scholarly journals Neural basis of functional ultrasound signals

2021 ◽  
Author(s):  
Anwar O. Nunez-Elizalde ◽  
Michael Krumin ◽  
Charu Bai Reddy ◽  
Gabriel Montaldo ◽  
Alan Urban ◽  
...  
Keyword(s):  
Firing Rate ◽  
Neural Activity ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Linear Filter ◽  
Neural Firing ◽  
Hemodynamic Response Function ◽  
Neural Basis ◽  
Neural Recordings ◽  
Highly Correlated

SummaryFunctional ultrasound imaging (fUSI) is a popular method for studying brain function, but it remains unclear to what degree its signals reflect neural activity on a trial-by-trial basis. Here, we answer this question with simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in firing rate measured in the same location and were closely predicted by convolving the firing rate with a 2.9 s wide linear filter. This filter matched the hemodynamic response function of awake mouse and was invariant across mice, stimulus conditions, and brain regions. fUSI signals matched neural firing also spatially: recordings with two probes revealed that firing rates were as highly correlated across hemispheres as fUSI signals. We conclude that fUSI signals bear a simple linear relationship to neuronal firing and accurately reflect neural activity both in time and in space.

scholarly journals Hidden Markov Models Predict the Future Choice Better Than a PSTH-Based Method

2019 ◽  
Vol 31 (9) ◽  
pp. 1874-1890 ◽  
Author(s):  
Encarni Marcos ◽  
Fabrizio Londei ◽  
Aldo Genovesio
Keyword(s):  
Firing Rate ◽  
Hidden Markov Models ◽  
Neural Activity ◽  
Markov Models ◽  
Hidden Markov ◽  
Experimental Session ◽  
Average Firing Rate ◽  
Action Execution ◽  
Neural Recordings ◽  
Neuronal Ensembles

Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.

scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

2017 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

AbstractWe previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

scholarly journals Interpreting wide-band neural activity using convolutional neural networks

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O'Leary ◽  
Matthias Nau ◽  
...  
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Wide Band ◽  
Brain Regions ◽  
Imaging Data ◽  
User Input ◽  
Neural Recordings ◽  
Learning Framework ◽  
Behavioral Variables ◽  
Spatial Behaviors

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.

scholarly journals Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load

2019 ◽  
Vol 5 (3) ◽  
pp. eaav3687 ◽  
Author(s):  
Ece Boran ◽  
Tommaso Fedele ◽  
Peter Klaver ◽  
Peter Hilfiker ◽  
Lennart Stieglitz ◽  
...  
Keyword(s):  
Working Memory ◽  
Neural Activity ◽  
Medial Temporal Lobe ◽  
Memory Load ◽  
Human Subjects ◽  
Verbal Working Memory ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Scalp Eeg ◽  
Oscillatory Coupling

The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During maintenance, neuronal firing was synchronized with intracranial hippocampal EEG. On the network level, synchronization between hippocampal and scalp EEG in the theta-alpha frequency range showed workload dependent oscillatory coupling between hippocampus and cortex. Thus, we found that persistent neural activity in the hippocampus participated in working memory processing that is specific to memory maintenance, load sensitive and synchronized to the cortex.

Neural Basis of Visual Distraction

2010 ◽  
Vol 22 (8) ◽  
pp. 1794-1807 ◽  
Author(s):  
So-Yeon Kim ◽  
Joseph B. Hopfinger
Keyword(s):  
Neural Activity ◽  
Brain Regions ◽  
Neural Mechanisms ◽  
Intraparietal Sulcus ◽  
Neural Basis ◽  
Luminance Change ◽  
Visual Distraction ◽  
Distractor Processing ◽  
Level Of Activity ◽  
New Evidence

The ability to maintain focus and avoid distraction by goal-irrelevant stimuli is critical for performing many tasks and may be a key deficit in attention-related problems. Recent studies have demonstrated that irrelevant stimuli that are consciously perceived may be filtered out on a neural level and not cause the distraction triggered by subliminal stimuli. However, in everyday situations, suprathreshold stimuli often do capture attention, but the neural mechanisms by which some stimuli rapidly and automatically trigger distraction remain unknown. Here, we investigated the neural basis of distraction by utilizing a particularly strong form of distractor: the abrupt appearance of a new object. Our results revealed a competitive relation between brain regions coding the locations of the target and the distractor, with distractor processing increasing and target processing decreasing, but only when the distractor was a new object; an equivalent luminance change to an existing object neither generated distraction nor affected target processing. Results also revealed changes in neural activity in intraparietal sulcus (IPS) and temporo-parietal junction (TPJ) that were unique to the new object distractor condition. The strongest relations between behavioral distraction and neural activity were observed in these parietal regions. Furthermore, participants who were less susceptible to distraction showed a more consistent, albeit more moderate, level of activity in IPS and TPJ. The present results thus provide new evidence regarding the neural mechanisms underlying distraction and resistance to it.

Global attractor alphabet of neural firing modes

2013 ◽  
Vol 110 (4) ◽  
pp. 907-915 ◽  
Author(s):  
Yoram Baram
Keyword(s):  
Firing Rate ◽  
Global Attractor ◽  
Single Neuron ◽  
Global Attractors ◽  
Neuronal Firing ◽  
Passive State ◽  
Rate Model ◽  
Neural Firing ◽  
Neural Interaction ◽  
Parameter Dependent

The elementary set, or alphabet, of neural firing modes is derived from the widely accepted conductance-based rectified firing-rate model. The firing dynamics of interacting neurons are shown to be governed by a multidimensional bilinear threshold discrete iteration map. The parameter-dependent global attractors of the map morph into 12 attractor types. Consistent with the dynamic modes observed in biological neuronal firing, the global attractor alphabet is highly visual and intuitive in the scalar, single-neuron case. As synapse permeability varies from high depression to high potentiation, the global attractor type varies from chaotic to multiplexed, oscillatory, fixed, and saturated. As membrane permeability decreases, the global attractor transforms from active to passive state. Under the same activation, learning and retrieval end at the same global attractor. The bilinear threshold structure of the multidimensional map associated with interacting neurons generalizes the global attractor alphabet of neuronal firing modes to multineuron systems. Selective positive or negative activation and neural interaction yield combinatorial revelation and concealment of stored neuronal global attractors.

scholarly journals Correlation Between BOLD fMRI and Theta-Band Local Field Potentials in the Human Hippocampal Area

2009 ◽  
Vol 101 (5) ◽  
pp. 2668-2678 ◽  
Author(s):  
Arne Ekstrom ◽  
Nanthia Suthana ◽  
David Millett ◽  
Itzhak Fried ◽  
Susan Bookheimer
Keyword(s):  
Firing Rate ◽  
Local Field ◽  
Neural Activity ◽  
Local Field Potentials ◽  
Bold Signal ◽  
Field Potentials ◽  
Neural Firing ◽  
Theta Band ◽  
Hippocampal Area ◽  
Signal Changes

The relation between the blood-oxygen-level-dependent (BOLD) signal, which forms the basis of functional magnetic resonance imaging (fMRI), and underlying neural activity is not well understood. We performed high-resolution fMRI in patients scheduled for implantation with depth electrodes for seizure monitoring while they navigated a virtual environment. We then recorded local field potentials (LFPs) and neural firing rate directly from the hippocampal area of the same subjects during the same task. Comparing BOLD signal changes with 396 LFP and 185 neuron recordings in the hippocampal area, we found that BOLD signal changes correlated positively with LFP power changes in the theta-band (4–8 Hz). This correlation, however, was largely present for parahippocampal BOLD signal changes; BOLD changes in the hippocampus correlated weakly or not at all with LFP power changes. We did not find a significant relationship between BOLD activity and neural firing rate in either region, which could not be accounted for by a lesser tendency for neurons to respond or a greater tendency for neurons to habituate to the task. Strengthening the idea of a dissociation between LFP power and neural firing rate in their relation to the BOLD signal, simultaneously recorded LFP power and neural firing rate changes were uncorrelated across electrodes. Together, our results suggest that the BOLD signal in the human hippocampal area has a more heterogenous relationship with underlying neural activity than has been described previously in other brain regions.

scholarly journals Phase-tuned neuronal firing encodes human contextual representations for navigational goals

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Andrew J Watrous ◽  
Jonathan Miller ◽  
Salman E Qasim ◽  
Itzhak Fried ◽  
Joshua Jacobs
Keyword(s):  
Firing Rate ◽  
Medial Temporal Lobe ◽  
Single Neuron ◽  
Detection Algorithm ◽  
Neuronal Firing ◽  
Neural Firing ◽  
Categorical Information ◽  
High Frequency Activity ◽  
Oscillation Detection ◽  
Intracranial Recordings

We previously demonstrated that the phase of oscillations modulates neural activity representing categorical information using human intracranial recordings and high-frequency activity from local field potentials (Watrous et al., 2015b). We extend these findings here using human single-neuron recordings during a virtual navigation task. We identify neurons in the medial temporal lobe with firing-rate modulations for specific navigational goals, as well as for navigational planning and goal arrival. Going beyond this work, using a novel oscillation detection algorithm, we identify phase-locked neural firing that encodes information about a person’s prospective navigational goal in the absence of firing rate changes. These results provide evidence for navigational planning and contextual accounts of human MTL function at the single-neuron level. More generally, our findings identify phase-coded neuronal firing as a component of the human neural code.

scholarly journals Population imaging of neural activity in awake behaving mice in multiple brain regions

2019 ◽  
Author(s):  
Kiryl D. Piatkevich ◽  
Seth Bensussen ◽  
Hua-an Tseng ◽  
Sanaya N. Shroff ◽  
Violeta Gisselle Lopez-Huerta ◽  
...  
Keyword(s):  
Neural Activity ◽  
Hippocampal Neurons ◽  
High Performance ◽  
Response Times ◽  
Signal To Noise Ratio ◽  
Population Analysis ◽  
Brain Regions ◽  
Striatal Neurons ◽  
Temporal Precision ◽  
Highly Correlated

AbstractA longstanding goal in neuroscience has been to image membrane voltage, with high temporal precision and sensitivity, in awake behaving mammals. Here, we report a genetically encoded voltage indicator, SomArchon, which exhibits millisecond response times and compatibility with optogenetic control, and which increases the sensitivity, signal-to-noise ratio, and number of neurons observable, by manyfold over previous reagents. SomArchon only requires conventional one-photon microscopy to achieve these high performance characteristics. These improvements enable population analysis of neural activity, both at the subthreshold and spiking levels, in multiple brain regions – cortex, hippocampus, and striatum – of awake behaving mice. Using SomArchon, we detect both positive and negative responses of striatal neurons during movement, highlighting the power of voltage imaging to reveal bidirectional modulation. We also examine how the intracellular subthreshold theta oscillations of hippocampal neurons govern spike output, finding that nearby cells can exhibit highly correlated subthreshold activities, even as they generate highly divergent spiking patterns.

scholarly journals Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile

2016 ◽  
Vol 113 (51) ◽  
pp. E8306-E8315 ◽  
Author(s):  
Alex T. L. Leong ◽  
Russell W. Chan ◽  
Patrick P. Gao ◽  
Ying-Shing Chan ◽  
Kevin K. Tsia ◽  
...  
Keyword(s):  
Long Range ◽  
Neural Activity ◽  
Visual Processing ◽  
Large Scale ◽  
Functional Integration ◽  
Low Frequency ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Neural Basis ◽  
Excitatory Neurons

One challenge in contemporary neuroscience is to achieve an integrated understanding of the large-scale brain-wide interactions, particularly the spatiotemporal patterns of neural activity that give rise to functions and behavior. At present, little is known about the spatiotemporal properties of long-range neuronal networks. We examined brain-wide neural activity patterns elicited by stimulating ventral posteromedial (VPM) thalamo-cortical excitatory neurons through combined optogenetic stimulation and functional MRI (fMRI). We detected robust optogenetically evoked fMRI activation bilaterally in primary visual, somatosensory, and auditory cortices at low (1 Hz) but not high frequencies (5–40 Hz). Subsequent electrophysiological recordings indicated interactions over long temporal windows across thalamo-cortical, cortico-cortical, and interhemispheric callosal projections at low frequencies. We further observed enhanced visually evoked fMRI activation during and after VPM stimulation in the superior colliculus, indicating that visual processing was subcortically modulated by low-frequency activity originating from VPM. Stimulating posteromedial complex thalamo-cortical excitatory neurons also evoked brain-wide blood-oxygenation-level–dependent activation, although with a distinct spatiotemporal profile. Our results directly demonstrate that low-frequency activity governs large-scale, brain-wide connectivity and interactions through long-range excitatory projections to coordinate the functional integration of remote brain regions. This low-frequency phenomenon contributes to the neural basis of long-range functional connectivity as measured by resting-state fMRI.

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