Biology subjects

Nummenmaa, L.

Publications and source records attributed to Nummenmaa, L..

57 records · Page 4Linked to original sources

bioRxiv2019

Magia: Robust automated modeling and image processing toolbox for PET neuroinformatics

IntroductionModelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, thus prohibiting large-scale standardized analysis of brain PET data. To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia in the automatic brain-PET data processing with four tracers binding to different binding sites: [11C]raclopride, [11C]carfentanil, [11C]MADAM, and [11C]PiB.\n\nMaterials and methodsFor each tracer, we processed 30 historical control subjects data with manual and automated methods. Five persons manually delineated the reference regions (cerebellar or occipital cortex depending on tracer) for each subject according to written and visual instructions. The automatic reference-region extraction was based on FreeSurfer parcellations. We first assessed inter-operator variance resulting from manual delineation of reference regions. Then we compared the differences between the manually and automatically produced reference regions and the subsequently obtained metrics.\n\nResultsThe manually delineated reference regions were remarkably different from each other. The differences translated into differences in outcome measures (binding potential or SUV-ratio), and the intra-class correlation coefficients were between 47 % and 96 % for the tracers. While the Magia-derived reference regions were topographically very different from the manually defined reference regions, Magia produced outcome measures highly consistent with average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3-5 % higher binding potentials as a result of slightly lower time-integrals of reference region time-activity curves.\n\nConclusionEven if Magia produces reference regions that are anatomically different from manually drawn reference regions, the resulting outcome measures are highly similar. Based on these results and considering the high inter-operator variance of the manual method, the high level of standardization and strong scalability of Magia, we conclude that Magia can be reliably used to process brain PET data.

neuroscience↗

bioRxiv2017

Reproducibility of importance extraction methods in neural network based fMRI classification

Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues using an fMRI classification scheme based on neural networks and compared a set of methods for extraction of category-related voxel importances in three simulated and two empirical datasets. The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification. Application of the proposed classification scheme to two previously published empirical fMRI datasets revealed robust importance maps that extensively overlap with univariate maps but also provide complementary information. We conclude that importance maps are superior to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.

neuroscience↗

bioRxiv2017

Distributed Affective Space Represents Multiple Emotion Categories Across The Brain

The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here we used pattern classification and hierarchical clustering to reveal and characterize the organization of discrete emotion categories in the human brain. We induced 14 emotions (6 \"basic\", such as fear and anger; and 8 \"non-basic\", such as shame and gratitude) and a neutral state in participants using guided mental imagery while their brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the fMRI signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that the emotions included in the study have discrete neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits, and highlight both overlaps and differences in the locations of these for each emotion. Locally differentiated engagement of these globally shared circuits defines the unique neural fingerprint activity pattern and the corresponding subjective feeling associated with each emotion.

neuroscience↗