Biology subjects

Jaaskelainen, I. P.

Publications and source records attributed to Jaaskelainen, I. P..

5 recordsLinked to original sources

bioRxiv2023

Cerebral Topographies of Perceived and Felt Emotions

Emotions modulate behavioral priorities based on exteroceptive and interoceptive inputs, and the related central and peripheral changes may often be experienced subjectively. Yet, it remains unresolved whether the perceptual and subjectively felt components of the emotion processes rely on shared brain mechanisms. We applied functional magnetic resonance imaging, a rich set of emotional movies, and high-dimensional, continuous ratings of perceived and felt emotions depicted in the same movies to investigate their cerebral organization. Eight main dimensions of emotions evoked during natural movie scene perception were represented in the brain across numerous spatial scales and patterns. Perceived and felt emotions generalized both between individuals and between different samples of stimuli depicting the same emotions. The neural affective space demonstrated an anatomical gradient from responses independent of specific emotions in polysensory areas and default mode regions to more localized and emotion-specific discrete processing in subcortical regions. Differences in neural activations during felt and perceived emotions suggest that temporoparietal areas and precuneus have a key role in computing the affective value of the sensory input. This affective value is then transformed into the subjective emotional experience in the anterior prefrontal cortices, cerebellum, and thalamus. Altogether these data reveal the similarities and differences of domain-general and emotion-specific affect networks in the brain during a wide range of perceived and felt emotions.

neuroscience↗

bioRxiv2020

Distributed source modeling of intracranial stereoelectroencephalographic measurements

Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, sEEG group analyses are complicated because the electrode implantations differ greatly across individuals. Here, using an auditory experiment as the test case, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we also estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. The source models accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope{approx}-3.6). The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized group analyses of both iERP and HBBG.

neuroscience↗

bioRxiv2020

Classification of emotions based on functional connectivity patterns of the human brain

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity signatures differ across emotions. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks, and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We conclude that functional connectivity patterns consistently differ across different emotions particularly within the default mode system.

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↗