Functional connectivity based parcellation of early visual cortices

Impact of Deprivation and Preferential Usage on Functional Connectivity Between Early Visual Cortex and Category-Selective Visual Regions

Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how removal of input changes brain function. However, an important question yet to be answered is: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. When central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with some portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. Specifically, we examined functional connectivity between category-selective visual areas and the cortical representation of three areas of the retina: the lesioned area, a preferentially used region of the intact retina, and a non-preferentially used region. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that the long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

Intra and inter-regional functional connectivity of the human brain due to Task-Evoked fMRI Data classification through CNN & LSTM

BACKGROUND AND PURPOSE

Olfaction is an early marker of neurodegenerative disease. Standard olfactory function is essential due to the importance of olfaction in human life. The psychophysical evaluation assesses the olfactory function commonly. It is patient-reported, and results rely on the patient's answers and collaboration. However, methodological difficulties attributed to the psychophysical evaluation of olfactory-related cerebral areas led to limited assessment of olfactory function in the human brain.

MATERIALS AND METHODS

The current study utilized clustering approaches to assess olfactory function in fMRI data and used brain activity to parcellate the brain with homogeneous properties. Deep neural network architecture based on ResNet convolutional neural networks (CNN) and Long Short-Term Model (LSTM) designed to classify healthy with olfactory disorders subjects.

RESULTS

The fMRI result obtained by k-means unsupervised machine learning model was within the expected outcome and similar to those found with the conn toolbox in detecting active areas. There was no significant difference between the means of subjects and every subject. Proposing a CRNN deep learning model to classify fMRI data in two different healthy and with olfactory disorders groups leads to an accuracy score of 97 %.

CONCLUSIONS

The K-means unsupervised algorithm can detect the active regions in the brain and analyze olfactory function. Classification results prove the CNN-LSTM architecture using ResNet provides the best accuracy score in olfactory fMRI data. It is the first attempt conducted on olfactory fMRI data in detail until now.

Impact of deprivation and preferential usage on functional connectivity between early visual cortex and category selective visual regions

Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how the removal of input changes brain function. However, an important question has yet to be answered: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. However, when central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with certain portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

Highlights

Portions of early visual cortex representing central vs. peripheral vision exhibit different patterns of connectivity to category-selective visual regions.When central vision is lost, cortical representations of peripheral vision display stronger functional connections to MT than central representations.When central vision is lost, connectivity to regions selective for tasks that involve central vision (FFA and PHA) are not significantly altered.These effects do not depend on which locations of peripheral vision are used more.

DRD2 TaqIA polymorphism-related functional connectivity between anterior insula and dorsolateral prefrontal cortex predicts the retention time in heroin-dependent individuals under methadone maintenance treatment

BACKGROUND

Dopamine receptor D2 (DRD2) TaqIA polymorphism has an influence on addiction treatment response and prognosis by mediating brain dopaminergic system efficacy. Insula is crucial for conscious urges to take drugs and maintain drug use. However, it remains unclear about the contribution of DRD2 TaqIA polymorphism to the regulation of insular on addiction behavioral and its relation with the therapeutic effect of methadone maintenance treatment (MMT).

METHODS

57 male former heroin dependents receiving stable MMT and 49 matched male healthy controls (HC) were enrolled. Salivary genotyping for DRD2 TaqA1 and A2 alleles, brain resting-state functional MRI scan and a 24-month follow-up for collecting illegal-drug-use information was conducted and followed by clustering of functional connectivity (FC) patterns of HC insula, insula subregion parcellation of MMT patients, comparing the whole brain FC maps between the A1 carriers and non-carriers and analyzing the correlation between the genotype-related FC of insula sub-regions with the retention time in MMT patients by Cox regression.

RESULTS

Two insula subregions were identified: the anterior insula (AI) and the posterior insula (PI) subregion. The A1 carriers had a reduced FC between the left AI and the right dorsolateral prefrontal cortex (dlPFC) relative to no carriers. And this reduced FC was a poor prognostic factor for the retention time in MMT patients.

CONCLUSION

DRD2 TaqIA polymorphism affects the retention time in heroin-dependent individuals under MMT by mediating the functional connectivity strength between left AI and right dlPFC, and the two brain regions are promising therapeutic targets for individualized treatment.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy with cortical shape having been shown to be a useful predictor of the retinotopic organization in early visual cortex. Although the current state-of-the-art in predicting retinotopic maps is able to account for gross individual differences, such models are unable to account for any idiosyncratic differences in the structure-function relationship from anatomical information alone due to their initial assumption of a template. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy in human visual cortex such that more realistic and idiosyncratic maps could be predicted. We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, our approach is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.

Genetic Architecture Underlying Differential Resting-state Functional Connectivity of Subregions Within the Human Visual Cortex

The human visual cortex is a heterogeneous entity that has multiple subregions showing substantial variability in their functions and connections. We aimed to identify genes associated with resting-state functional connectivity (rsFC) of visual subregions using transcriptome-neuroimaging spatial correlations in discovery and validation datasets. Results showed that rsFC of eight visual subregions were associated with expression measures of eight gene sets, which were specifically expressed in brain tissue and showed the strongest correlations with visual behavioral processes. Moreover, there was a significant divergence in these gene sets and their functional features between medial and lateral visual subregions. Relative to those associated with lateral subregions, more genes associated with medial subregions were found to be enriched for neuropsychiatric diseases and more diverse biological functions and pathways, and to be specifically expressed in multiple types of neurons and immune cells and during the middle and late stages of cortical development. In addition to shared behavioral processes, lateral subregion associated genes were uniquely correlated with high-order cognition. These findings of commonalities and differences in the identified rsFC-related genes and their functional features across visual subregions may improve our understanding of the functional heterogeneity of the visual cortex from the perspective of underlying genetic architecture.

Cohesive communities in dynamic brain functional networks

In large-scale brain network dynamics, brain nodes switching between modules has been found to correlate with cognition. However, how the brain nodes engage in this kind of reorganization of modules is unclear. Based on a functional magnetic resonance imaging dataset, we construct dynamic brain functional networks and investigate nodal module temporal dynamic behavior by applying the multilayer network analysis approach. We reveal three cohesive communities that are groups of brain nodes linked in the same community during brain module dynamic reorganization. We show that the cohesive communities have higher clustering coefficients and lower characteristic path lengths than the controlled community, indicating cohesive communities are the parts of brain networks with high information processing efficiency. The smaller sample entropy of functional connectivity in cohesive communities also proves their property of being more "static" compared with the controlled community in brain dynamics. Specifically, compared with the controlled community, the functional connectivity of cohesive communities is restricted strictly by structure connectivity and shows more similarity to structure connectivity. More importantly, we find that the cohesive communities are stable not only in the resting state but also when processing cognitive tasks. Our results not only show that cohesive communities may be the fundamental community organization to support brain network dynamics but also provide insights into the intrinsic structural relationship between the resting state and task states of the brain.

Data-Driven Parcellation Approaches Based on Functional Connectivity of Visual Cortices in Primary Open-Angle Glaucoma

Purpose

Functional changes have been observed between diseased and healthy subjects, and functional brain atlases derived from healthy populations may fail to reflect functional characteristic of the diseased brain. Therefore the aim of this study was to generate a visual atlas based on functional connectivity from primary open-angle glaucoma (POAG) patients and to prove the applicability of the visual atlas in functional connectivity and network analysis.

Methods

Functional magnetic resonance images were acquired from 36 POAG patients and 20 healthy controls. Two data-driven approaches, K-means and Ward clustering algorithms, were adopted for visual cortices parcellation. Dice coefficient and adjusted Rand index were used to assess reproducibility of the two approaches. Homogeneity index, silhouette coefficient, and network properties were adopted to assess functional validity for the data-driven approaches and frequently used brain atlas. Graph theoretical analysis was adopted to investigate altered network patterns in POAG patients based on data-driven visual atlas.

Results

Parcellation results demonstrated asymmetric patterns between left and right hemispheres in POAG patients compared with healthy controls. In terms of evaluating metrics, K-means performed better than Ward clustering in reproducibility. Data-driven parcellations outperformed frequently used brain atlases in terms of functional homogeneity and network properties. Graph theoretical analysis revealed that atlases generated by data-driven approaches were more conducive in detecting network alterations between POAG patients and healthy controls.

Conclusions

Our findings suggested that POAG patients experienced functional alterations in the visual cortices. Results also highlighted the necessity of data-driven atlases for functional connectivity and functional network analysis of POAG brain.

Whole-brain functional connectivity correlates of obesity phenotypes

Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community.

Sample Entropy Combined with the K-Means Clustering Algorithm Reveals Six Functional Networks of the Brain

Identifying brain regions contained in brain functional networks and functions of brain functional networks is of great significance in understanding the complexity of the human brain. The 160 regions of interest (ROIs) in the human brain determined by the Dosenbach’s template have been divided into six functional networks with different functions. In the present paper, the complexity of the human brain is characterized by the sample entropy (SampEn) of dynamic functional connectivity (FC) which is obtained by analyzing the resting-state functional magnetic resonance imaging (fMRI) data acquired from healthy participants. The 160 ROIs are clustered into six clusters by applying the K-means clustering algorithm to the SampEn of dynamic FC as well as the static FC which is also obtained by analyzing the resting-state fMRI data. The six clusters obtained from the SampEn of dynamic FC and the static FC show very high overlap and consistency ratios with the six functional networks. Furthermore, for four of six clusters, the overlap ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC, and for five of six clusters, the consistency ratios corresponding to the SampEn of dynamic FC are larger than that corresponding to the static FC. The results show that the combination of machine learning methods and the FC obtained using the blood oxygenation level-dependent (BOLD) signals can identify the functional networks of the human brain, and nonlinear dynamic characteristics of the FC are more effective than the static characteristics of the FC in identifying brain functional networks and the complexity of the human brain.

Possible links between the lag structure in visual cortex and visual streams using fMRI

Conventional functional connectivity analysis using functional magnetic resonance imaging (fMRI) measures the correlation of temporally synchronized brain activities between brain regions. Lag structure analysis relaxes the synchronicity constraint of fMRI signals, and thus, this approach might be better at explaining functional connectivity. However, the sources of the lag structure in fMRI are primarily unknown. Here, we applied lag structure analysis to the human visual cortex to identify the possible sources of lag structure. A total of 1,250 fMRI data from two independent databases were considered. We explored the temporal lag patterns between the central and peripheral visual fields in early visual cortex and those in two visual pathways of dorsal and ventral streams. We also compared the lag patterns with effective connectivity obtained with dynamic causal modeling. We found that the lag structure in early visual cortex flows from the central to peripheral visual fields and the order of the lag structure flow was consistent with the order of signal flows in visual pathways. The effective connectivity computed by dynamic causal modeling exhibited similar patterns with the lag structure results. This study suggests that signal flows in visual streams are possible sources of the lag structure in human visual cortex.

Altered Functional Connectivity of the Primary Visual Cortex in Adult Comitant Strabismus: A Resting-State Functional MRI Study

PURPOSE

The aim of this study was to examine the functional connectivity between the primary visual cortex and other cortical areas during rest in normal subjects and patients with comitant strabismus using functional magnetic resonance imaging (fMRI).

METHODS

A prospective, observational study was conducted. Ten patients with comitant exotropia and eleven matched healthy subjects underwent resting-state fMRI with their eyes closed. Resting-state fMRI was performed using a 3.0 T MR scanner. The primary visual cortex was subdivided into anterior and posterior subdivisions. The resting-state functional connectivities within the primary visual cortex and between the primary visual cortex and other cortical areas were calculated for each group and compared between the strabismic and normal control groups. fMRI data were analyzed using Statistical Parametric Mapping software and Analysis of Functional NeuroImages software.

RESULTS

Compared with the normal controls, patients with comitant strabismus had increased functional connectivity between the posterior primary visual cortex and other cortical areas, especially the visual cortex [Brodmann area 19 (BA19)] and other oculomotor regions, such as the frontal eye field (BA6).

CONCLUSIONS

The fMRI results suggest that ongoing and permanent cortical changes occur in patients with comitant strabismus. Disrupted brain functional connectivities are associated with abnormal eye movement and loss of stereopsis. Our study provides a neurological basis for understanding the pathophysiology of comitant strabismus, which may prompt new areas of research to more precisely define this basis and extend these findings to enhance diagnosis and treatment.

Functional connectivity based parcellation of early visual cortices

Human brain can be divided into multiple brain regions based on anatomical and functional properties. Recent studies showed that resting-state connectivity can be utilized for parcellating brain regions and identifying their distinctive roles. In this study, we aimed to parcellate the primary and secondary visual cortices (V1 and V2) into several subregions based on functional connectivity and to examine the functional characteristics of each subregion. We used resting-state data from a research database and also acquired resting-state data with retinotopy results from a local site. The long-range connectivity profile and three different algorithms (i.e., K-means, Gaussian mixture model distribution, and Ward's clustering algorithms) were adopted for the parcellation. We compared the parcellation results within V1 and V2 with the eccentric map in retinotopy. We found that the boundaries between subregions within V1 and V2 were located in the parafovea, indicating that the anterior and posterior subregions within V1 and V2 corresponded to peripheral and central visual field representations, respectively. Next, we computed correlations between each subregion within V1 and V2 and intermediate and high-order regions in ventral and dorsal visual pathways. We found that the anterior subregions of V1 and V2 were strongly associated with regions in the dorsal stream (V3A and inferior parietal gyrus), whereas the posterior subregions of V1 and V2 were highly related to regions in the ventral stream (V4v and inferior temporal gyrus). Our findings suggest that the anterior and posterior subregions of V1 and V2, parcellated based on functional connectivity, may have distinct functional properties.