Variance and Autocorrelation of the Spontaneous Slow Brain Activity

Kaneoke, Yoshiki; Donishi, Tomohiro; Iwatani, Jun; Ukai, Shigeo; Shinosaki, Kazuhiro; Tomita, Masaki

doi:10.1371/journal.pone.0038131

Cited by 25 publications

(28 citation statements)

References 35 publications

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“…where r jk is the correlation between original overt speech and reconstruction, r jh is the correlation between original overt speech and baseline reconstruction and r kh is the correlation between overt speech reconstruction and baseline reconstruction; df = n − 3 is the effective sample size (Kaneoke et al, 2012) and where…”

Section: Methodsmentioning

confidence: 99%

Decoding spectrotemporal features of overt and covert speech from the human cortex

et al. 2014

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Auditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70–150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p < 10−5; paired two-sample t-test). For the covert speech condition, dynamic time warping was first used to realign the covert speech reconstruction with the corresponding original speech from the overt condition. Reconstruction accuracy was then evaluated as the correlation between original and reconstructed speech features. Covert reconstruction accuracy was compared to the accuracy obtained from reconstructions in the baseline control condition. Reconstruction accuracy for the covert condition was significantly better than for the control condition (p < 0.005; paired two-sample t-test). The superior temporal gyrus, pre- and post-central gyrus provided the highest reconstruction information. The relationship between overt and covert speech reconstruction depended on anatomy. These results provide evidence that auditory representations of covert speech can be reconstructed from models that are built from an overt speech data set, supporting a partially shared neural substrate.

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Section: Methodsmentioning

confidence: 99%

Decoding spectrotemporal features of overt and covert speech from the human cortex

et al. 2014

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“…Various brain networks relevant to specific functions and diseases have been identified [13]–[20]. Electrophysiological studies have presented evidence of a modified CNS network in tinnitus subjects [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…Because time series data is not random, the effective sample size of independent measurements across time must be estimated in order to calculate the statistical significance of FC [29]. The effective sample size can be estimated using the autocorrelation coefficient (AC) in the two regions as discussed in our previous study [20]. AC has physiological relevance [20], with low autocorrelation values distributed around the caudal brain regions and high values observed in the default mode network (DMN) regions, defined as “ a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment” [15].…”

Section: Introductionmentioning

confidence: 99%

“…The effective sample size can be estimated using the autocorrelation coefficient (AC) in the two regions as discussed in our previous study [20]. AC has physiological relevance [20], with low autocorrelation values distributed around the caudal brain regions and high values observed in the default mode network (DMN) regions, defined as “ a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment” [15]. In this study, we evaluated two types of regional global connectivity without sample size adjustment ( rGC ) or regional global connectivity with sample size adjustment ( rGCa ) in subjects with tinnitus.…”

Section: Introductionmentioning

confidence: 99%

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Brain Regions Responsible for Tinnitus Distress and Loudness: A Resting-State fMRI Study

et al. 2013

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Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC) strength is affected by the regional autocorrelation coefficient (AC). In this study, we used resting-state functional MRI (fMRI) and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC) and with taking into account the effect of AC (rGCa) to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.

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“…Maxim et al modeled the noise of rsfMRI data with a fractional Gaussian noise model and demonstrated that variance of rsfMRI was higher in ventricles and the rim of cortices and patients with Alzheimer’s disease had higher variance in places near the ventricles and sulcal CSF [29]. Kaneoke et al [21] showed higher variance also in regions near brain ventricles and CSF but lower variance in many other places including temporal lobes, striatum, insula, visual cortex, and prefrontal cortex. No one has yet paid attention to the higher-order TTFs.…”

Section: Introductionmentioning

confidence: 99%

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

Zang

Ding

et al. 2016

Med Biol Eng Comput

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The time-to-time fluctuations (TTFs) of resting-state brain activity as captured by resting-state fMRI (rsfMRI) have been repeatedly shown to be informative of functional brain structures and disease-related alterations. TTFs can be characterized by the mean and the range of successive difference. The former can be measured with the mean squared successive difference (MSSD), which is mathematically similar to standard deviation; the latter can be calculated by the variability of the successive difference (VSD). The purpose of this study was to evaluate both the resting state-MSSD and VSD of rsfMRI regarding their test–retest stability, sensitivity to brain state change, as well as their biological meanings. We hypothesized that MSSD and VSD are reliable in resting brain; both measures are sensitive to brain state changes such as eyes-open compared to eyes-closed condition; both are predictive of age. These hypotheses were tested with three rsfMRI datasets and proven true, suggesting both MSSD and VSD as reliable and useful tools for resting-state studies.

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Variance and Autocorrelation of the Spontaneous Slow Brain Activity

Cited by 25 publications

References 35 publications

Decoding spectrotemporal features of overt and covert speech from the human cortex

Decoding spectrotemporal features of overt and covert speech from the human cortex

Brain Regions Responsible for Tinnitus Distress and Loudness: A Resting-State fMRI Study

Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity

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