scholarly journals The major brain networks of human visual consciousness

2021 ◽  
Author(s):  
Hal Blumenfeld ◽  
Sharif Kronemer ◽  
Mark Aksen ◽  
Julia Ding ◽  
Jun Ryu ◽  
...  
Keyword(s):  
Signal Detection ◽  
Brain Networks ◽  
Modern Science ◽  
Anterior Cingulate ◽  
Large Sample Size ◽  
Attention And Executive Control ◽  
Spatiotemporal Resolution ◽  
Control Networks ◽  
Neuroimaging Data ◽  
Visual Consciousness

Abstract Understanding consciousness is one of the most important and challenging questions in modern science. Existing theories have pursued single unifying mechanisms but do not succeed in explaining consciousness. Importantly, the neural circuits that distinguish messages that arrive from the outside world and attain consciousness have remained unknown. Here we identify signals throughout the entire brain at high spatiotemporal resolution specifically related to consciousness. To accomplish this, we combined a large sample size of electrical and neuroimaging data with a novel experimental approach to remove confounding signal unrelated to consciousness1-3. We discovered three major brain networks driving conscious visual perception. First, we found increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula. Second, we found increases in frontoparietal attention and executive control networks and in the cerebellum. Finally, we found decreases in the default mode network. Our results identify subcortical and cortical networks designed for signal detection, attentional amplification, and perceptual processing that together can explain visual consciousness. These findings provide evidence that understanding consciousness can be reframed as requiring multiple overlapping brain networks to produce consciousness of visual events4.

scholarly journals Brain Resilience: The Effect of White Matter Disease on Brain Networks in Cognitively Normal Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 918-918
Author(s):  
Blake Neyland ◽  
Christina Hugenschmidt ◽  
Samuel Lockhart ◽  
Laura Baker ◽  
Suzanne Craft ◽  
...  
Keyword(s):  
Older Adults ◽  
White Matter ◽  
White Matter Hyperintensities ◽  
Brain Networks ◽  
Network Connectivity ◽  
Anterior Cingulate ◽  
Functional Brain ◽  
Mobility Function ◽  
Functional Brain Networks ◽  
Brain Resilience

Abstract Brain pathologies are increasingly understood to confer mobility risk, but the malleability of functional brain networks may be a mechanism for mobility reserve. In particular, white matter hyperintensities (WMH) are strongly associated with mobility and alter functional network connectivity. To assess the potential role of brain networks as a mechanism of mobility reserve, 116 participants with MRI from the Brain Networks and Mobility Function (B-NET) were categorized into 4 groups based on median splits of SPPB scores and WMH burden: Expected Healthy (EH: low WMH, SPPB>10, N=45), Expected Impaired (EI: high WMH, SPPB10, N=24), Unexpected Impaired (EI: low WMH, SPPB<10, N=10) and Unexpected Unhealthy (UH: low WMH, SPPB<10, N=37). Functional brain networks were calculated using graph theory methods and white matter hyperintensities were quantified with the Lesion Segmentation Toolbox (LST) in SPM12. Somatomotor cortex community structure (SMC-CS) was similar between UH and EH with both having higher consistency than EI and UI. However, UH displayed a unique increase in second-order connections between the motor cortex and the anterior cingulate. It is possible that this increase in connections is a signal of higher reserve or resilience in UH participants and may indicate a mechanism of compensation in regards to mobility function and advanced WMH burden. These data suggest functional brain networks may be a mechanism for mobility resilience in older adults at mobility risk due to WMH burden.

scholarly journals Individualized non-invasive brain stimulation engages the subgenual anterior cingulate and amygdala

2018 ◽  
Author(s):  
Desmond J Oathes ◽  
Jared Zimmerman ◽  
Romain Duprat ◽  
Seda Cavdaroglu ◽  
Morgan Scully ◽  
...  
Keyword(s):  
Brain Stimulation ◽  
Brain Networks ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Clinical Effects ◽  
Brain Response ◽  
Subcortical Structures ◽  
Non Invasive ◽  
Individual Participant ◽  
Communication Flow

Brain stimulation is used clinically to treat a variety of neurological and psychiatric conditions. The mechanisms of the clinical effects of these brain-based therapies are presumably dependent on their effects on brain networks. It has been hypothesized that using individualized brain network maps is an optimal strategy for defining network boundaries and topologies. Traditional non-invasive imaging can determine correlations between structural or functional time series. However, they cannot easily establish hierarchies in communication flow as done in non-human animals using invasive methods. In the present study, we interleaved functional MRI recordings with non-invasive transcranial magnetic stimulation in the attempt to map causal communication between the prefrontal cortex and two subcortical structures thought to contribute to affective dysregulation: the subgenual anterior cingulate cortex (sgACC) and the amygdala. In both cases, we found evidence that these brain areas were engaged when TMS was applied to prefrontal sites determined from each participant's previous fMRI scan. Specifically, after transforming individual participant images to within-scan quantiles of evoked TMS response, we modeled the average quantile response within a given region across stimulation sites and individuals to demonstrate that the targets were differentially influenced by TMS. Furthermore, we found that the sgACC distributed brain network, estimated in a separate cohort, was engaged in response to sgACC focused TMS and was partially separable from the proximal default mode network response. The amygdala, but not its distributed network, responded to TMS. Our findings indicate that individual targeting and brain response measurements usefully capture causal circuit mapping to the sgACC and amygdala in humans, setting the stage for approaches to non-invasively modulate subcortical nodes of distributed brain networks in clinical interventions and mechanistic human neuroscience studies.

scholarly journals Social structure learning in human anterior insula

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tatiana Lau ◽  
Samuel J Gershman ◽  
Mina Cikara
Keyword(s):  
Social Group ◽  
Structure Learning ◽  
Functional Neuroimaging ◽  
Group Structure ◽  
Anterior Insula ◽  
Anterior Cingulate ◽  
Latent Structure ◽  
Group Representations ◽  
Neuroimaging Data ◽  
Category Labels

Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and interpersonal, or dyadic, similarity as the sole inputs to social group representations. Using a model-based analysis of functional neuroimaging data, we find that separate areas correlate with dyadic similarity and latent structure learning. Trial-by-trial estimates of ‘allyship’ based on dyadic similarity between participants and each agent recruited medial prefrontal cortex/pregenual anterior cingulate (pgACC). Latent social group structure-based allyship estimates, in contrast, recruited right anterior insula (rAI). Variability in the brain signal from rAI improved prediction of variability in ally-choice behavior, whereas variability from the pgACC did not. These results provide novel insights into the psychological and neural mechanisms by which people learn to distinguish ‘us’ from ‘them.’

scholarly journals Depression Is Associated With Preserved Cortical Thickness Relative to Apathy in Frontotemporal Dementia

2020 ◽  
pp. 089198872096425
Author(s):  
Rakshathi Basavaraju ◽  
Xinyang Feng ◽  
Jeanelle France ◽  
Edward D. Huey ◽  
Frank A. Provenzano
Keyword(s):  
Neurodegenerative Disease ◽  
Frontal Cortex ◽  
Frontotemporal Dementia ◽  
Cortical Thickness ◽  
Gray Matter ◽  
Anterior Cingulate ◽  
Parietal Lobes ◽  
Neuroimaging Data ◽  
Rostral Anterior Cingulate Cortex ◽  
The Right

Objectives: To understand the differential neuroanatomical substrates underlying apathy and depression in Frontotemporal dementia (FTD). Methods: T1-MRIs and clinical data of patients with behavioral and aphasic variants of FTD were obtained from an open database. Cortical thickness was derived, its association with apathy severity and difference between the depressed and not depressed were examined with appropriate covariates. Results: Apathy severity was significantly associated with cortical thinning of the lateral parts of the right sided frontal, temporal and parietal lobes. The right sided orbitofrontal, parsorbitalis and rostral anterior cingulate cortex were thicker in depressed compared to patients not depressed. Conclusions: Greater thickness of right sided ventromedial and inferior frontal cortex in depression compared to patients without depression suggests a possible requisite of gray matter in this particular area for the manifestation of depression in FTD. This study demonstrates a method for deriving neuroanatomical patterns across non-harmonized neuroimaging data in a neurodegenerative disease.

scholarly journals Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

2020 ◽  
Vol 10 (2) ◽  
pp. 115 ◽  
Author(s):  
Chella Kamarajan ◽  
Babak A. Ardekani ◽  
Ashwini K. Pandey ◽  
Sivan Kinreich ◽  
Gayathri Pandey ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Random Forest ◽  
Alcohol Use ◽  
Long Range ◽  
Alcohol Use Disorder ◽  
Neuropsychological Functioning ◽  
Brain Networks ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Neuropsychological Performance

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior–posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.

scholarly journals A statistical model for brain networks inferred from large-scale electrophysiological signals

2017 ◽  
Vol 14 (128) ◽  
pp. 20160940 ◽  
Author(s):  
Catalina Obando ◽  
Fabrizio De Vico Fallani
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Brain Networks ◽  
Biological Data ◽  
Current View ◽  
Exponential Random Graph Models ◽  
Eyes Closed ◽  
Graph Metrics ◽  
Neuroimaging Data ◽  
Eyes Open

Network science has been extensively developed to characterize the structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graph models (ERGMs) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes than other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) than EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the functional segregation and integration of the brain in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

scholarly journals Rat anterior cingulate cortex recalls features of remote reward locations after disfavoured reinforcements

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ali Mashhoori ◽  
Saeedeh Hashemnia ◽  
Bruce L McNaughton ◽  
David R Euston ◽  
Aaron J Gruber
Keyword(s):  
Anterior Cingulate Cortex ◽  
Cognitive Processes ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Task Space ◽  
Spatiotemporal Resolution ◽  
Related Information ◽  
Current State ◽  
Multimodal Information ◽  
And Task

The anterior cingulate cortex (ACC) encodes information supporting mnemonic and cognitive processes. We show here that a rat’s position can be decoded with high spatiotemporal resolution from ACC activity. ACC neurons encoded the current state of the animal and task, except for brief excursions that sometimes occurred at target feeders. During excursions, the decoded position became more similar to a remote target feeder than the rat’s physical position. Excursions recruited activation of neurons encoding choice and reward, and the likelihood of excursions at a feeder was inversely correlated with feeder preference. These data suggest that the excursion phenomenon was related to evaluating real or fictive choice outcomes, particularly after disfavoured reinforcements. We propose that the multiplexing of position with choice-related information forms a mental model isomorphic with the task space, which can be mentally navigated via excursions to recall multimodal information about the utility of remote locations.

scholarly journals Ultrasound functional neuroimaging reveals propagation of task-related brain activity in behaving primates

2018 ◽  
Author(s):  
Alexandre Dizeux ◽  
Marc Gesnik ◽  
Harry Ahnine ◽  
Kevin Blaize ◽  
Fabrice Arcizet ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Anterior Cingulate ◽  
Global Network ◽  
High Temporal Resolution ◽  
Wide Field ◽  
Spatiotemporal Resolution ◽  
Macro Scale ◽  
The Brain

ABSTRACTIn recent decades, neuroimaging has played an invaluable role in improving the fundamental understanding of the brain. At the macro scale, neuroimaging modalities such as MRI, EEG, and MEG, exploit a wide field of view to explore the brain as a global network of interacting regions. However, this comes at the price of either limited spatiotemporal resolution or limited sensitivity. At the micro scale, electrophysiology is used to explore the dynamic aspects of neuronal activity with a very high temporal resolution. However, this modality requires a statistical averaging of several tens of single task responses. A large-scale neuroimaging modality of sufficient spatial and temporal resolution and sensitivity to study brain region activation dynamically would open new territories of possibility in neuroscienceWe show that neurofunctional ultrasound imaging (fUS) is both able to assess brain activation during single cognitive tasks within superficial and deeper areas of the frontal cortex areas, and image the directional propagation of information within and between these regions. Equipped with an fUS device, two macaque rhesus monkeys were instructed before a stimulus appeared to rest (fixation) or to look towards (saccade) or away (antisaccade) from a stimulus. Our results identified an abrupt transient change in activity for all acquisitions in the supplementary eye field (SEF) when the animals were required to change a rule regarding the task cued by a stimulus. Simultaneous imaging in the anterior cingulate cortex and SEF revealed a time delay in the directional functional connectivity of 0.27 ± 0.07 s and 0.9 ± 0.2 s for animals S and Y, respectively. These results provide initial evidence that recording cerebral hemodynamics over large brain areas at a high spatiotemporal resolution and sensitivity with neurofunctional ultrasound can reveal instantaneous monitoring of endogenous brain signals and behavior.

scholarly journals Changes of Structural and Functional Attention Control Networks in Subclinical Hypothyroidism

2021 ◽  
Vol 15 ◽  
Author(s):  
Jingjing Yin ◽  
Lei Xie ◽  
DongXue Luo ◽  
Jinzhuang Huang ◽  
Ruiwei Guo ◽  
...  
Keyword(s):  
Stroop Task ◽  
Subclinical Hypothyroidism ◽  
Attentional Control ◽  
Structural Changes ◽  
Cingulate Cortex ◽  
Brain Network ◽  
Attention Control ◽  
Anterior Cingulate ◽  
Middle Temporal Gyrus ◽  
Control Networks

Objective: This study aimed to explore the structural changes in patients with subclinical hypothyroidism (SCH) using voxel-based morphometry (VBM) and to investigate the altered attentional control networks using functional MRI (fMRI) during the performance of a modified Stroop task with Chinese characters.Methods: High-resolution three-dimensional (3D) T1-weighted images and an fMRI scan were taken from 18 patients with SCH and 18 matched control subjects. The Montreal Cognitive Assessment Chinese-revised (MoCA-CR) and the Stroop task were used to evaluate the cognitive and attention control of the participants.Results: Compared to controls, the VBM results showed decreased gray matter volumes (GMVs) in bilateral prefrontal cortices (PFCs, including middle, medial, and inferior frontal gyri), cingulate gyrus, precuneus, left middle temporal gyrus, and insula in patients with SCH. The fMRI results showed a distributed network of brain regions in both groups, consisting of PFCs (including superior and middle and inferior frontal cortices), anterior cingulate cortex (ACC), posterior cingulate cortex, and precuneus, as well as the insula and caudate nucleus. Compared to controls, the SCH group had lower activation of the above brain areas, especially during the color-naming task. In addition, the normalized GMV (nGMV) was negatively correlated with thyroid-stimulating hormone (TSH) level (r = −0.722, p < 0.001).Conclusion: Results indicate that patients with SCH exhibit reduced GMVs, altered BOLD signals, and activation in regions associated with attention control, which further suggest that patients with SCH may have attentional control deficiency, and the weakened PFC–ACC–precuneus brain network might be one of the neural mechanisms. Negative correlations between nGMV and TSH suggest that TSH elevation may induce abnormalities in the cortex.

scholarly journals Effector-independent brain network for auditory-motor integration: fMRI evidence from singing and cello playing

2020 ◽  
Author(s):  
Melanie Segado ◽  
Robert J. Zatorre ◽  
Virginia B. Penhune
Keyword(s):  
Motor Control ◽  
Auditory Feedback ◽  
Brain Activity ◽  
Brain Networks ◽  
Anterior Cingulate ◽  
List Type ◽  
Control Mechanisms ◽  
Monitoring And Control ◽  
Pitch Regulation ◽  
The Brain

AbstractMany everyday tasks share high-level sensory goals but differ in the movements used to accomplish them. One example of this is musical pitch regulation, where the same notes can be produced using the vocal system or a musical instrument controlled by the hands. Cello playing has previously been shown to rely on brain structures within the singing network for performance of single notes, except in areas related to primary motor control, suggesting that the brain networks for auditory feedback processing and sensorimotor integration may be shared (Segado et al. 2018). However, research has shown that singers and cellists alike can continue singing/playing in tune even in the absence of auditory feedback (Chen et al. 2013, Kleber et al. 2013), so different paradigms are required to test feedback monitoring and control mechanisms. In singing, auditory pitch feedback perturbation paradigms have been used to show that singers engage a network of brain regions including anterior cingulate cortex (ACC), anterior insula (aINS), and intraparietal sulcus (IPS) when compensating for incorrect pitch feedback, and posterior superior temporal gyrus (pSTG) and supramarginal gyrus (SMG) when ignoring it (Zarate et al. 2005, 2008). To determine whether the brain networks for cello playing and singing directly overlap in these sensory-motor integration areas, in the present study expert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning. We found that cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well. Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring; differences between singing/playing across all three conditions were most prominent in M1, centered on the relevant motor effectors (hand, larynx). These findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network and suggests that differences arise primarily at the level of forward motor control.HighlightsExpert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning.Cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well.Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring.Differences between singing/playing across were most prominent in M1, centered around the relevant motor effectors (hand, larynx)Findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network with differences arising primarily at the level of forward motor control

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