scholarly journals MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongsheng Xiao ◽  
Brandon J. Forys ◽  
Matthieu P. Vanni ◽  
Timothy H. Murphy
Keyword(s):  
Machine Learning ◽  
Quantitative Analysis ◽  
Brain Activity ◽  
Spatial Scales ◽  
Brain Regions ◽  
Brain Atlas ◽  
Convolutional Network ◽  
Segmentation Approach ◽  
Automated Machine Learning ◽  
User Friendly

AbstractUnderstanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

scholarly journals Functional MRI of large scale activity in behaving mice

2020 ◽  
Author(s):  
Madalena S. Fonseca ◽  
Mattia G. Bergomi ◽  
Zachary F. Mainen ◽  
Noam Shemesh
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Spatial Scales ◽  
Distributed Processing ◽  
Brain Regions ◽  
Visually Guided ◽  
Dynamic Interactions ◽  
Complex Behaviour ◽  
Wide Range ◽  
Circuit Techniques

ABSTRACTBehaviour involves complex dynamic interactions across many brain regions. Detecting whole-brain activity in mice performing sophisticated behavioural tasks could facilitate insights into distributed processing underlying behaviour, guide local targeting, and help bridge the disparate spatial scales between rodent and human studies. Here, we present a comprehensive approach for recording brain-wide activity with functional magnetic resonance imaging (fMRI) compatible with a wide range of behavioural paradigms and neuroscience questions. We introduce hardware and procedural advances to allow multi-sensory, multi-action behavioural paradigms in the scanner. We identify signal artifacts arising from task-related body movements and propose novel strategies to suppress them. We validate and explore our approach in a 4-odour classical conditioning and a visually-guided operant task, illustrating how it can be used to extract information insofar intangible to rodent behaviour studies. Our work paves the way for future studies combining fMRI and local circuit techniques during complex behaviour to tackle multi-scale behavioural neuroscience questions.

scholarly journals Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret

2018 ◽  
Author(s):  
Célian Bimbard ◽  
Charlie Demené ◽  
Constantin Girard ◽  
Susanne Radtke-Schuller ◽  
Shihab Shamma ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Multiple Scales ◽  
Functional Organization ◽  
Brain Activity ◽  
Spatial Scales ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Sensory Response ◽  
Response Modulation ◽  
Sensory Responses

A major challenge in neuroscience is to longitudinally monitor whole brain activity across multiple spatial scales in the same animal. Functional UltraSound (fUS) is an emerging technology that offers images of cerebral blood volume over large brain portions. Here we show for the first time its capability to resolve the functional organization of sensory systems at multiple scales in awake animals, both within structures by precisely mapping sensory responses, and between structures by elucidating the connectivity scheme of top-down projections. We demonstrate that fUS provides stable (over days), yet rapid, highly-resolved 3D tonotopic maps in the auditory pathway of awake ferrets, with unprecedented sharp functional resolution (100μm). This was performed in four different brain regions, including small (1-2mm3 size), subcortical (8mm deep) and previously undescribed structures in the ferret. Furthermore, we used fUS to map longdistance projections from frontal cortex, a key source of sensory response modulation, to auditory cortex.

scholarly journals Multi-dimensional predictions of psychotic symptoms via machine learning

2020 ◽  
Author(s):  
Jeremy A Taylor ◽  
Kit Melissa Larsen ◽  
Marta I Garrido
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Brain Regions ◽  
Psychotic Symptoms ◽  
Diverse Range ◽  
Auditory Oddball ◽  
Applied Machine Learning ◽  
Dimensional Diagnosis ◽  
Auditory Oddball Task ◽  
Fusion Framework

AbstractThe diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.

scholarly journals Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

2021 ◽  
Author(s):  
Daniela Calvetti ◽  
Brian Johnson ◽  
Annalisa Pascarella ◽  
Francesca Pitolli ◽  
Erkki Somersalo ◽  
...  
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Longitudinal Studies ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Atlas ◽  
Meditation Practice ◽  
Linear Discriminant ◽  
The Brain

AbstractMeditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in $$\alpha$$ α , $$\beta$$ β , $$\gamma$$ γ , and $$\theta$$ θ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

scholarly journals Neuronal Sequence Models for Bayesian Online Inference

2021 ◽  
Vol 4 ◽  
Author(s):  
Sascha Frölich ◽  
Dimitrije Marković ◽  
Stefan J. Kiebel
Keyword(s):  
Machine Learning ◽  
Sensory Input ◽  
Brain Activity ◽  
Brain Regions ◽  
Hierarchical Networks ◽  
Bayesian Brain ◽  
Fast Recognition ◽  
Wide Range ◽  
Human Ethology ◽  
The Brain

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

scholarly journals Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Célian Bimbard ◽  
Charlie Demene ◽  
Constantin Girard ◽  
Susanne Radtke-Schuller ◽  
Shihab Shamma ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Multiple Scales ◽  
Functional Organization ◽  
Brain Activity ◽  
Spatial Scales ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Sensory Response ◽  
Response Modulation ◽  
Long Distance

A major challenge in neuroscience is to longitudinally monitor whole brain activity across multiple spatial scales in the same animal. Functional UltraSound (fUS) is an emerging technology that offers images of cerebral blood volume over large brain portions. Here we show for the first time its capability to resolve the functional organization of sensory systems at multiple scales in awake animals, both within small structures by precisely mapping and differentiating sensory responses, and between structures by elucidating the connectivity scheme of top-down projections. We demonstrate that fUS provides stable (over days), yet rapid, highly-resolved 3D tonotopic maps in the auditory pathway of awake ferrets, thus revealing its unprecedented functional resolution (100/300µm). This was performed in four different brain regions, including very small (1–2 mm3 size), deeply situated subcortical (8 mm deep) and previously undescribed structures in the ferret. Furthermore, we used fUS to map long-distance projections from frontal cortex, a key source of sensory response modulation, to auditory cortex.

Inefficient Encoding as an Explanation for Age-Related Deficits in Recollection-Based Processing

2014 ◽  
Vol 28 (3) ◽  
pp. 148-161 ◽  
Author(s):  
David Friedman ◽  
Ray Johnson
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Semantic Retrieval ◽  
Age Related ◽  
The Face ◽  
Episodic Memories

A cardinal feature of aging is a decline in episodic memory (EM). Nevertheless, there is evidence that some older adults may be able to “compensate” for failures in recollection-based processing by recruiting brain regions and cognitive processes not normally recruited by the young. We review the evidence suggesting that age-related declines in EM performance and recollection-related brain activity (left-parietal EM effect; LPEM) are due to altered processing at encoding. We describe results from our laboratory on differences in encoding- and retrieval-related activity between young and older adults. We then show that, relative to the young, in older adults brain activity at encoding is reduced over a brain region believed to be crucial for successful semantic elaboration in a 400–1,400-ms interval (left inferior prefrontal cortex, LIPFC; Johnson, Nessler, & Friedman, 2013 ; Nessler, Friedman, Johnson, & Bersick, 2007 ; Nessler, Johnson, Bersick, & Friedman, 2006 ). This reduced brain activity is associated with diminished subsequent recognition-memory performance and the LPEM at retrieval. We provide evidence for this premise by demonstrating that disrupting encoding-related processes during this 400–1,400-ms interval in young adults affords causal support for the hypothesis that the reduction over LIPFC during encoding produces the hallmarks of an age-related EM deficit: normal semantic retrieval at encoding, reduced subsequent episodic recognition accuracy, free recall, and the LPEM. Finally, we show that the reduced LPEM in young adults is associated with “additional” brain activity over similar brain areas as those activated when older adults show deficient retrieval. Hence, rather than supporting the compensation hypothesis, these data are more consistent with the scaffolding hypothesis, in which the recruitment of additional cognitive processes is an adaptive response across the life span in the face of momentary increases in task demand due to poorly-encoded episodic memories.

Comparative analysis of changes of bioelectrical activity of the brain during the neutral touch and during the performance of the myofascial release technique

2018 ◽  
pp. 46-55
Author(s):  
A. F. Belyaev ◽  
G. E. Piskunova
Keyword(s):  
Soft Tissue ◽  
Brain Activity ◽  
Direct Action ◽  
Brain Regions ◽  
Therapeutic Touch ◽  
Bioelectrical Activity ◽  
Therapeutic Action ◽  
Osteopathic Treatment ◽  
The Difference ◽  
Multiparameter Analysis

Introduction. One of the main tools of an osteopath are soft tissue techniques, which have a number of particular qualities such as minimization of force and duration of indirect techniques with an emphasis on muscle and ligamentous structures; combination of gestures, tendency to maximal relaxation and exclusion of direct action on pathological symptoms such as tension, overtone and pain. Minimization of the force applied during the performance of soft tissue techniques often invites a question whether there are differences between the usual touch and the therapeutic touch of an osteopath.Goal of research - to reveal the changes in the bioelectrical activity of the cerebral cortex arising in the process of osteopathic treatment in order to prove its specifi city in comparison with nonspecifi c tactile stimulation (neutral touch).Materials and methods. 75 people were examined with the use of multiparameter analysis of multichannel EEG in different times. 25 patients were clinically healthy adults, whereas 50 patients had signs of somatic dysfunctions.Results. Computer encephalography permits to perceive the difference between the neutral touch and the therapeutic action. An identifi cation reaction is a response to the neutral touch (changes in brain bioelectrical activity with an increase in statistically signifi cant connections in the temporal lobes), whereas the therapeutic action provokes the state of purposeful brain activity during still point (intensifi cation of frontooccipital interactions).Conclusions. Osteopathic action causes additional tension in the processing of incoming information, which requires participation of different brain regions, including interhemispheric mechanisms associated with analysis, maintenance of attention and regulation of targeted activities.

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