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 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 Discrimination of Schizophrenia Auditory Hallucinators by Machine Learning of Resting-State Functional MRI

2015 ◽  
Vol 25 (03) ◽  
pp. 1550007 ◽  
Author(s):  
Darya Chyzhyk ◽  
Manuel Graña ◽  
Döst Öngür ◽  
Ann K. Shinn
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Right Hemisphere ◽  
Inferior Frontal Gyrus ◽  
Low Frequency ◽  
Brain Regions ◽  
Classification Performance ◽  
Applied Machine Learning ◽  
Heschl’S Gyrus ◽  
Heschl's Gyrus

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.

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 The ecological cocktail party: Measuring brain activity during an auditory oddball task with background noise

2018 ◽  
Author(s):  
Joanna E. M. Scanlon ◽  
Danielle L. Cormier ◽  
Kimberley A. Townsend ◽  
Jonathan W.P. Kuziek ◽  
Kyle E. Mathewson
Keyword(s):  
White Noise ◽  
New Technologies ◽  
Brain Activity ◽  
Real Life ◽  
Auditory Oddball ◽  
Oddball Task ◽  
Eeg Recordings ◽  
Auditory Oddball Task ◽  
The Brain ◽  
Sound Condition

AbstractMost experiments using EEG recordings take place in highly isolated and restricted environments, limiting their applicability to real-life scenarios. New technologies for mobile EEG are changing this by allowing EEG recording to take place outside of the laboratory. However, before results from experiments performed outside the laboratory can be fully understood, the effects of ecological stimuli on brain activity during cognitive tasks must be examined. In this experiment, participants performed an auditory oddball task while also listening to concurrent background noises of silence, white noise and outdoor ecological sounds, as well as a condition in which the tones themselves were at a low volume. We found a significantly increased N1 and decreased P2 when participants performed the task with outdoor sounds and white noise in the background, with the largest differences in the outdoor sound condition. This modulation in the N1 and P2 replicates what we have previously found outside while people ride bicycles (Scanlon et al., 2017b). No behavioural differences were found in response to the target tones. We interpret these modulations in early ERPs as indicative of sensory filtering of background sounds, and that ecologically valid sounds require more filtering than synthetic sounds. Our results reveal that much of what we understand about the brain will need to be updated as we step outside the lab.

Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland

2016 ◽  
Vol 47 (3) ◽  
pp. 495-506 ◽  
Author(s):  
E. Rikandi ◽  
S. Pamilo ◽  
T. Mäntylä ◽  
J. Suvisaari ◽  
T. Kieseppä ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Regions ◽  
First Episode Psychosis ◽  
Positive Symptom ◽  
Psychotic Symptoms ◽  
First Episode ◽  
Chronic Patients ◽  
Control Subjects ◽  
Alice In Wonderland ◽  
Episode Psychosis

BackgroundWhile group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an individual patient. Earlier machine-learning studies have focused on data collected from chronic patients during rest or simple tasks. We set out to unravel brain activation patterns during naturalistic stimulation in first-episode psychosis (FEP).MethodWe recorded brain activity from 46 FEP patients and 32 control subjects viewing scenes from the fantasy film Alice in Wonderland. Scenes with varying degrees of fantasy were selected based on the distortion of the ‘sense of reality’ in psychosis. After cleaning the data with a novel maxCorr method, we used machine learning to classify patients and healthy control subjects on the basis of voxel- and time-point patterns.ResultsMost (136/194) of the voxels that best classified the groups were clustered in a bilateral region of the precuneus. Classification accuracies were up to 79.5% (p = 5.69 × 10−8), and correct classification was more likely the higher the patient's positive-symptom score. Precuneus functioning was related to the fantasy content of the movie, and the relationship was stronger in control subjects than patients.ConclusionsThese findings are the first to show abnormalities in precuneus functioning during naturalistic information processing in FEP patients. Correlational findings suggest that these alterations are associated with positive psychotic symptoms and processing of fantasy. The results may provide new insights into the neuronal basis of reality distortion in psychosis.

scholarly journals The ecological cocktail party: Measuring brain activity during an auditory oddball task with background noise

2019 ◽  
Vol 56 (11) ◽  
Author(s):  
Joanna E. M. Scanlon ◽  
Danielle L. Cormier ◽  
Kimberley A. Townsend ◽  
Jonathan W. P. Kuziek ◽  
Kyle E. Mathewson
Keyword(s):  
Background Noise ◽  
Brain Activity ◽  
Cocktail Party ◽  
Auditory Oddball ◽  
Oddball Task ◽  
Auditory Oddball Task

Time-Frequency Bands of Electromagnetic Wave from ERPCOH on Developmental Changes

2013 ◽  
Vol 479-480 ◽  
pp. 517-523
Author(s):  
Ming Chung Ho ◽  
Chin Fei Huang ◽  
Chia Yi Chou ◽  
Ming Chi Lu ◽  
Yung Yi Chang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Brain Activity ◽  
Frequency Bands ◽  
Time Frequency ◽  
Auditory Oddball ◽  
Age Related ◽  
Adult Participants ◽  
Beta 2 ◽  
Auditory Oddball Task ◽  
The Brain

Recent changes in ongoing background activity are one of the most popular approaches to investigate brain activity for understanding child development. However, research using event-related responses of cortico-cortical connections to explore changes during childhood is rare. This study investigates mature changes in brain connectivity in associative reorganization patterns and hypothesizes that age-related changes affect oscillatory connections. The sample included children aged 7 years, 11 years, and adults. The 3 groups were studied in the time-frequency domain to analyze event-related cross phase coherence (ERPCOH) between different parts of the brain as they performed an auditory oddball task. Compared to the adult participants, the 11-year-old participants were found to have increased connectivity in theta (4-7 Hz), beta-2 (20-30 Hz), and gamma bands (30-50 Hz) in the early component (N1, 80-140 ms), although ERPCOH value decreased in the alpha-1 (7-10 Hz) and alpha-2 bands (10-13 Hz). Compared to the 11-year-old participants, 7-year-old participants had greater connectivity decreases in all frequency bands, most significantly in theta, beta-2, and gamma bands.

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 Taking off the training wheels: Measuring auditory P3 during outdoor cycling using an active wet EEG system

2017 ◽  
Author(s):  
Joanna E. M. Scanlon ◽  
Kimberley A. Townsend ◽  
Danielle L. Cormier ◽  
Jonathan W. P. Kuziek ◽  
Kyle E. Mathewson
Keyword(s):  
Statistical Power ◽  
Brain Activity ◽  
Event Related Potentials ◽  
List Type ◽  
Auditory Oddball ◽  
Oddball Task ◽  
Lower Amplitude ◽  
Related Potentials ◽  
A Minor ◽  
Auditory Oddball Task

AbstractMobile EEG allows the investigation of brain activity in increasingly complex environments. In this study, EEG equipment was adapted for use and transportation in a backpack while cycling. Participants performed an auditory oddball task while cycling outside and sitting in an isolated chamber inside the lab. Cycling increased EEG noise and marginally diminished alpha amplitude. However, this increased noise did not influence the ability to measure reliable event related potentials (ERP). The P3 was similar in topography, and morphology when outside on the bike, with a lower amplitude in the outside cycling condition. There was only a minor decrease in the statistical power to measure reliable ERP effects. Unexpectedly, when biking outside significantly decreased P2 and increased N1 amplitude were observed when evoked by both standards and targets compared with sitting in the lab. This may be due to attentional processes filtering the overlapping sounds between the tones used and similar environmental frequencies. This study established methods for mobile recording of ERP signals. Future directions include investigating auditory P2 filtering inside the laboratory.HighlightsA backpack containing all the equipment necessary to record ERP and EEG was worn by participants as they rode a bicycle outside along a streetEEG and ERP data from an auditory oddball task is compared with data acquired within subject inside the labReliable MMN/N2b and P3 responses were measured during bicycle riding outside equal in magnitude to those obtained inside the labA surprising decrease in the P2 component of the ERP evoked by targets and standards was observed when doing the task outside on a bicycle, which we attribute to increased auditory filtering

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.

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