scholarly journals Experience, but not age, is associated with volumetric mushroom body expansion in solitary alkali bees

2021 ◽  
pp. jeb.238899
Author(s):  
Mallory A. Hagadorn ◽  
Makenna M. Johnson ◽  
Adam R. Smith ◽  
Marc A. Seid ◽  
Karen M. Kapheim

In social insects, changes in behavior are often accompanied by structural changes in the brain. This neuroplasticity may come with experience (experience-dependent) or age (experience-expectant). Yet, the evolutionary relationship between neuroplasticity and sociality is unclear, because we know little about neuroplasticity in the solitary relatives of social species. We used confocal microscopy to measure brain changes in response to age and experience in a solitary halictid bee (Nomia melanderi). First, we compared the volume of individual brain regions among newly-emerged females, laboratory females deprived of reproductive and foraging experience, and free-flying, nesting females. Experience, but not age, led to significant expansion of the mushroom bodies—higher-order processing centers associated with learning and memory. Next, we investigated how social experience influences neuroplasticity by comparing the brains of females kept in the laboratory either alone or paired with another female. Paired females had significantly larger olfactory regions of the mushroom bodies. Together, these experimental results indicate that experience-dependent neuroplasticity is common to both solitary and social taxa, whereas experience-expectant neuroplasticity may be an adaptation to life in a social colony. Further, neuroplasticity in response to social chemical signals may have facilitated the evolution of sociality.

2015 ◽  
Author(s):  
Stephen H Montgomery ◽  
Richard M Merrill ◽  
Swidbert R Ott

Behavioral and sensory adaptations are often based in the differential expansion of brain components. These volumetric differences represent changes in investment, processing capacity and/or connectivity, and can be used to investigate functional and evolutionary relationships between different brain regions, and between brain composition and behavioral ecology. Here, we describe the brain composition of two species of Heliconius butterflies, a long-standing study system for investigating ecological adaptation and speciation. We confirm a previous report of striking mushroom body expansion, and explore patterns of post-eclosion growth and experience-dependent plasticity in neural development. This analysis uncovers age- and experience-dependent post-emergence mushroom body growth comparable to that in foraging hymenoptera, but also identifies plasticity in several other neuropil. An interspecific analysis indicates that Heliconius display remarkable levels of investment in mushroom bodies for a lepidopteran, and indeed rank highly compared to other insects. Our analyses lay the foundation for future comparative and experimental analyses that will establish Heliconius as a useful case study in evolutionary neurobiology.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.


2020 ◽  
Author(s):  
Tuomas Puoliväli ◽  
Tuomo Sipola ◽  
Anja Thiede ◽  
Marina Kliuchko ◽  
Brigitte Bogert ◽  
...  

AbstractLearning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions encompassing the whole cortex. Using a supervised machine learning technique called support vector machines, we achieved significant (κ = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding an acquired skill from magnetic resonance images of brain structure is feasible to some extent. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely overlapped with earlier findings, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude.


2021 ◽  
Vol 15 ◽  
Author(s):  
Chenggang Song ◽  
Weidong Zhao ◽  
Hong Jiang ◽  
Xiaoju Liu ◽  
Yumei Duan ◽  
...  

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.


2015 ◽  
Vol 39 (4) ◽  
pp. 293-303 ◽  
Author(s):  
Adeline Jabès ◽  
Charles A Nelson

In 1995, Nelson published a paper describing a model of memory development during the first years of life. The current article seeks to provide an update on the original work published 20 years ago. Specifically, we review our current knowledge on the relation between the emergence of explicit memory functions throughout development and the maturation of associated brain regions. It is now well established that the brain regions subserving explicit memory functions (i.e. the hippocampal formation) are far from mature at birth, and exhibit important and gradual structural changes during childhood and beyond. Accordingly, explicit memory functions develop progressively. While some functions are present shortly after birth (formerly proposed as pre-explicit memory), others exhibit protracted developmental profiles during the first years of life. We examine the link between the emergence of different memory functions and the maturation of specific hippocampal circuits.


2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Traute Demirakca ◽  
Vita Cardinale ◽  
Sven Dehn ◽  
Matthias Ruf ◽  
Gabriele Ende

This study investigated the impact of “life kinetik” training on brain plasticity in terms of an increased functional connectivity during resting-state functional magnetic resonance imaging (rs-fMRI). The training is an integrated multimodal training that combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twenty-one subjects completed at least 11 one-hour-per-week “life kinetik” training sessions in 13 weeks as well as before and after rs-fMRI scans. Additionally, 11 control subjects with 2 rs-fMRI scans were included. The CONN toolbox was used to conduct several seed-to-voxel analyses. We searched for functional connectivity increases between brain regions expected to be involved in the exercises. Connections to brain regions representing parts of the default mode network, such as medial frontal cortex and posterior cingulate cortex, did not change. Significant connectivity alterations occurred between the visual cortex and parts of the superior parietal area (BA7). Premotor area and cingulate gyrus were also affected. We can conclude that the constant challenge of unfamiliar combinations of coordination tasks, combined with visual perception and working memory demands, seems to induce brain plasticity expressed in enhanced connectivity strength of brain regions due to coactivation.


2021 ◽  
Vol 10 (7) ◽  
pp. 1454
Author(s):  
Benjamin Clemens ◽  
Mikhail Votinov ◽  
Andrei Alexandru Puiu ◽  
Andre Schüppen ◽  
Philippa Hüpen ◽  
...  

The brain structural changes related to gender incongruence (GI) are still poorly understood. Previous studies comparing gray matter volumes (GMV) between cisgender and transgender individuals with GI revealed conflicting results. Leveraging a comprehensive sample of transmen (n = 33), transwomen (n = 33), cismen (n = 24), and ciswomen (n = 25), we employ a region-of-interest (ROI) approach to examine the most frequently reported brain regions showing GMV differences between trans- and cisgender individuals. The primary aim is to replicate previous findings and identify anatomical regions which differ between transgender individuals with GI and cisgender individuals. On the basis of a comprehensive literature search, we selected a set of ROIs (thalamus, putamen, cerebellum, angular gyrus, precentral gyrus) for which differences between cis- and transgender groups have been previously observed. The putamen was the only region showing significant GMV differences between cis- and transgender, across previous studies and the present study. We observed increased GMV in the putamen for transwomen compared to both transmen and ciswomen and for all transgender participants compared to all cisgender participants. Such a pattern of neuroanatomical differences corroborates the large majority of previous studies. This potential replication of previous findings and the known involvement of the putamen in cognitive processes related to body representations and the creation of the own body image indicate the relevance of this region for GI and its potential as a structural biomarker for GI.


2018 ◽  

The brain undergoes structural changes as it develops over childhood, but whether abnormal structural changes are associated with emerging depressive symptoms in adolescence is unknown. Now, a longitudinal study that enrolled 205 participants aged 8-25 years without signs of depression has used magnetic resonance imaging (MRI) to monitor these brain changes over adolescence.


2021 ◽  
Vol 15 ◽  
Author(s):  
Hao Li ◽  
Jingyi Yue ◽  
Yufeng Wang ◽  
Feng Zou ◽  
Meng Zhang ◽  
...  

The prevalence of mobile phone addiction (MPA) has increased rapidly in recent years, and it has had a certain negative impact on emotions (e.g., anxiety and depression) and cognitive capacities (e.g., executive control and working memory). At the level of neural circuits, the continued increase in activity in the brain regions associated with addiction leads to neural adaptations and structural changes. At present, the spontaneous brain microstates that could be negatively influenced by MPA are unclear. In this study, the temporal characteristics of four resting-state electroencephalogram (RS-EEG) microstates (MS1, MS2, MS3, and MS4) related to mobile phone addiction tendency (MPAT) were investigated using the Mobile Phone Addiction Tendency Scale (MPATS). We attempted to analyze the correlation between MPAT and corresponding microstates and provide evidence to explain the brain and behavioral changes caused by MPA. The results showed that the total score of the MPATS was positively correlated with the duration of MS1, related to phonological processing and negatively correlated with the duration of MS2, related to visual or imagery processing, and MS4, related to the attentional network; the score of the withdrawal symptoms subscale was additionally associated with duration of MS3, related to the cingulo-opercular emotional network. Based on these results, we believe that MPAT may have some negative effects on attentional networks and sensory brain networks; moreover, withdrawal symptoms may induce some negative emotions.


2020 ◽  
Vol 9 (1) ◽  
pp. 64-77
Author(s):  
K.M. Shipkova

The paper considers the directions of cognitive neurorehabilitation based on new data from neuroscience on the "musical brain", the influence of a music enriched environment on structural changes in a healthy brain and its pathology. A modern understanding of the brain foundations of musical perception is given. The role of music in the formation of brain shown by the example of structural and morphological differences between the brains of musicians and non-musicians. The article shows the influence of musical executive activity on the rate of brain ontogenesis, the formation of pathways, and an increase in the volume of white and gray matter in the brain regions associated with musical perception. The specificity of the hemispheric geography of perceptual musical brain maps described. The review of modern research directions on the role of the use of music-enriched environment in the rehabilitation of cognitive disorders is given. Various types of music technologies used in rehabilitation practice specified: neurological music therapy (NMT), musical intonation therapy (MIT) and music supported therapy (MST). Special attention is paid to the description of types of music therapy in working process with aphasia and dementia. It shows the common psychological structure of musical and speech perception, the friendliness of structural brain rearrangements and regression of aphasic disorders during MIT We consider data from studies using neuroimaging methods that prove the effectiveness of MIT in aphasia. For dementia, the productivity of using a music enriched environment in the form of MST is demonstrated. Data on the multiplicity and duration of MST courses to achieve a positive rehabilitation effect are provided. The importance of using a music enriched environment in the rehabilitation of cognitive disorders of organic genesis in the field of neuropsychological practice is discussed.


Sign in / Sign up

Export Citation Format

Share Document