Schizophrenia (SCZ) is associated with structural brain changes, with considerable variation in the extent to which these cortical regions are influenced. We present a novel metric that summarises individual structural variation across the brain, while considering prior effect sizes, established via meta-analysis. We determine individual participant deviation from a within-sample-norm across structural MRI regions of interest (ROIs). For each participant, we weight the normalised deviation of each ROI by the effect size (Cohen's d) of the difference between SCZ/control for the corresponding ROI from the SCZ Enhancing Neuroimaging Genomics through Meta-Analysis working group. We generate a morphometric risk score (MRS) representing the average of these weighted deviations. We investigate if SCZ-MRS is elevated in a SCZ case/control sample (NCASE = 50; NCONTROL = 125), a replication sample (NCASE = 23; NCONTROL = 20) and a sample of asymptomatic young adults with extreme SCZ polygenic risk (NHIGH-SCZ-PRS = 95; NLOW-SCZ-PRS = 94). SCZ cases had higher SCZ-MRS than healthy controls in both samples (Study 1: β = 0.62, P < 0.001; Study 2: β = 0.81, P = 0.018). The high liability SCZ-PRS group also had a higher SCZ-MRS (Study 3: β = 0.29, P = 0.044). Furthermore, the SCZ-MRS was uniquely associated with SCZ status, but not attention-deficit hyperactivity disorder (ADHD), whereas an ADHD-MRS was linked to ADHD status, but not SCZ. This approach provides a promising solution when considering individual heterogeneity in SCZ-related brain alterations by identifying individual's patterns of structural brain-wide alterations.
Aims and scope.EJN publishes original research articles and reviews in the broad fields of molecular, cellular, systems, behavioral, and cognitive neurosciences.EJN aims to advance our understanding of the nervous system in health and disease, thereby improving the diagnosis and treatment of neuro psychiatric and neurodegenerative disorders.
The accompanying data is the data for the study: Perry, G. (2016) 'The visual gamma response to faces reflects the presence of sensory evidence and not awareness of the stimulus', Royal Society Open Science. For methods used to collect the data see that publication. Behavioural data: threshold_data.csv - Data from the session in which individuals' psychmetric functions for face detection were acquired. Column 1 contains partcipant number. Colums 2-10 contain response rates to each level of phase scrambling (w). Data in each cell corresponds to the proprtion of times the participant responded that they saw face for the corresponding value of w. From the resulting function fits the values of w required for each participant to identify the face 0.5% (subthreshold), 50% (threshold) and 99.5% (suprathreshold) were derived and these are given in colums 11-13. response_data.csv - Response data from the MEG session. Column 1 contains partcipant number. Colums 2-7 give total number of trials that partcipants reported seeing ('seen') or not seeing ('unseen') a face in each of the three conditions. Colums 8-9 give the number of trials in each of the analysed sub-conditions ('subthreshold unseen', 'threshold unseen', 'threshold seen', 'suprathreshold seen') after trial exclusions. MEG data: virtual_sensor_coordinates.csv - Coordinates in Talairach space for the locations used for virtual sensor analysis (as well as group mean and standard deviation). Dashed cells correspond to individuals for which no location was found. gamma_amplitude.csv - Gamma amplitudes used in the main t-test analyses. Column 1 contains partcipant number. Columns 2-9 contains mean gamma amplitude for each participant by sub-condition and hemisphere. virtual_sensor_participant_x_y_z.csv contains the virtual sensor timeseries for participant x from trials in sub-condition y from hemsiphere z. Column one gives time (in ms relative to stimulus onset). Subsequenct columns give the virtual sensor data for each trial (trials containing artefacts have been removed). tf_participant_x_y_z.csv contains the time-frequency data for participant x from sub-condition y from hemsiphere z. Column 1 gives time relative to stimulus onset (in ms), row 1 gives frequency (in Hz), other columns/rows give data at the corresponding time and frequency as % amplitude relative to baseline.
Abstract This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18–63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.
Abstract Neuronal oscillations in the gamma frequency range play an important role in stimulus processing in the brain. The frequency of these oscillations can vary widely between participants and is strongly genetically determined, but the cause of this variability is not understood. Previous studies have reported correlations between individual differences in gamma frequency and the concentration of the inhibitory neurotransmitter, gamma‐aminobutyric acid (GABA), as well as with age and primary visual cortex (V1) area and thickness. This study assessed the relationships between all of these variables in the same group of participants. There were no significant correlations between gamma frequency and GABA+ concentration, V1 area or V1 thickness, although the relationship with GABA+/Cr approached significance. Considering age as a covariate further reduced the strength of all correlations and, in an additional dataset with a larger age range, gamma frequency was strongly inversely correlated with age but not V1 thickness or area, suggesting that age modulates gamma frequency via an additional, as yet unknown, mechanism. Consistent with other recent studies, these findings do not demonstrate a clear relationship between gamma frequency and GABA+ concentration. Further investigation of additional variables and the interactions between them will be necessary in order to more accurately determine predictors of the frequency of gamma oscillations.
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