Dataset Information

Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

ABSTRACT: Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., "the month you were born is an odd number"), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6 to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.

SUBMITTER: Nawa NE

PROVIDER: S-EPMC4688375 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

Nawa Norberto E NE Ando Hiroshi H

Frontiers in human neuroscience 20151223

Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healt ...[more]

PMID: 26778992

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Project description:We have used commercially available oligonucleotide arrays to measure the relative expression levels of >10,000 genes and ESTs. Recent evidence suggested certain advantages of performing such expression experiments on two or more different platforms of oligonucleotide arrays. Here we have used two oligonucleotide platforms, MG U74Av2.0 arrays from Affymetrix and CodeLink UniSet1 from GE Healthcare, to assess whole brain gene expression patterns in naive C57BL/6 and DBA/2J mice. C57BL/6J (n = 5) and DBA/2J (n = 4) male mice, 25-30 g body weight, were obtained from Jackson Laboratories. Animals were group-housed and quickly sacrificed by CO2 exposure according to a protocol approved by the UCHSC IACUC. Brains were rapidly removed and stored at -80oC until use. Whole brain was homogenized in lysis buffer using a Polytron, and total RNA was isolated using the RNeasy Midi Kit (Qiagen) following the protocol supplied by the manufacturer. An additional clean-up of total RNA was carried out using the RNeasy Mini Kit (Qiagen). Using the protocol supplied by the manufacturer, double-stranded cDNA was synthesized from the total RNA (4 ug of total RNA from an individual mouse brain was used for each array) and was used to obtain biotin-labeled cRNA by an in vitro transcription reaction. Biotin-labeled cRNA was recovered using the RNeasy kit. Biotin-labeled cRNA from each mouse was then fragmented and hybridized with two separate (duplicate) CodeLink BioArrays. The average of the gene expression intensity values for each probe set from these technical replicates was used for data analysis. The BioArrays were subsequently stained for 30 min with Cy5-Streptavidin and washed prior to scanning. For MG U74Av2 arrays,double-stranded cDNA was synthesized from 5 ug of total RNA, using the protocol supplied by the manufacturer, and used to obtain biotin-labeled cRNA by an in vitro transcription reaction. Biotin-labeled cRNA was then fragmented and the quality of the fragmented cRNA was assessed using Test2 chips prior to hybridization with the Affymetrix arrays. Hybridization of the cRNA with the Affymetrix arrays was carried out according to the manufacturer's protocol. The arrays were stained with streptavidin-phycoerythrin conjugate and scanned by a GeneArray scanner. Keywords: other

Project description:ObjectiveSex differences in the brain are traditionally treated as binary. We present new evidence that a continuous measure of sex differentiation of the brain can explain sex differences in psychopathology. The degree of sex-differentiated brain features (ie, features that are more common in one sex) may predispose individuals toward sex-biased psychopathology and may also be influenced by the genome. We hypothesized that individuals with a female-biased differentiation score would have greater female-biased psychopathology (internalizing symptoms, such as anxiety and depression), whereas individuals with a male-biased differentiation score would have greater male-biased psychopathology (externalizing symptoms, such as disruptive behaviors).MethodUsing the Philadelphia Neurodevelopmental Cohort database acquired from database of Genotypes and Phenotypes, we calculated the sex differentiation measure, a continuous data-driven calculation of each individual's degree of sex-differentiating features extracted from multimodal brain imaging data (magnetic resonance imaging [MRI] /diffusion MRI) from the imaged participants (n = 866, 407 female and 459 male).ResultsIn male individuals, higher differentiation scores were correlated with higher levels of externalizing symptoms (r = 0.119, p = .016). The differentiation measure reached genome-wide association study significance (p < 5∗10-8) in male individuals with single nucleotide polymorphisms Chromsome5:rs111161632:RASGEF1C and Chromosome19:rs75918199:GEMIN7, and in female individuals with Chromosome2:rs78372132:PARD3B and Chromosome15:rs73442006:HCN4.ConclusionThe sex differentiation measure provides an initial topography of quantifying male and female brain features. This demonstration that the sex of the human brain can be conceptualized on a continuum has implications for both the presentation of psychopathology and the relation of the brain with genetic variants that may be associated with brain differentiation.

Dataset Information

Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

Publications

Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

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