Brain kernel: a new spatial covariance function for fMRI data

A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore the fact that correlations in neural activity may fall off at different rates in different brain areas, or exhibit discontinuities across anatomical or functional boundaries. Moreover, such methods do not exploit the fact that widely separated brain regions may exhibit strong correlations due to bilateral symmetry or the network organization of brain regions. To capture this non-stationary spatial correlation structure, we introduce the brain kernel, a continuous covariance function for whole-brain activity patterns. We define the brain kernel in terms of a continuous nonlinear mapping from 3D brain coordinates to a latent embedding space, parametrized with a Gaussian process (GP). The brain kernel specifies the prior covariance between voxels as a function of the distance between their locations in embedding space. The GP mapping warps the brain nonlinearly so that highly correlated voxels are close together in latent space, and uncorrelated voxels are far apart. We estimate the brain kernel using resting-state fMRI data, and we develop an exact, scalable inference method based on block coordinate descent to overcome the challenges of high dimensionality (10-100K voxels). Finally, we illustrate the brain kernel's usefulness with applications to brain decoding and factor analysis with multiple task-based fMRI datasets.

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Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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Decreased Intrinsic Neural Timescales in Mesial Temporal Lobe Epilepsy

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.772365 ◽

2021 ◽

Vol 15 ◽

Author(s):

Kefan Wang ◽

Xiaonan Zhang ◽

Chengru Song ◽

Keran Ma ◽

Man Bai ◽

...

Keyword(s):

Temporal Lobe Epilepsy ◽

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Mesial Temporal Lobe Epilepsy ◽

Fmri Data ◽

Healthy Controls ◽

Neural Signals ◽

Mesial Temporal Lobe ◽

The Brain

It is well established that epilepsy is characterized by the destruction of the information capacity of brain network and the interference with information processing in regions outside the epileptogenic focus. However, the potential mechanism remains poorly understood. In the current study, we applied a recently proposed approach on the basis of resting-state fMRI data to measure altered local neural dynamics in mesial temporal lobe epilepsy (mTLE), which represents how long neural information is stored in a local brain area and reflect an ability of information integration. Using resting-state-fMRI data recorded from 36 subjects with mTLE and 36 healthy controls, we calculated the intrinsic neural timescales (INT) of neural signals by summing the positive magnitude of the autocorrelation of the resting-state brain activity. Compared to healthy controls, the INT values were significantly lower in patients in the right orbitofrontal cortices, right insula, and right posterior lobe of cerebellum. Whereas, we observed no statistically significant changes between patients with long- and short-term epilepsy duration or between left-mTLE and right-mTLE. Our study provides distinct insight into the brain abnormalities of mTLE from the perspective of the dynamics of the brain activity, highlighting the significant role of intrinsic timescale in understanding neurophysiological mechanisms. And we postulate that altered intrinsic timescales of neural signals in specific cortical brain areas may be the neurodynamic basis of cognitive impairment and emotional comorbidities in mTLE patients.

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Stimulation-specific information is represented as local activity patterns across the brain

10.1101/726414 ◽

2019 ◽

Author(s):

Amirouche Sadoun ◽

Tushar Chauhan ◽

Samir Mameri ◽

Yifan Zhang ◽

Pascal Barone ◽

...

Keyword(s):

Brain Activity ◽

Activity Patterns ◽

Three Dimensional ◽

Correlation Method ◽

Brain Regions ◽

Specific Information ◽

Spatially Correlated ◽

Face Area ◽

Spatial Configurations ◽

The Brain

AbstractModern neuroimaging represents three-dimensional brain activity, which varies across brain regions. It remains unknown whether activity within brain regions is organized in spatial configurations to reflect perceptual and cognitive processes. We developed a rotational cross-correlation method allowing a straightforward analysis of spatial activity patterns for the precise detection of the spatially correlated distributions of brain activity. Using several statistical approaches, we found that the seed patterns in the fusiform face area were robustly correlated to brain regions involved in face-specific representations. These regions differed from the non-specific visual network meaning that activity structure in the brain is locally preserved in stimulation-specific regions. Our findings indicate spatially correlated perceptual representations in cerebral activity and suggest that the 3D coding of the processed information is organized in locally preserved activity patterns. More generally, our results provide the first demonstration that information is represented and transmitted as local spatial configurations of brain activity.

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Functional MRI

10.1093/med/9780190297763.001.0001 ◽

2018 ◽

Keyword(s):

Functional Mri ◽

Resting State ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Blood Oxygen Level Dependent ◽

Brain Regions ◽

Functional Brain Imaging ◽

Fmri Signal ◽

The Brain

Functional MRI with BOLD (Blood Oxygen Level Dependent) imaging is one of the commonly used modalities for studying brain function in neuroscience. The underlying source of the BOLD fMRI signal is the variation in oxyhemoglobin to deoxyhemoglobin ratio at the site of neuronal activity in the brain. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI was developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform any goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. The signals originate from spatially distinct functionally related brain regions but exhibit coherent time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. These networks represent the strength of the functional connectivity between distinct functionally related brain regions and have been used as imaging markers of various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. This book provides a review of the basic principles of fMRI (signal sources, acquisition methods, and data analysis) and its potential clinical applications.

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Spatiotemporal Trajectories in Resting-state FMRI Revealed by Convolutional Variational Autoencoder

10.1101/2021.01.25.427841 ◽

2021 ◽

Author(s):

Xiaodi Zhang ◽

Eric Maltbie ◽

Shella Keilholz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Spatiotemporal Patterns ◽

Network Activity ◽

Fmri Data ◽

Variational Autoencoder ◽

Spatiotemporal Trajectories ◽

The Brain

AbstractRecent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

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The Effects of Acupuncture at Real or Sham Acupoints on the Intrinsic Brain Activity in Mild Cognitive Impairment Patients

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2015/529675 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Baohui Jia ◽

Zhishun Liu ◽

Baoquan Min ◽

Zhenchang Wang ◽

Aihong Zhou ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Healthy Controls ◽

The Difference ◽

The Brain

Accumulating neuroimaging studies in humans have shown that acupuncture can modulate a widely distributed brain network in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients. Acupuncture at different acupoints could exert different modulatory effects on the brain network. However, whether acupuncture at real or sham acupoints can produce different effects on the brain network in MCI or AD patients remains unclear. Using resting-state fMRI, we reported that acupuncture at Taixi (KI3) induced amplitude of low-frequency fluctuation (ALFF) change of different brain regions in MCI patients from those shown in the healthy controls. In MCI patients, acupuncture at KI3 increased or decreased ALFF in the different regions from those activated by acupuncture in the healthy controls. Acupuncture at the sham acupoint in MCI patients activated the different brain regions from those in healthy controls. Therefore, we concluded that acupuncture displays more significant effect on neuronal activities of the above brain regions in MCI patients than that in healthy controls. Acupuncture at KI3 exhibits different effects on the neuronal activities of the brain regions from acupuncture at sham acupoint, although the difference is only shown at several regions due to the close distance between the above points.

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Basic Principles of Functional MRI

Functional MRI ◽

10.1093/med/9780190297763.003.0002 ◽

2018 ◽

pp. 20-29

Author(s):

Cheuk Ying Tang

Keyword(s):

Functional Mri ◽

Resting State ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Brain Regions ◽

Functional Brain Imaging ◽

Basic Principles ◽

Fmri Signal ◽

The Brain

Blood oxygen level dependent (BOLD) MRI, also called functional MRI (fMRI), is one of the most widely used modalities for studying brain function. The underlying source of the fMRI signal is blood flow and the oxygenation state of hemoglobin. fMRI is mostly used to map out the location and intensity of brain activity that correlate with mental activities. In recent years, a new approach to fMRI has been developed that is called resting-state fMRI. The fMRI signal from this method does not require the brain to perform a goal-directed task; it is acquired with the subject at rest. It was discovered that there are low-frequency fluctuations in the fMRI signal in the brain at rest. These signals come from spatially distinct brain regions but exhibit coherent, time-synchronous fluctuations. Several of the networks have been identified and are called resting-state networks. The networks represent the strength of the functional connectivity between distinct brain regions and have been used as imaging biomarkers for various neurological and psychiatric diseases. Resting-state fMRI is also ideally suited for functional brain imaging in disorders of consciousness and in subjects under anesthesia. In this chapter, we provide an introductory review of the basic principles of fMRI: signal sources, acquisition methods, and data analysis.

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Microsleep is associated with brain activity patterns unperturbed by auditory inputs

Journal of Neurophysiology ◽

10.1152/jn.00825.2018 ◽

2019 ◽

Vol 122 (6) ◽

pp. 2568-2575

Author(s):

Zixin Yong ◽

Joo Huang Tan ◽

Po-Jang Hsieh

Keyword(s):

Auditory Cortex ◽

Brain Activity ◽

Activity Patterns ◽

Brain Regions ◽

Auditory Stimulation ◽

Simple Task ◽

Highly Active ◽

Early Afternoon ◽

Mri Scans ◽

The Brain

Microsleeps are brief episodes of arousal level decrease manifested through behavioral signs. Brain activity during microsleep in the presence of external stimulus remains poorly understood. In this study, we sought to understand neural responses to auditory stimulation during microsleep. We gave participants the simple task of listening to audios of different pitches and amplitude modulation frequencies during early afternoon functional MRI scans. We found the following: 1) microsleep was associated with cortical activations in broad motor and sensory regions and deactivations in thalamus, irrespective of auditory stimulation; 2) high and low pitch audios elicited different activity patterns in the auditory cortex during awake but not microsleep state; and 3) during microsleep, spatial activity patterns in broad brain regions were similar regardless of the presence or types of auditory stimulus (i.e., stimulus invariant). These findings show that the brain is highly active during microsleep but the activity patterns across broad regions are unperturbed by auditory inputs. NEW & NOTEWORTHY During deep drowsy states, auditory inputs could induce activations in the auditory cortex, but the activation patterns lose differentiation to high/low pitch stimuli. Instead of random activations, activity patterns across the brain during microsleep appear to be structured and may reflect underlying neurophysiological processes that remain unclear.

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Faces and voices in the brain: a modality-general person-identity representation in superior temporal sulcus

10.1101/338475 ◽

2018 ◽

Author(s):

Maria Tsantani ◽

Nikolaus Kriegeskorte ◽

Carolyn McGettigan ◽

Lúcia Garrido

Keyword(s):

Auditory Processing ◽

Brain Activity ◽

Activity Patterns ◽

Superior Temporal Sulcus ◽

Brain Regions ◽

Similarity Analysis ◽

Representational Similarity Analysis ◽

Person Identity ◽

Identity Representation ◽

The Brain

AbstractFace-selective and voice-selective brain regions have been shown to represent face-identity and voice-identity, respectively. Here we investigated whether there are modality-general person-identity representations in the brain that can be driven by either a face or a voice, and that invariantly represent naturalistically varying face and voice tokens of the same identity. According to two distinct models, such representations could exist either in multimodal brain regions (Campanella and Belin, 2007) or in face-selective brain regions via direct coupling between face- and voice-selective regions (von Kriegstein et al., 2005). To test the predictions of these two models, we used fMRI to measure brain activity patterns elicited by the faces and voices of familiar people in multimodal, face-selective and voice-selective brain regions. We used representational similarity analysis (RSA) to compare the representational geometries of face- and voice-elicited person-identities, and to investigate the degree to which pattern discriminants for pairs of identities generalise from one modality to the other. We found no matching geometries for faces and voices in any brain regions. However, we showed crossmodal generalisation of the pattern discriminants in the multimodal right posterior superior temporal sulcus (rpSTS), suggesting a modality-general person-identity representation in this region. Importantly, the rpSTS showed invariant representations of face- and voice-identities, in that discriminants were trained and tested on independent face videos (different viewpoint, lighting, background) and voice recordings (different vocalizations). Our findings support the Multimodal Processing Model, which proposes that face and voice information is integrated in multimodal brain regions.Significance statementIt is possible to identify a familiar person either by looking at their face or by listening to their voice. Using fMRI and representational similarity analysis (RSA) we show that the right posterior superior sulcus (rpSTS), a multimodal brain region that responds to both faces and voices, contains representations that can distinguish between familiar people independently of whether we are looking at their face or listening to their voice. Crucially, these representations generalised across different particular face videos and voice recordings. Our findings suggest that identity information from visual and auditory processing systems is combined and integrated in the multimodal rpSTS region.

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Meaningful patterns of information in the brain revealed through analysis of errors

10.1101/673681 ◽

2019 ◽

Cited By ~ 2

Author(s):

Alexandra Woolgar ◽

Nadene Dermody ◽

Soheil Afshar ◽

Mark A. Williams ◽

Anina N. Rich

Keyword(s):

Visual Cortex ◽

Brain Activity ◽

Activity Patterns ◽

Brain Regions ◽

Stimulus Response ◽

Correct Rule ◽

Frontoparietal Cortex ◽

Response Task ◽

Incorrect Information ◽

The Brain

SummaryGreat excitement has surrounded our ability to decode task information from human brain activity patterns, reinforcing the dominant view of the brain as an information processor. We tested a fundamental but overlooked assumption: that such decodable information is actually used by the brain to generate cognition and behaviour. Participants performed a challenging stimulus-response task during fMRI. Our novel analyses trained a pattern classifier on data from correct trials, and used it to examine stimulus and rule coding on error trials. There was a striking interaction in which frontoparietal cortex systematically represented incorrect rule but correct stimulus information when participants used the wrong rule, and incorrect stimulus but correct rule information on other types of errors. Visual cortex, by contrast, did not code correct or incorrect information on error. Thus behaviour was tightly linked to coding in frontoparietal cortex and only weakly linked to coding in visual cortex. Human behaviour may indeed result from information-like patterns of activity in the brain, but this relationship is stronger in some brain regions than in others. Testing for information coding on error can help establish which patterns constitute behaviourally-meaningful information.

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