03070nas a2200301 4500008004100000022001400041245007000055210006700125260001600192300001100208490000700219520218100226653001002407653001002417653001502427653002502442653001202467653001102479653003102490653001402521653000902535100002102544700001702565700002302582700002402605700002302629856011602652 2024 eng d a1097-019300aComplexity organization of resting-state functional-MRI networks.0 aComplexity organization of restingstate functionalMRI networks c2024 Aug 15 ae268090 v453 a

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

10aAdult10aBrain10aConnectome10aDefault Mode Network10aEntropy10aHumans10aMagnetic Resonance Imaging10aNerve Net10aRest1 aTrevino, Gabriel1 aLee, John, J1 aShimony, Joshua, S1 aLuckett, Patrick, H1 aLeuthardt, Eric, C uhttps://www.neurotechcenter.org/publications/2024/complexity-organization-resting-state-functional-mri-networks02753nas a2200373 4500008004100000022001400041245011800055210006900173260001200242300001100254490000800265520163200273653001001905653001501915653001701930653002001947653001501967653002501982653001102007653001702018653001102035653000902046653001602055653001402071653002802085653001802113653001602131100003202147700002102179700001902200700001902219700001902238856012202257 2021 eng d a1095-957200aWithin-subject reaction time variability: Role of cortical networks and underlying neurophysiological mechanisms.0 aWithinsubject reaction time variability Role of cortical network c08/2021 a1181270 v2373 a

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject's task is the same from trial to trial.

10aAdult10aAlgorithms10aAlpha Rhythm10aCerebral Cortex10aConnectome10aElectrocorticography10aFemale10aGamma Rhythm10aHumans10aMale10aMiddle Aged10aNerve Net10aPsychomotor Performance10aReaction Time10aYoung Adult1 aParaskevopoulou, Sivylla, E1 aCoon, William, G1 aBrunner, Peter1 aMiller, Kai, J1 aSchalk, Gerwin uhttps://www.neurotechcenter.org/publications/2021/within-subject-reaction-time-variability-role-cortical-networks-and03803nas a2200433 4500008004100000022001400041024002500055245010000080210006900180260001200249300001100261490000600272520257600278653001002854653001002864653001802874653002502892653002702917653002202944653002802966653001102994653001103005653001603016653000903032653001603041653001403057653003403071653002803105100001903133700002203152700001803174700002103192700001803213700002903231700001803260700002403278700001903302856004803321 2011 eng d a1741-2552 aNIHMSID: NIHMS48176700aUsing the electrocorticographic speech network to control a brain-computer interface in humans.0 aUsing the electrocorticographic speech network to control a brai c06/2011 a0360040 v83 a

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

10aAdult10aBrain10aBrain Mapping10aComputer Peripherals10aElectroencephalography10aEvoked Potentials10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aNerve Net10aSpeech Production Measurement10aUser-Computer Interface1 aLeuthardt, E C1 aGaona, Charles, M1 aSharma, Mohit1 aSzrama, Nicholas1 aRoland, Jarod1 aFreudenberg, Zachary, V.1 aSolisb, Jamie1 aBreshears, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/21471638