts neuronal activity in the th region to the fMRI signal (which is additionally corrupted by additive Gaussian noise). Full details are given in [20] (link),[23] (link).
In DCM, model parameters are estimated using Bayesian methods. Usually, the parameters are of greatest interest as these describe how connections between brain regions are dependent on experimental manipulations. For a given DCM indexed by , a prior distribution, is specified using biophysical and dynamic constraints [20] (link). The likelihood, can be computed by numerically integrating the neurodynamic (equation 1) and hemodynamic processes (equation 2). The posterior density is then estimated using a nonlinear variational approach described in [23] (link),[25] (link). Other Bayesian estimation algorithms can, of course, be used to approximate the posterior density. Reassuringly, posterior confidence regions found using the nonlinear variational approach have been found to be very similar to those obtained using a computationally more expensive sample-based algorithm [26] (link).
Penny W.D., Stephan K.E., Daunizeau J., Rosa M.J., Friston K.J., Schofield T.M, & Leff A.P. (2010). Comparing Families of Dynamic Causal Models. PLoS Computational Biology, 6(3), e1000709.
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Publication 2010
BLOOD Blood Vessel Blood Volume Brain Cell Respiration Cognition deoxyhemoglobin Diencephalon Electroencephalography Hemodynamics Homeostasis Neurons Oxygen Vasodilation
2
Interpreting Parameter Estimates in Presence of Collinearity
Collinearity can be problematic when interpreting model results, and a common misunderstanding in fMRI analysis is that collinearity fully ruins any ability to interpret the results. In all cases of collinearity inferences are valid, meaning the type I error rate is controlled. The primary worry is that the highly variable parameter estimates may be much larger or smaller than the true magnitude, but when collinearity causes this to occur in the model the estimated variance is also large. Thus, although the parameter estimates are highly variable, they are unbiased and Type 1 error rate is preserved, so inferences remain valid in the presence of collinearity [5 ]. This means if multiple estimates from independent studies (or subjects) are averaged, the mean will converge on the true value. Most fMRI analysis software packages use a multistage approach to analyze data, otherwise known as the summary statistics approach to the mixed model [6 , 7 (link)]. The first stage of analysis involves analyzing each run of data independently and, assuming there is a single run per subject, the second stage combines first level estimates in a group model. The impact of collinearity depends on what stage of modeling the collinearity occurred (single subject or in group model). If the collinearity occurs in the first level, say if two explanatory variables for two trial types are correlated and the interest is in the effect of the first trial type, the individual subject parameter estimates will be highly variable, but when averaged at the group level the estimates that are too large tend to balance out with those that are too small to arrive at an estimate that is closer to the true mean estimate. On the other hand, collinearity between age and gender would occur in the group model and in this case the parameter estimate with, say, age may be much larger or smaller than the true effect such that if the magnitude of the effect is of great interest, care should be taken in interpreting it. The confidence interval associated with the estimate will reflect the wide range of parameter values that are consistent with the data, so if a region of interest analysis is performed this should be included as well. Note that this would also be the case in analyses, such as VBM (voxel based morphometry), where there is only one stage in the analysis analyzing relationships between brain measures and sets of covariates that tend to be highly correlated. The inferences are valid, but in the presence of collinearity, the parameter estimates will be highly variable.
Mumford J.A., Poline J.B, & Poldrack R.A. (2015). Orthogonalization of Regressors in fMRI Models. PLoS ONE, 10(4), e0126255.
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Publication 2015
Diencephalon fMRI
3
Multivariate Neurophysiological Modeling of Stress Response

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Publication 2009
Brain Cloning Vectors Diencephalon fMRI Heart physiology Speech
4
Myelin Mapping via T1w/T2w Ratio Imaging
The T2w image was registered to the T1w image using FSL’s FLIRT (Jenkinson et al., 2002 (link)) with 6 parameters (rigid body) and the mutual information cost function. This registration precisely aligned all brain regions except for small portions of ventral orbitofrontal cortex, overlying the sphenoid sinus, and inferior temporal cortex, overlying the mastoid air cells. In these areas, the gradient echo T1w and spin echo T2w data were affected differently by magnetic susceptibility-induced signal dephasing and signal loss (see artifactual results). The T2w image was resampled using the spline interpolation algorithm of FSL’s applywarp tool. Spline interpolation minimizes the white matter and CSF contamination of grey matter voxels that would result from the volumetric blurring inherent in trilinear interpolation. Spline interpolation yielded similar results when applied only to the T2w image or when applied separately to both the T1w and T2w images so that they were resampled the same number of times.
Division of the T1w image by the aligned T2w image mathematically cancels the signal intensity bias related to the sensitivity profile of the radio frequency receiver coils, which is the same in both images. Taking the ratio also increases the contrast related to myelin content. A simple approximation (Eq. 1) explains both effects: if myelin contrast is represented by x in the T1w image and 1/x in the T2w image and the receive bias field is represented by b in both images, the T1w/T2w ratio image equals x2, i.e. enhanced myelin contrast, with no bias field contribution. Because the noise in the T1w and T2w images is uncorrelated, there is increased myelin contrast relative to the noise (i.e. increased CNR).
Alternative bias field correction methods such as FSL’s FAST (Zhang et al., 2002 ) and MINC’s nu_correct (Sled et al., 1998 ) are not sufficiently accurate for the myelin mapping technique presented here. As demonstrated below, myelin mapping relies on detection of subtle differences in grey matter intensity that are obscured by either incomplete correction of the bias field or by errors in the bias field that can occur around the exterior of the brain. These errors take the form of local inhomogeneities between superficial cortex on the gyral crowns and deeper cortex in the fundi of sulci, and they result from the steep image intensity gradient between brain tissue and extra-cerebral tissues. These errors become more apparent when one runs a bias field correction utility multiple times in an attempt to completely remove the bias field. Intensity variations due to transmit field biases are minimal when using body transmit coils, as used here with the Siemens 3T Trios, because such coils produce very uniform transmit fields over the head. Further, some of the residual biases from the transmit field may also be reduced when dividing the images, since, while the transmit profiles between the two sequences are different; they are correlated. Indeed, there was no discernible global signal bias in our T1w/T2w ratio images, as the low frequency variations in grey and white matter were anti-correlated. We would expect them to be correlated if a bias field were present, as they are in the raw T1w and T2w images. These assumptions will not apply at higher resonant frequencies (i.e. at higher field strengths like 7T) where local transmit coils are used and where the transmit field biases are much stronger (Van de Moortele et al., 2009 (link)). In this case, it will be necessary to use sequences for the ratio that have very similar transmit profiles.
In volume slices of T1w and T2w images, interesting local signal inhomogeneities are evident in the grey matter, particularly in regions such as the central sulcus (Fig. 1A,B). These inhomogeneities are enhanced in the T1w/T2w ratio images (Fig. 1C). When a color palette is used instead of grey scale, the differences become even more apparent (Fig. 1D). The boundaries drawn on the colorized T1w/T2w image in Figure 1D represent putative transitions between cortical areas (see Results). Indeed, a direct comparison between myelin stained histology and T1 contrast in the central sulcus reported a similar border between areas 4 and 3a that was aligned in both methodologies (Geyer et al., 2011 (link)).
Glasser M.F, & Van Essen D.C. (2011). Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI. The Journal of Neuroscience, 31(32), 11597-11616.
Publication 2011
Brain Cells Cortex, Cerebral Crowns Diencephalon ECHO protocol Gray Matter Head Human Body Hypersensitivity Muscle Rigidity Myelin Sheath Orbitofrontal Cortex Process, Mastoid Sphenoid Sinus STEEP1 protein, human Susceptibility, Disease Temporal Lobe Tissues TRIO protein, human White Matter
5
Multimodal Brain Imaging Analysis Pipeline
We utilized the Freesurfer pipeline version 5.1.0 (http://surfer.nmr.mgh.harvard.edu/), which includes removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Segonne et al. 2004 (link)), automated Talairach transformation, segmentation of the subcortical white matter and deep grey matter volumetric structures (Fischl et al. 2002 (link); Fischl et al. 2004a (link); Segonne et al. 2004 (link)) intensity normalization (Sled et al. 1998 (link)), tessellation of the grey matter white matter boundary, automated topology correction (Fischl et al. 2001 (link); Segonne et al. 2007 (link)), and surface deformation following intensity gradients to optimally place the grey/white and grey/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al. 1999 (link); Dale and Sereno 1993 (link); Fischl and Dale 2000 (link)). Once the cortical models are complete, registration to a spherical atlas takes place which utilizes individual cortical folding patterns to match cortical geometry across subjects (Fischl et al. 1999 (link)). This is followed by parcellation of the cerebral cortex into units based on gyral and sulcal structure (Desikan et al. 2006 (link); Fischl et al. 2004b (link)). The pipeline generated 68 cortical thickness, cortical volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures (34 from each hemisphere) and 46 regional subcortical volumes. Volumes of white matter hypointensities, optic chiasm, right and left vessel, and left and right choroid plexus were excluded from further analysis. Cortical thickness and volumetric measures from the right and left side were averaged (Fjell et al. 2009 (link); Walhovd et al. 2011 (link)). In total 259 variables obtained from the pipeline were used as input variables for the OPLS classification, 34 cortical regions (7 types of measures) and 21 regional volumes (Table 2). Figure 1 illustrates the location of both the cortical and subcortical regions. This segmentation approach has been used for multivariate classification of Alzheimer’s disease and healthy controls (Westman et al. 2011d (link)), neuropsychological-image analysis (Liu et al. 2010c (link), 2011 (link)), imaging-genetic analysis (Liu et al. 2010a (link), b (link)) and biomarker discovery (Thambisetty et al. 2010 (link)).

Variable included in OPLS analysis

Cortical measuresaSubcortical measuresb
Banks of superior temporal sulcusThird ventricle
Caudal anterior cingulateFourth ventricle
Caudal middle frontal gyrusInferior lateral ventricle
Cuneus cortexLateral ventricle
Entorhinal cortexCerebrospinal fluid (CSF)
Fusiform gyrusAccumbens
Inferior parietal cortexAmygdala
Inferior temporal gyrusBrainstem
Isthmus of cingulate cortexCaudate
Lateral occipital cortexCerebellum cortex
Lateral orbitofronral cortexCerebellum white matter
Lingual gyrusCorpus callosum anterior
Medial orbitalfrontal cortexCorpus callosum central
Middle temporal gyrusCorpus callosum midanterior
Parahippocampal gyrusCorpus callosum midposterior
Paracentral sulcusCorpus callosum posterior
Frontal operculumHippocampus
Orbital operculumPutamen
Triangular part of inferior frontal gyrusPallidum
Pericalcarine cortexThalamus proper
Postcentral gyrusVentral diencephalon (DC)
Posterior cingulate cortex
Precentral gyrus
Precuneus cortex
Rostral anterior cingulate cortex
Rostral middle frontal gyrus
Superior frontal gyrus
Superior parietal gyrus
Superior temporal gyrus
Supramarginal gyrus
Frontal pole
Temporal pole
Transverse temporal cortex
Insular

259 variables in total included in OPLS analysis

aCortical measures = 34 regions (cortical volumes, cortical thickness, surface area, mean curvature, gaussian curvature, folding index and curvature index)

bSubcortical measures = 21 regions (volumes)

Representations of ROIs included as candidate input variables in the multivariate OPLS model. a Coronal view of a T1-weighted MPRAGE image displaying the regional volumes. b Lateral and medial views of the grey matter surface illustrating the 34 regional cortical thickness measures

Westman E., Aguilar C., Muehlboeck J.S, & Simmons A. (2012). Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment. Brain Topography, 26(1), 9-23.
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Publication 2012
Alzheimer's Disease Biological Markers Blood Vessel Brain Cerebrospinal Fluid Childbirth Corpus Callosum Cortex, Cerebral Diencephalon Gray Matter Gyrus, Anterior Cingulate Hybrids Optic Chiasms Plexus, Chorioid Posterior Cingulate Cortex Tissues White Matter

Most recents protocols related to «Diencephalon»

1
Meta-analysis of Infant Brain Response to Linguistic Regularities
We aggregated 23 published and unpublished fNIRS studies, conducted in four different laboratories, testing brain responses of typically developing infants to two different types of linguistic regularities: repetition-based regularities (R) and diversity-, i.e., nonrepetition-based regularities (N). The studies were identified by searching through PubMed and Google Scholar, using the search strings “repetition-based regularity,” “rule learning,” “fNIRS,” and “infants.” Exclusion criteria included (i) testing atypical populations or (ii) using methods other than NIRS. Papers including more than one study were considered separate studies. Of the 43 hits, those that met either of the exclusion criteria were discarded, leaving 12 published studies.27 (link)–31 (link, link, link, link) Additionally, 11 studies from the last author’s laboratory were added. These studies were not published in peer-reviewed articles, although some of them are available online in PhD dissertations (Table 1). We know of no other unpublished studies.
The studies used similar methodology, e.g., similar stimuli and experimental designs, but it addressed slightly different theoretical questions and, as a result, varied along a few dimensions (e.g., the auditory versus visual nature of the stimuli, see Table 1). We used these factors as moderators in the meta-analysis. Furthermore, studies varied in whether they tested repetition-based regularities, diversity-based regularities, or both (Table 1). Consequently, we conducted three separate meta-analyses evaluating the effect sizes of (i) the comparison between brain responses to repetitions versus a zero baseline (“R versus 0”); (ii) the comparison between brain responses to diversity (nonrepetition) versus a zero baseline (“N versus 0”); and (iii) the comparison between brain responses to repetitions versus diversity (“R versus N”). A specific study may have contributed to just one or several of the comparisons (see the last column of Table 1). As a result, 23 studies were included in the final analysis for the R versus 0 comparison, 19 in the analysis of the R versus N comparison, and 17 in the analysis of the N versus 0 comparison. Details can be found in Table 1. Additionally, Table 2 reports the most relevant technical details of NIRS data acquisition.
The study comprised data from a total of 487 infants, aged between 0 and 9 months, tested in four different countries (Canada, France, Italy, and USA). Table 1 provides information about each study’s individual sample size.
Gemignani J., de la Cruz-Pavía I., Martinez A., Nallet C., Pasquini A., Lucarini G., Cavicchiolo F, & Gervain J. (2023). Reproducibility of infant fNIRS studies: a meta-analytic approach. Neurophotonics, 10(2), 023518.
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Publication 2023
Auditory Perception Brain Diencephalon Infant Population Group Spectroscopy, Near-Infrared
2
Effective Connectivity in Emotion Processing
Dynamic causal modeling was performed using SPM12. This is a Bayesian framework to infer directed (effective) connectivity between brain regions (Friston et al., 2003 (link)). Since the purpose of the present study was to investigate the neural circuits differentially coding for positive and negative valence in the implicit processing of facial expressions and words, two different DCMs were performed, one for faces (Face-DCM) and one for words (Word-DCM).
Ballotta D., Maramotti R., Borelli E., Lui F, & Pagnoni G. (2023). Neural correlates of emotional valence for faces and words. Frontiers in Psychology, 14, 1055054.
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Publication 2023
Diencephalon Face Nervousness
3
Investigating Brain Age Gaps in Schizophrenia
Independent t test and Chi-square test were used for the statistical analyses of continuous and categorical clinicodemographic variables, respectively. The P value was set at 0.05.
We used analysis of covariance (ANCOVA) to examine the differences in the brain age gaps between participants with schizophrenia and HCs in the TAMI and BT cohorts, with chronological age, sex, MMSE score, and duration of education as the covariates. Moreover, the false discovery rate (FDR) method was used to correct P values for multiple comparisons [53 ]. After FDR correction, the significance level was set at 0.05. The partial eta-squared (partial η2) values were calculated as effect size measures. The BrainNet Viewer (http://www.nitrc.org/projects/bnv/) was used for result visualizations [54 (link)].
After comparing the brain age gaps between the two groups, we performed a multiple regression analysis for brain regions that aged faster than usual. In each regression model, the dependent variable was brain age gaps for a given brain region; the independent variables were clinicodemographic characteristics, including PANSS subscale scores (for positive symptoms, negative symptoms, and general psychopathology symptoms), illness duration, age of onset, history of nicotine use, and body mass index; and the control variables were chronological age and sex. In addition, after excluding participants without any antipsychotic information and controlling for chronological age and sex, we used a regression analysis to investigate the association between brain age gaps and chlorpromazine (CPZ) equivalent dosage. Finally, the FDR method was used to control for differences in the comparison procedures.
Zhu J.D., Wu Y.F., Tsai S.J., Lin C.P, & Yang A.C. (2023). Investigating brain aging trajectory deviations in different brain regions of individuals with schizophrenia using multimodal magnetic resonance imaging and brain-age prediction: a multicenter study. Translational Psychiatry, 13, 82.
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Publication 2023
Antipsychotic Agents Brain Chlorpromazine Diencephalon Index, Body Mass Mini Mental State Examination Nicotine Schizophrenia
4
Brain Activity Differences in Internet Gaming Disorder
To explore the brain activity differences between the RE-IGD individuals and the PER-IGD individuals, a two-sample t-test was performed on the normalized ReHo with REST software. The result and statistical map were set at a combined threshold of p < 0.05 (AlphaSim corrected) and a minimum cluster size of 135 voxels.
To evaluate the association of altered ReHo in different brain regions, we performed Spearman’s correlation analysis between mean ReHo values and self-reported craving scores and IAT scores of PER-IGD individuals and RE- IGD individuals. Correlations between brain response features and scale scores can help us better understand the main findings.
Liu X., Zheng Y., Niculescu M., Liang Q., Yang A., Dong G., Gao Z., Lin P., Liu Y., Chen L, & Xu D. (2023). The involvement of spontaneous brain activity in natural recovery from internet gaming disorder: A resting-state fMRI study. Frontiers in Psychiatry, 14, 1093784.
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Publication 2023
Brain Diencephalon
5
Whole-Brain Computational Modeling of Neural Dynamics
Macroscale whole-brain computational models represent regional activity in terms of two key ingredients: (i) a biophysical model of each region's local dynamics; and (ii) inter-regional anatomical connectivity. Thus, such in silico models provide a well-suited tool to investigate how the structural connectivity of the brain shapes the corresponding macroscale neural dynamics (Cabral et al., 2017 (link); Cofré et al., 2020 (link); Deco and Kringelbach, 2014 (link); Demirtaş et al., 2019 (link); Kringelbach and Deco, 2020 (link); Shine et al., 2021 (link); Wang et al., 2019 (link)). In particular, the Dynamic Mean Field (DMF) model employed here simulates each region (defined via an anatomical parcellation scheme) as a macroscopic neural field comprising mutually coupled excitatory and inhibitory populations (80% excitatory and 20% inhibitory), providing a neurobiologically plausible account of regional neuronal firing rate. Regions are then connected according to empirical anatomical connectivity obtained e.g. from DWI data (Deco et al., 2014 (link); G. 2013 (link); Deco and Jirsa, 2012 (link)). The reader is referred to (Deco et al., 2018 (link); Herzog et al., 2022 (link); 2020 (link); Luppi et al., 2022b (link)) for details of the DMF model and its implementation. Due to its multi-platform compatibility, low memory usage, and high speed, we used the recently developed FastDMF library (Herzog et al., 2022 (link)), available online at https://www.gitlab.com/concog/fastdmf.
The structural connectivity (SC) for the DMF model used here was obtained by following the procedure described by Wang et al. (2019) (link) to derive a consensus structural connectivity matrix. A consensus matrix A was obtained separately for each group (healthy controls, MCS patients, UWS patients) as follows: for each pair of regions i and j, if more than half of subjects had non-zero connection i and j, Aij was set to the average across all subjects with non-zero connections between i and j. Otherwise, Aij was set to zero.
The DMF model has one free parameter, known as “global coupling” and denoted by G, which accounts for differences in transmission between brain regions, considering the effects of neurotransmission but also synaptic plasticity mechanisms. Thus, separately for each group, we used a model informed by that group's consensus connectome to generate 40 simulations for each value of G between 0.1 and 2.5, using increments of 0.1. Finally, we set the G parameter to the value just before the one at which the simulated firing of each model became unstable, reflecting a near-critical regime.
Subsequently, for each group, 40 further simulations were obtained from the corresponding DMF model with the optimal G parameter. A Balloon-Windkessel hemodynamic model (Friston et al., 2003 (link)) was then used to turn simulated regional neuronal activity into simulated regional BOLD signal. Finally, simulated regional BOLD signal was bandpass filtered in the same range as the empirical data (0.008–0.09 Hz, or 0.04–0.07 Hz for the intrinsic ignition analysis).
As an alternative way of finding the most suitable value of G for the simulation of each condition, we adopted the approach previously described (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)) which aims to obtain the best match between empirical and simulated functional connectivity dynamics. First, we quantified empirical functional connectivity dynamics (FCD) in terms of Pearson correlation between regional BOLD timeseries, computed within a sliding window of 30 TRs with increments of 3 TRs (Deco et al., 2018 (link); Hansen et al., 2015 (link); Herzog et al., 2020 (link); Luppi et al., 2022b (link)). Subsequently, the resulting matrices of functional connectivity at times tx and ty were themselves correlated, for each pair of timepoints tx and ty, thereby obtaining an FCD matrix of time-versus-time correlations. Thus, each entry in the FCD matrix represents the similarity between functional connectivity patterns at different points in time. This procedure was repeated for each subject of each group (controls, MCS, and UWS). For each simulation at each value of G, we used the Kolmogorov-Smirnov distance to compare the histograms of empirical (group-wise) and simulated FCD values (obtained from the upper triangular FCD matrix). Finally, we set the model's G parameter to the value that was observed to minimize the mean KS distance - corresponding to the model that is best capable of simulating the temporal dynamics of resting-state brain functional connectivity observed in the corresponding group (Figure S8). After having found the value of G for each condition, simulated BOLD signals were obtained as described above. This same procedure was also used for fitting the DMF model based on each individual's structural connectome, simulating BOLD signals to fit their own empirical FCD.
Luppi A.I., Mediano P.A., Rosas F.E., Allanson J., Pickard J.D., Williams G.B., Craig M.M., Finoia P., Peattie A.R., Coppola P., Menon D.K., Bor D, & Stamatakis E.A. (2023). Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage, 269, 119926.
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Publication 2023
Brain Connectome Diencephalon DNA Library Hemodynamics Memory Nervousness Neuronal Plasticity Neurons Patients Psychological Inhibition Strains Synaptic Transmission Transmission, Communicable Disease

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More about "Diencephalon"

interbrain, midbrain, thalamus, hypothalamus, epithalamus, PubCompare.ai, MATLAB, Prism 6, Prism 9, GraphPad Prism 7, SPSS 20.0, TRIzol reagent, Prism 8, MS-222, SPSS Statistics 22, GE Discovery MR750