The Oscillatory ReConstruction Algorithm adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings

Neural oscillations show substantial variability within and across individuals and brain regions, yet most existing studies analyze oscillations using canonical, fixed-frequency bands. Thus, there is an ongoing need for tools that capture oscillatory variability in neural signals. Toward this end, Oscillatory ReConstruction Algorithm is a novel and adaptive analytic tool that allows researchers to measure neural oscillations with more precision and less researcher bias.

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The Oscillatory ReConstruction Algorithm (ORCA) adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings

10.1101/855288 ◽

2019 ◽

Cited By ~ 1

Author(s):

Andrew J Watrous ◽

Robert Buchanan

Keyword(s):

Spectral Decomposition ◽

Spectral Characteristics ◽

Reconstruction Algorithm ◽

Brain Regions ◽

Neural Oscillations ◽

Neural Signals ◽

Frequency Bands ◽

Eyes Closed ◽

Spectral Estimates ◽

Band Limited

AbstractNeural oscillations are routinely analyzed using methods that measure activity in canonical frequency bands (e.g. alpha, 8-12 Hz), though the frequency of neural signals is not fixed and varies within and across individuals based on numerous factors including neuroanatomy, behavioral demands, and species. Further, band-limited activity is an often assumed, typically unmeasured model of neural activity and band definitions vary considerably across studies. These factors together mask individual differences and can lead to noisy spectral estimates and interpretational problems when linking electrophysiology to behavior. We developed the Oscillatory ReConstruction Algorithm (“ORCA”), an unsupervised method to measure the spectral characteristics of neural signals in adaptively identified bands which incorporates two new methods for frequency band identification. ORCA uses the instantaneous power, phase, and frequency of activity in each band to reconstruct the signal and directly quantify spectral decomposition performance using each of four different models. To reduce researcher bias, ORCA provides spectral estimates derived from the best model and requires minimal hyperparameterization. Analyzing human scalp EEG data during eyes open and eyes-closed “resting” conditions, we first identify variability in the frequency content of neural signals across subjects and electrodes. We demonstrate that ORCA significantly improves spectral decomposition compared to conventional methods and captures the well-known increase in low-frequency activity during eyes closure in electrode- and subject-specific frequency bands. We further illustrate the utility of our method in rodent CA1 recordings. ORCA is a novel analytic tool that will allow researchers to investigate how non-stationary neural oscillations vary across behaviors, brain regions, individuals, and species.

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The predictive roles of neural oscillations in speech motor adaptability

Journal of Neurophysiology ◽

10.1152/jn.00043.2016 ◽

2016 ◽

Vol 115 (5) ◽

pp. 2519-2528 ◽

Cited By ~ 4

Author(s):

Ranit Sengupta ◽

Sazzad M. Nasir

Keyword(s):

Brain Regions ◽

Brain Network ◽

Neural Oscillations ◽

Motor Speech ◽

Frequency Bands ◽

Time Frequency ◽

Continuous Eeg ◽

Outcome Differences ◽

Eeg Recordings ◽

Speech Motor

The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations.

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Identifying robust and sensitive frequency bands for interrogating neural oscillations

NeuroImage ◽

10.1016/j.neuroimage.2010.03.037 ◽

2010 ◽

Vol 51 (4) ◽

pp. 1319-1333 ◽

Cited By ~ 50

Author(s):

Alexander J. Shackman ◽

Brenton W. McMenamin ◽

Jeffrey S. Maxwell ◽

Lawrence L. Greischar ◽

Richard J. Davidson

Keyword(s):

Neural Oscillations ◽

Frequency Bands

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T-SNE visualization of large-scale neural recordings

10.1101/087395 ◽

2016 ◽

Cited By ~ 5

Author(s):

George Dimitriadis ◽

Joana Neto ◽

Adam R. Kampff

Keyword(s):

Large Scale ◽

New Technologies ◽

Dimensional Space ◽

Clustering Algorithms ◽

Brain Regions ◽

Data Sets ◽

Neural Recordings ◽

Sorting Problem ◽

Feature Spaces ◽

High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

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Dissociable oscillatory networks support gain and loss processing in human orbitofrontal cortex

10.1101/2021.02.25.432874 ◽

2021 ◽

Author(s):

Ignacio Saez ◽

Jack Lin ◽

Edward Chang ◽

Josef Parvizi ◽

Robert T. Knight ◽

...

Keyword(s):

Neural Activity ◽

Orbitofrontal Cortex ◽

Low Frequency ◽

Brain Regions ◽

Reward Processing ◽

Neural Oscillations ◽

Beta Band ◽

Neuronal Ensembles ◽

Gain And Loss

AbstractHuman neuroimaging and animal studies have linked neural activity in orbitofrontal cortex (OFC) to valuation of positive and negative outcomes. Additional evidence shows that neural oscillations, representing the coordinated activity of neuronal ensembles, support information processing in both animal and human prefrontal regions. However, the role of OFC neural oscillations in reward-processing in humans remains unknown, partly due to the difficulty of recording oscillatory neural activity from deep brain regions. Here, we examined the role of OFC neural oscillations (<30Hz) in reward processing by combining intracranial OFC recordings with a gambling task in which patients made economic decisions under uncertainty. Our results show that power in different oscillatory bands are associated with distinct components of reward evaluation. Specifically, we observed a double dissociation, with a selective theta band oscillation increase in response to monetary gains and a beta band increase in response to losses. These effects were interleaved across OFC in overlapping networks and were accompanied by increases in oscillatory coherence between OFC electrode sites in theta and beta band during gain and loss processing, respectively. These results provide evidence that gain and loss processing in human OFC are supported by distinct low-frequency oscillations in networks, and provide evidence that participating neuronal ensembles are organized functionally through oscillatory coherence, rather than local anatomical segregation.

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The Significance of Neural Inter-Frequency Correlations

10.21203/rs.3.rs-329644/v1 ◽

2021 ◽

Author(s):

Oscar Wiljam Savolainen

Keyword(s):

Multiple Testing ◽

Statistical Significance ◽

Significance Test ◽

Stationary Time Series ◽

Frequency Bands ◽

Neural Recordings ◽

Time Frequency ◽

Common Information ◽

Neural Processes ◽

Long Term Trends

Abstract It is of great interest in neuroscience to determine what frequency bands in the brain contain common information. However, to date, a comprehensive statistical approach to this question has been lacking. As such, this work presents a novel statistical significance test for correlated power across frequency bands in non-stationary time series. The test accounts for biases that often go untreated in time-frequency analysis, i.e. intra-frequency autocorrelation, inter-frequency non-dyadicity, and multiple testing under dependency. It is used to test all of the inter-frequency correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings, using a very large, publicly available dataset. The results show that neural processes have signatures across a very broad range of frequency bands. In particular, LFP frequency bands as low as 20 Hz were found to almost always be significantly correlated to kHz frequency ranges. This test also has applications in a broad range of fields, e.g. biological signal processing, economics, finance, climatology, etc. It is useful whenever one wants to robustly determine whether short-term components in a signal are robustly related to long-term trends, or what frequencies contain common information.

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Changes in Degree Centrality of Network Nodes in Different Frequency Bands in Parkinson’s Disease With Depression and Without Depression

Frontiers in Neuroscience ◽

10.3389/fnins.2021.638554 ◽

2021 ◽

Vol 15 ◽

Author(s):

Haiyan Liao ◽

Jinyao Yi ◽

Sainan Cai ◽

Qin Shen ◽

Qinru Liu ◽

...

Keyword(s):

Basal Ganglia ◽

Magnetic Resonance ◽

Frequency Band ◽

Limbic System ◽

Resting State ◽

Brain Regions ◽

Degree Centrality ◽

Functional Magnetic Resonance ◽

Frequency Bands ◽

Medial Frontal Gyrus

BackgroundDepression induces an early onset of Parkinson’s disease (PD), aggravates dyskinesia and cognitive impairment, and accelerates disease progression. However, it is very difficult to identify and diagnose PD with depression (PDD) in the early clinical stage. Few studies have suggested that the changes in neural networks are associated with PDD, while degree centrality (DC) has been documented to be effective in detecting brain network changes.ObjectivesThe objectives of this study are to explore DC changes between patients with PDD and without depression (PDND) and to find the key brain hubs involved with depression in PD patients.MethodsOne hundred and four PD patients and 54 healthy controls (HCs) underwent brain resting-state functional magnetic resonance imaging. The Data Processing and Analysis of Brain Imaging and Resting-State Functional Magnetic Resonance Data Analysis Toolkit were used for processing and statistical analysis. The DC value of each frequency band was calculated. One-way analysis of variance and a two-sample t-test for post hoc comparison were used to compare the differences of the DC values in different frequency bands among PDD, PDND, and healthy control group. Gaussian random field was used for multiple comparison correction. Pearson correlation analysis was performed between each individual’s DC map and clinical indicators.ResultsThe DC value of different brain regions changed in PDD and PDND in different frequency bands. The prefrontal lobe, limbic system, and basal ganglia were the main brain regions involved. PDD patients showed a wider range and more abnormal brain areas in the slow-4 frequency band (0.027–0.073 Hz) compared to the HCs. PDD showed a decreased DC value in the medial frontal gyrus, bilateral cuneus gyrus, right lingual gyrus, bilateral supplementary motor area (SMA), bilateral superior frontal gyrus, and left paracentral lobule, but an increased DC value in the bilateral brainstem, midbrain, bilateral parahippocampal gyrus, cerebellum, left superior temporal gyrus, bilateral insula, left fusiform gyrus, and left caudate nucleus in the traditional frequency band (0.01–0.08 Hz) compared to PDND patients. PDND patients displayed more abnormal functions in the basal ganglia in the slow-4 frequency band.ConclusionThe DC changes in PDD and PDND are frequency dependent and frequency specific. The medial frontal gyrus, SMA, and limbic system may be the key hubs for depression in PD.

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Imaging of neural oscillations with embedded inferential and group prevalence statistics

10.1101/164731 ◽

2017 ◽

Author(s):

Peter W. Donhauser ◽

Esther Florin ◽

Sylvain Baillet

Keyword(s):

Brain Activity ◽

Signal Strength ◽

Brain Regions ◽

Oscillatory Activity ◽

Neural Oscillations ◽

Source Reconstruction ◽

Signal To Noise ◽

Source Imaging ◽

Methodological Challenges ◽

Oscillatory Power

AbstractMagnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. Imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges1. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.Author summaryThe oscillatory activity of the brain produces a repertoire of signal dynamics that is rich and complex. Noninvasive recording techniques such as scalp magnetoencephalography and electroencephalography (MEG, EEG) are key methods to advance our comprehension of the role played by neural oscillations in brain functions and dysfunctions. Yet, there are methodological challenges in mapping these elusive components of brain activity that have remained unresolved. We introduce a new mapping technique, called imaging with embedded statistics (iES), which alleviates these difficulties. With iES, signal detection is constrained explicitly to the operational hypotheses of the study design. We show, in a variety of experimental contexts, how iES emphasizes the oscillatory components of brain activity, if any, that match the experimental hypotheses, even in deeper brain regions where signal strength is expected to be weak in MEG. Overall, the proposed method is a new imaging tool to respond to a wide range of neuroscience questions concerning the scaffolding of brain dynamics via anatomically-distributed neural oscillations.

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