scholarly journals Normative cerebral cortical thickness for human visual areas

2019 ◽  
Author(s):  
Ivan Alvarez ◽  
Andrew J. Parker ◽  
Holly Bridge
Keyword(s):  
Cortical Thickness ◽  
List Type ◽  
Statistical Comparison ◽  
Broad Agreement ◽  
Visual Systems ◽  
Human Connectome Project ◽  
Subject Variability ◽  
Visual Areas ◽  
Abnormal Visual ◽  
Within Subject Variability

1AbstractStudies of changes in cerebral neocortical thickness often rely on small control samples for comparison with specific populations with abnormal visual systems. We present a normative dataset for FreeSurfer-derived cortical thickness across 25 human visual areas derived from 960 participants in the Human Connectome Project. Cortical thickness varies systematically across visual areas, in broad agreement with canonical visual system hierarchies in the dorsal and ventral pathways. In addition, cortical thickness estimates show consistent within-subject variability and reliability. Importantly, cortical thickness estimates in visual areas are well described by a normal distribution, making them amenable to direct statistical comparison.HighlightsNormative neocortical thickness values for human visual areas measured with FreeSurferA gradient of increasing neocortical thickness with visual area hierarchyConsistent within- and between-subject variability in neocortical thickness across visual areas

scholarly journals FMRI multi-scale cortical spontaneous activity: 7T vs. 3T

2021 ◽  
Author(s):  
Xiu-Xia Xing ◽  
Chao Jiang ◽  
Xiao Gao ◽  
Yin-Shan Wang ◽  
Xi-Nian Zuo
Keyword(s):  
Magnetic Field ◽  
Field Strength ◽  
Human Brain ◽  
Spontaneous Activity ◽  
Resting State ◽  
Spatial Scales ◽  
Human Connectome Project ◽  
Activity Measurements ◽  
Subject Variability ◽  
Within Subject Variability

This paper describes the use of the Human Connectome Project (HCP) data for mapping the distribution of spontaneous activity in the human brain across different spatial scales, magnets and individuals. Specifically, the resting-state functional MRI signals acquired under the HCP 3 tesla (T) and 7T magnet protocols were measured by computational methods at multiple spatial scales across the cerebral cortex using: 1) an amplitude metric on a single measuring unit (ALFF), 2) a functional homogeneity metric on a set of neighboring measuring units (ReHo) and 3) a homotopic functional connectivity metric on pairs of symmetric measuring units between the two hemispheres (VMHC). Statistical assessments on these measurements revealed that all the raw metrics were enhanced by the higher magnetic field, highlighting their dependence on magnet field strength. Measurement reliability of these global measurements were moderate to high and comparable between between 3T and 7T magnets. The differences in these measurements introduced by the higher magnetic field were spatially dependent and varied according to specific cortical regions. Specifically, the spatial contrasts of ALFF were enhanced by the 7T magnet within the anterior cortex while weakened in the posterior cortex. This is opposite for ReHo and VMHC. This scale-dependent phenomena also held true for measurement reliabilities, which were enhanced by the 7T magnet for ReHo and VMHC and weakened for ALFF. These reliability differences were primarily located in high-order associate cortex, reflecting the corresponding changes of individual differences: higher between-subject variability and lower within-subject variability for ReHo and VMHC, lower between-subject variability and higher within-subject variability for ReHo and VMHC with respect to higher magnetic field strength. Our work, for the first time, demonstrates the spatial-scale dependence of spontaneous cortical activity measurements in the human brain and their test-retest reliability across different magnet strengths, and discussed about the statistical implications for experimental design using resting-state fMRI.

scholarly journals Statistical power: implications for planning MEG studies

2019 ◽  
Author(s):  
Maximilien Chaumon ◽  
Aina Puce ◽  
Nathalie George
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Single Subject ◽  
List Type ◽  
Group Level ◽  
Spatial Properties ◽  
Human Connectome Project ◽  
Neurophysiological Studies ◽  
Subject Variability ◽  
The Impact

AbstractStatistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated “experiments” using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the “signal condition”, but not in the “noise condition”, and detected significant differences at sensor level with classical paired t-tests across subjects. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not.Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies. Rather, it highlights the importance of considering the spatial constraints underlying expected sources of activity while designing experiments.HighlightsAdequate sample size (number of subjects and trials) is key to robust neuroscienceWe simulated evoked MEG experiments and examined sensor-level detectabilityStatistical power varied by source distance, orientation & between-subject variabilityConsider source detectability at sensor-level when designing MEG studiesSample size for MEG studies? Consider source with lowest expected statistical power

Within-Subject Variability in Ingratiation as a Function of Self-Esteem and Time

2019 ◽  
Vol 18 (4) ◽  
pp. 216-226
Author(s):  
Katharina Schmitte ◽  
Bert Schreurs ◽  
Mien Segers ◽  
I. M. “Jim” Jawahar
Keyword(s):  
Individual Differences ◽  
Small Group ◽  
Self Esteem ◽  
Random Coefficient ◽  
Person Perspective ◽  
Authority Figure ◽  
Subject Variability ◽  
Over Time ◽  
Within Subject Variability

Abstract. Adopting a within-person perspective, we theorize why ingratiation use directed toward an authority figure increases over time and for whom. We posit that as the appraisal event draws closer, the salience of achieving good evaluations increases, leading to an increasing use of ingratiation. We further propose that the increase will be stronger for individuals with low relative to high self-esteem. Participants were 349 students enrolled in a small-group, tutor-led management course. Data were collected in three bi-weekly waves and analyzed using random coefficient modeling. Results show that ingratiation use increased as time to the evaluation decreased, and low self-esteem students ingratiated more as time progressed. We conclude that ingratiation use varies as a function of contextual and inter-individual differences.

scholarly journals Myelin densities in retinotopically defined dorsal visual areas of the macaque

2021 ◽  
Author(s):  
Xiaolian Li ◽  
Qi Zhu ◽  
Wim Vanduffel
Keyword(s):  
Visual Cortex ◽  
Visual Field ◽  
Cortical Thickness ◽  
New World Monkeys ◽  
Isotropic Resolution ◽  
Density Mapping ◽  
Area V2 ◽  
Visual Areas ◽  
Density Results

AbstractThe visuotopic organization of dorsal visual cortex rostral to area V2 in primates has been a longstanding source of controversy. Using sub-millimeter phase-encoded retinotopic fMRI mapping, we recently provided evidence for a surprisingly similar visuotopic organization in dorsal visual cortex of macaques compared to previously published maps in New world monkeys (Zhu and Vanduffel, Proc Natl Acad Sci USA 116:2306–2311, 2019). Although individual quadrant representations could be robustly delineated in that study, their grouping into hemifield representations remains a major challenge. Here, we combined in-vivo high-resolution myelin density mapping based on MR imaging (400 µm isotropic resolution) with fine-grained retinotopic fMRI to quantitatively compare myelin densities across retinotopically defined visual areas in macaques. Complementing previously documented differences in populational receptive-field (pRF) size and visual field signs, myelin densities of both quadrants of the dorsolateral posterior area (DLP) and area V3A are significantly different compared to dorsal and ventral area V3. Moreover, no differences in myelin density were observed between the two matching quadrants belonging to areas DLP, V3A, V1, V2 and V4, respectively. This was not the case, however, for the dorsal and ventral quadrants of area V3, which showed significant differences in MR-defined myelin densities, corroborating evidence of previous myelin staining studies. Interestingly, the pRF sizes and visual field signs of both quadrant representations in V3 are not different. Although myelin density correlates with curvature and anticorrelates with cortical thickness when measured across the entire cortex, exactly as in humans, the myelin density results in the visual areas cannot be explained by variability in cortical thickness and curvature between these areas. The present myelin density results largely support our previous model to group the two quadrants of DLP and V3A, rather than grouping DLP- with V3v into a single area VLP, or V3d with V3A+ into DM.

scholarly journals Importance of between and within Subject Variability in Extracellular Vesicle Abundance and Cargo when Performing Biomarker Analyses

Cells ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 485
Author(s):  
Lauren A. Newman ◽  
Alia Fahmy ◽  
Michael J. Sorich ◽  
Oliver G. Best ◽  
Andrew Rowland ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Drug Exposure ◽  
Extracellular Vesicle ◽  
The Novel ◽  
Protein Markers ◽  
Diurnal Variability ◽  
Normal Ranges ◽  
Sampling Protocols ◽  
Subject Variability ◽  
Within Subject Variability

Small extracellular vesicles (sEV) have emerged as a potential rich source of biomarkers in human blood and present the intriguing potential for a ‘liquid biopsy’ to track disease and the effectiveness of interventions. Recently, we have further demonstrated the potential for EV derived biomarkers to account for variability in drug exposure. This study sought to evaluate the variability in abundance and cargo of global and liver-specific circulating sEV, within (diurnal) and between individuals in a cohort of healthy subjects (n = 10). We present normal ranges for EV concentration and size and expression of generic EV protein markers and the liver-specific asialoglycoprotein receptor 1 (ASGR1) in samples collected in the morning and afternoon. EV abundance and cargo was generally not affected by fasting, except CD9 which exhibited a statistically significant increase (p = 0.018). Diurnal variability was observed in the expression of CD81 and ASGR1, which significantly decreased (p = 0.011) and increased (p = 0.009), respectively. These results have potential implications for study sampling protocols and normalisation of biomarker data when considering the expression of sEV derived cargo as a biomarker strategy. Specifically, the novel finding that liver-specific EVs exhibit diurnal variability in healthy subjects should have broad implications in the study of drug metabolism and development of minimally invasive biomarkers for liver disease.

Head positioning control in a gravito-inertial field and in normal gravity

2004 ◽  
Vol 14 (4) ◽  
pp. 321-333
Author(s):  
Frédéric Sarès ◽  
Christophe Bourdin ◽  
Jean-Michel Prieur ◽  
Jean-Louis Vercher ◽  
Jean-Pierre Menu ◽  
...  
Keyword(s):  
Inertial Force ◽  
The Body ◽  
Body Axis ◽  
Positioning Control ◽  
Head Positioning ◽  
Subject Variability ◽  
Visual Frame ◽  
Vector Orientation ◽  
The Right ◽  
Within Subject Variability

The way in which the head is controlled in roll was investigated by dissociating the body axis and the gravito-inertial force orientation. Seated subjects (N = 8) were requested to align their head with their trunk, 30° to the left, 30° to the right or with the gravito-inertial vector, before, during (Per Rotation), after off-center rotation and on a tilted chair without rotation (Tilted). The gravito-inertial vector angle during rotation and the chair tilt angle were identical (17°). The subjects were either in total darkness or facing a visual frame that was fixed to the trunk. Both final error and within-subject variability of head positioning increased when the body axis and the gravito-inertial vector were dissociated (Per Rotation and Tilted). However, the behavior was different depending on whether the subjects were in the Tilted or Per Rotation conditions. The presentation of the visual frame reduced the within-subject variability and modified the perception of the gravito-inertial vector's orientation on the tilted chair. As head positioning with respect to the body and sensing of the gravito-inertial vector are modified when body axis and gravito-inertial vector orientation are dissociated, the observed decrease in performance while executing motor tasks in a gravito-inertial field may be at least in part attributed to the inaccurate sensing of head position.

scholarly journals Training neural networks to recognize speech increased their correspondence to the human auditory pathway but did not yield a shared hierarchy of acoustic features

2021 ◽  
Author(s):  
Jessica A.F. Thompson ◽  
Yoshua Bengio ◽  
Elia Formisano ◽  
Marc Schönwiesner
Keyword(s):  
Neural Networks ◽  
Recognition Task ◽  
Single Layer ◽  
Auditory Pathway ◽  
7 Tesla ◽  
List Type ◽  
Acoustic Features ◽  
Visual Areas ◽  
Cortical Regions ◽  
Fully Connected

AbstractThe correspondence between the activity of artificial neurons in convolutional neural networks (CNNs) trained to recognize objects in images and neural activity collected throughout the primate visual system has been well documented. Shallower layers of CNNs are typically more similar to early visual areas and deeper layers tend to be more similar to later visual areas, providing evidence for a shared representational hierarchy. This phenomenon has not been thoroughly studied in the auditory domain. Here, we compared the representations of CNNs trained to recognize speech (triphone recognition) to 7-Tesla fMRI activity collected throughout the human auditory pathway, including subcortical and cortical regions, while participants listened to speech. We found no evidence for a shared representational hierarchy of acoustic speech features. Instead, all auditory regions of interest were most similar to a single layer of the CNNs: the first fully-connected layer. This layer sits at the boundary between the relatively task-general intermediate layers and the highly task-specific final layers. This suggests that alternative architectural designs and/or training objectives may be needed to achieve fine-grained layer-wise correspondence with the human auditory pathway.HighlightsTrained CNNs more similar to auditory fMRI activity than untrainedNo evidence of a shared representational hierarchy for acoustic featuresAll ROIs were most similar to the first fully-connected layerCNN performance on speech recognition task positively associated with fmri similarity

scholarly journals Surface-based analysis of cortical thickness and volume loss in Alzheimer’s disease

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily Iannopollo ◽  
Ryan Plunkett ◽  
Kara Garcia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Surface Matching ◽  
Cortical Surface ◽  
Volume Loss ◽  
Cortical Volume ◽  
Human Connectome Project ◽  
And Control

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness.   References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207.  4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18. 

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