Project description:The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).

Project description:Contemporary Jews comprise an aggregate of ethno-religious communities whose worldwide members identify with each other through various shared religious, historical, and cultural traditions1,2. Historical evidence suggests common origins in the Middle East, followed by migrations leading to the establishment of communities of Jews in Europe, Africa, and Asia - in what is termed the Jewish Diaspora3-5. This complex demographic history imposes special challenges in attempting to address the genetic structure of the Jewish people6. While many genetic studies have shed light on Jewish diseases and origins, including those focusing on uniparentally- and biparentally-inherited markers7-16, genome-wide patterns of variation across the vast geographic span of Jewish Diaspora communities and their respective neighbors have yet to be addressed. Here we use high-density bead arrays to genotype individuals from 14 Jewish Diaspora communities, and compare these patterns of genome-wide diversity with those from 69 Old World non-Jewish populations, of which 25 have not been previously reported. These samples were carefully chosen to provide comprehensive comparisons between Jewish and non-Jewish populations in the Diaspora, as well as with non-Jewish populations from the Middle East and North Africa. Principal component and structure-like analyses identify previously unrecognized genetic substructure within the Middle East. Most Jewish samples form a remarkably tight sub-cluster that overlies Druze and Cypriot samples, but not samples from other Levantine populations or paired Diaspora host populations. In contrast, Ethiopian Jews (Beta Israel) and Bene Israel Indian Jews cluster with neighbouring autochthonous populations in Ethiopia and western India, respectively; despite a clear paternal link between the Bene Israel and the Levant. These results cast light on the variegated genetic architecture of the Middle East, and trace the origins of most Jewish Diaspora communities to the Levant. 466 samples are analysed on three different Illumina platforms.

Project description:Contemporary Jews comprise an aggregate of ethno-religious communities whose worldwide members identify with each other through various shared religious, historical, and cultural traditions1,2. Historical evidence suggests common origins in the Middle East, followed by migrations leading to the establishment of communities of Jews in Europe, Africa, and Asia - in what is termed the Jewish Diaspora3-5. This complex demographic history imposes special challenges in attempting to address the genetic structure of the Jewish people6. While many genetic studies have shed light on Jewish diseases and origins, including those focusing on uniparentally- and biparentally-inherited markers7-16, genome-wide patterns of variation across the vast geographic span of Jewish Diaspora communities and their respective neighbors have yet to be addressed. Here we use high-density bead arrays to genotype individuals from 14 Jewish Diaspora communities, and compare these patterns of genome-wide diversity with those from 69 Old World non-Jewish populations, of which 25 have not been previously reported. These samples were carefully chosen to provide comprehensive comparisons between Jewish and non-Jewish populations in the Diaspora, as well as with non-Jewish populations from the Middle East and North Africa. Principal component and structure-like analyses identify previously unrecognized genetic substructure within the Middle East. Most Jewish samples form a remarkably tight sub-cluster that overlies Druze and Cypriot samples, but not samples from other Levantine populations or paired Diaspora host populations. In contrast, Ethiopian Jews (Beta Israel) and Bene Israel Indian Jews cluster with neighbouring autochthonous populations in Ethiopia and western India, respectively; despite a clear paternal link between the Bene Israel and the Levant. These results cast light on the variegated genetic architecture of the Middle East, and trace the origins of most Jewish Diaspora communities to the Levant.

Dataset Information

The representational structure of mental states generalizes across target people and stimulus modalities.

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The representational structure of mental states generalizes across target people and stimulus modalities.

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