Validating the independent components of neuroimaging time series via clustering and visualization

Himberg, Johan; Hyvärinen, Aapo; Esposito, Fabrizio

doi:10.1016/j.neuroimage.2004.03.027

Cited by 1,109 publications

(722 citation statements)

References 26 publications

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“…2004). The IC validation, in terms of task relatedness, is determined by (1) the spectral power of each IC time course at the task frequency and (2) the phase of each IC time course relative to the task reference function (Schmithorst et al.…”

Section: Methodsmentioning

confidence: 99%

Rapidly acquired multisensory association in the olfactory cortex

et al. 2015

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BackgroundThe formation of an odor percept in humans is strongly associated with visual information. However, much less is known about the roles of learning and memory in shaping the multisensory nature of odor representations in the brain.MethodThe dynamics of odor and visual association in olfaction was investigated using three functional magnetic resonance imaging (fMRI) paradigms. In two paradigms, a visual cue was paired with an odor. In the third, the same visual cue was never paired with an odor. In this experimental design, if the visual cue was not influenced by odor–visual pairing, then the blood‐oxygen‐level‐dependent (BOLD) signal elicited by subsequent visual cues should be similar across all three paradigms. Additionally, intensity, a major dimension of odor perception, was used as a modulator of associative learning which was characterized in terms of the spatiotemporal behavior of the BOLD signal in olfactory structures.ResultsA single odor–visual pairing cue could subsequently induce primary olfactory cortex activity when only the visual cue was presented. This activity was intensity dependent and was also detected in secondary olfactory structures and hippocampus.ConclusionThis study provides evidence for a rapid learning response in the olfactory system by a visual cue following odor and visual cue pairing. The novel data and paradigms suggest new avenues to explore the dynamics of odor learning and multisensory representations that contribute to the construction of a unified odor percept in the human brain.

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Section: Methodsmentioning

confidence: 99%

Rapidly acquired multisensory association in the olfactory cortex

et al. 2015

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“…We selected 60 components with the largest eigenvalues. Then, FastICA was run 30 times with different initial conditions and the data were bootstrapped (Himberg et al, 2004). The contrast function was based on skewness.…”

Section: Clustering Of Human Neoplasms Based On Amplification Profilesmentioning

confidence: 99%

DNA copy number amplification profiling of human neoplasms

et al. 2006

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DNA copy number amplifications activate oncogenes and are hallmarks of nearly all advanced tumors. Amplified genes represent attractive targets for therapy, diagnostics and prognostics. To investigate DNA amplifications in different neoplasms, we performed a bibliomics survey using 838 published chromosomal comparative genomic hybridization studies and collected amplification data at chromosome band resolution from more than 4500 cases. Amplification profiles were determined for 73 distinct neoplasms. Neoplasms were clustered according to the amplification profiles, and frequently amplified chromosomal loci (amplification hot spots) were identified using computational modeling. To investigate the site specificity and mechanisms of gene amplifications, colocalization of amplification hot spots, cancer genes, fragile sites, virus integration sites and gene size cohorts were tested in a statistical framework. Amplification-based clustering demonstrated that cancers with similar etiology, cell-oforigin or topographical location have a tendency to obtain convergent amplification profiles. The identified amplification hot spots were colocalized with the known fragile sites, cancer genes and virus integration sites, but global statistical significance could not be ascertained. Large genes were significantly overrepresented on the fragile sites and the reported amplification hot spots. These findings indicate that amplifications are selected in the cancer tissue environment according to the qualitative traits and localization of cancer genes.

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“…The optimal number of gICA components was determined to be 70 using a modified version of ICASSO (RRID:SCR_014981; ICASSO was included in the GIFT v4.0a package; http://mialab.mrn.org/software/gift/) (Himberg, Hyvärinen, & Esposito, 2004; Ma et al., 2011) such that the overall R‐index is close to the minimum and the index quality of at most two components falls below 0.7. The R‐index as defined in Himberg et al., (2004) is a cluster validity index (Levine & Domany, 2001) that constitutes a measure of compactness and separation of independent components.…”

Section: Methodsmentioning

confidence: 99%

“…The R‐index as defined in Himberg et al., (2004) is a cluster validity index (Levine & Domany, 2001) that constitutes a measure of compactness and separation of independent components. This setup was considered a good consistency trade‐off between RSN quality and clustering validity (R‐index) considering the differences among all four pipelines.…”

Section: Methodsmentioning

confidence: 99%

The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury

et al. 2017

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IntroductionDynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data.MethodsThe sample cohort consists of 50 mTBI patients and matched healthy controls. A 5‐min resting‐state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion‐related variance was removed. In all pipelines, group‐independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding‐window size. A linear support vector machine was used to test how each pipeline affects classification accuracy.ResultsResults suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding‐window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters.ConclusionThe sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.

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Validating the independent components of neuroimaging time series via clustering and visualization

Cited by 1,109 publications

References 26 publications

Rapidly acquired multisensory association in the olfactory cortex

Rapidly acquired multisensory association in the olfactory cortex

DNA copy number amplification profiling of human neoplasms

The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury

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