Laser stimulation of Aδ-fibre nociceptors in the skin evokes nociceptive-specific brain responses (laser-evoked potentials, LEPs). The largest vertex complex (N2–P2) is widely used to assess ...nociceptive pathways in physiological and clinical studies. The aim of this study was to develop an automated method to measure amplitudes and latencies of the N2 and P2 peaks on a single-trial basis.
LEPs were recorded after Nd:YAP laser stimulation of the left hand dorsum in 7 normal volunteers. For each subject, a basis set of 4 regressors (the N2 and P2 waveforms and their respective temporal derivatives) was derived from the time-averaged data and regressed against every single-trial LEP response. This provided a separate quantitative estimate of amplitude and latency for the N2 and P2 components of each trial.
All estimates of LEP parameters correlated significantly with the corresponding measurements performed by a human expert (N2 amplitude:
R
2=0.70; P2 amplitude:
R
2=0.70; N2 latency:
R
2=0.81; P2 latency:
R
2=0.59. All
P<0.0001). Furthermore, regression analysis was able to extract an LEP response from a subset of the trials that had been classified by the human expert as without response.
This method provides a simple, fast and unbiased measurement of different components of single-trial LEP responses.
This method is particularly desirable in several experimental conditions (e.g. drug studies, correlations with experimental variables, simultaneous EEG/fMRI and low signal-to-noise ratio data) and in clinical practice. The described multiple linear regression approach can be easily implemented for measuring evoked potentials in other sensory modalities.
The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is ...unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.
After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for ...post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms.
•Why only some moments within a trauma intrude while others do not is unclear.•Neuroimaging may provide further clues as to why this is the case.•Multivariate pattern analysis, a recent neuroimaging analysis tool, was able to predict intrusive memories.•Those brain networks involved in intrusive memory prediction are presented.•Multivariate pattern analysis may inform future innovation in mental health.
A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no ...single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.
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The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. ...Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time.
Background. Motor practice is an important component of neurorehabilitation. Imaging studies in healthy individuals show that dynamic brain activation changes with practice. Defining patterns of ...functional brain plasticity associated with motor practice following stroke could guide rehabilitation. Objective. The authors aimed to test whether practice-related changes in brain activity differ after stroke and to explore spatial relationships between activity changes and patterns of structural degeneration. Methods. They studied 10 patients at least 6 months after left-hemisphere subcortical strokes and 18 healthy controls. Diffusion-weighted magnetic resonance imaging (MRI) was acquired at baseline, and functional MRI (fMRI) was acquired during performance of a visuomotor tracking task before and after a 15-day period of practice of the same task. Results. Smaller short-term practice effects at baseline correlated with lower fractional anisotropy in the posterior limbs of the internal capsule (PLIC) bilaterally in patients (t > 3; cluster P < .05). After 15 days of motor practice a Group × Time interaction (z > 2.3; cluster P < .05) was found in the basal ganglia, thalamus, inferior frontal gyrus, superior temporal gyrus, and insula. In these regions, healthy controls showed decreases and patients showed increases in activity with practice. Some regions of interest had a loss of white matter connectivity at baseline. Conclusions. Performance gains with motor practice can be associated with increased activity in regions that have been either directly or indirectly impaired by loss of connectivity. These results suggest that neurorehabilitation interventions may be associated with compensatory adaptation of intact brain regions as well as enhanced activity in regions with impaired structural connectivity.
Functional connectomics from resting-state fMRI Smith, Stephen M; Vidaurre, Diego; Beckmann, Christian F ...
Trends in cognitive sciences,
12/2013, Letnik:
17, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Highlights • Spontaneous fluctuations in brain activity reflect functional brain networks. • We review rfMRI for mapping the functional connectome. • We review methods for functional connectomics ...network analysis. • We describe the WU–Minn Human Connectome Project. • We present exciting new analyses using the latest-released HCP data.
This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ...ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.
The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current ...coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First, there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation "coordinate" and "standardized effect-size estimate" data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.
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