SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting ...state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation.
The human language effective connectome Rolls, Edmund T.; Deco, Gustavo; Huang, Chu-Chung ...
NeuroImage (Orlando, Fla.),
09/2022, Volume:
258
Journal Article
Peer reviewed
Open access
To advance understanding of brain networks involved in language, the effective connectivity between 26 cortical regions implicated in language by a community analysis and 360 cortical regions was ...measured in 171 humans from the Human Connectome Project, and complemented with functional connectivity and diffusion tractography, all using the HCP multimodal parcellation atlas. A (semantic) network (Group 1) involving inferior cortical regions of the superior temporal sulcus cortex (STS) with the adjacent inferior temporal visual cortex TE1a and temporal pole TG, and the connected parietal PGi region, has effective connectivity with inferior temporal visual cortex (TE) regions; with parietal PFm which also has visual connectivity; with posterior cingulate cortex memory-related regions; with the frontal pole, orbitofrontal cortex, and medial prefrontal cortex; with the dorsolateral prefrontal cortex; and with 44 and 45 for output regions. It is proposed that this system can build in its temporal lobe (STS and TG) and parietal parts (PGi and PGs) semantic representations of objects incorporating especially their visual and reward properties. Another (semantic) network (Group 3) involving superior regions of the superior temporal sulcus cortex and more superior temporal lobe regions including STGa, auditory A5, TPOJ1, the STV and the Peri-Sylvian Language area (PSL) has effective connectivity with auditory areas (A1, A4, A5, Pbelt); with relatively early visual areas involved in motion, e.g., MT and MST, and faces/words (FFC); with somatosensory regions (frontal opercular FOP, insula and parietal PF); with other TPOJ regions; and with the inferior frontal gyrus regions (IFJa and IFSp). It is proposed that this system builds semantic representations specialising in auditory and related facial motion information useful in theory of mind and somatosensory / body image information, with outputs directed not only to regions 44 and 45, but also to premotor 55b and midcingulate premotor cortex. Both semantic networks (Groups 1 and 3) have access to the hippocampal episodic memory system via parahippocampal TF. A third largely frontal network (Group 2) (44, 45, 47l; 55b; the Superior Frontal Language region SFL; and including temporal pole TGv) receives effective connectivity from the two semantic systems, and is implicated in syntax and speech output.
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon ...multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.
In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.
When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.
Parcellation of the orbitofrontal cortex, anterior cingulate cortex, and inferior frontal gyrus based on their functional connectivity with the whole brain in resting state fMRI with 654 participants ...was performed to investigate how these regions with different functions in reward, emotion and their disorders are functionally connected to each other and to the whole brain. The human medial and lateral orbitofrontal cortex, the ventromedial prefrontal cortex, the anterior cingulate cortex, and the right and left inferior frontal gyrus have different functional connectivity with other brain areas and with each other; and each of these regions has several parcels with different functional connectivity with other brain areas. In terms of functional connectivity, the lateral orbitofrontal cortex extends especially on the right into the orbital part of the inferior frontal gyrus and provides connectivity with premotor cortical areas. The orbitofrontal cortex, especially the lateral orbitofrontal cortex, has connectivity not only with language-related areas in the inferior frontal gyrus (Broca's area), but also with the angular and supramarginal gyri. In this context, whereas the connectivity of the orbitofrontal cortex, ventromedial prefrontal cortex, and anterior cingulate cortex is symmetrical, the connectivity of the inferior frontal gyrus triangular and opercular parts is asymmetrical for the right and the left hemispheres. These findings have implications for understanding the neural bases of human emotion and decision-making, and for their disorders including depression.
Abstract
Emerging evidence shows that the coronavirus disease 2019 (COVID-19) pandemic is negatively affecting mental health around the globe. Interventions to alleviate the psychological impact of ...the pandemic are urgently needed. Whether mindfulness practice may protect against the harmful emotional effects of a pandemic crisis remains hitherto unknown. We investigated the influence of mindfulness training on mental health during the COVID-19 outbreak in China. We hypothesized that mindfulness practitioners might manifest less pandemic-related distress, depression, anxiety, and stress than non-practitioners and that more frequent practice would be associated with an improvement in mental health during the pandemic. Therefore, we assessed pandemic-related distress and symptoms of depression, anxiety, and stress, as well as the frequency of meditation practice at the peak of new infections (Feb 4–5;
N
= 673) and three weeks later (Feb 29–30;
N
= 521) in mindfulness practitioners via online questionnaires. Self-reported symptoms were also collected from non-practitioners at peak time only (
N
= 1550). We found lower scores of pandemic-related distress in mindfulness practitioners compared to non-practitioners. In general, older participants showed fewer symptoms of depression and anxiety. In younger practitioners, pandemic-related distress decreased from peak to follow-up. Importantly, increased mindfulness training during the preceding two weeks was associated with lower scores of depression and anxiety at both assessments. Likewise, practice frequency predicted individual improvement in scores of depression, anxiety, and stress at follow-up. Our results indicate that mindfulness meditation might be a viable low-cost intervention to mitigate the psychological impact of the COVID-19 crisis and future pandemics.
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based ...algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method.
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a ...system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
Abstract
The orbitofrontal cortex extends into the laterally adjacent inferior frontal gyrus. We analyzed how voxel-level functional connectivity of the inferior frontal gyrus and orbitofrontal ...cortex is related to depression in 282 people with major depressive disorder (125 were unmedicated) and 254 controls, using FDR correction P < 0.05 for pairs of voxels. In the unmedicated group, higher functional connectivity was found of the right inferior frontal gyrus with voxels in the lateral and medial orbitofrontal cortex, cingulate cortex, temporal lobe, angular gyrus, precuneus, hippocampus and frontal gyri. In medicated patients, these functional connectivities were lower and toward those in controls. Functional connectivities between the lateral orbitofrontal cortex and the precuneus, posterior cingulate cortex, inferior frontal gyrus, ventromedial prefrontal cortex and the angular and middle frontal gyri were higher in unmedicated patients, and closer to controls in medicated patients. Medial orbitofrontal cortex voxels had lower functional connectivity with temporal cortex areas, the parahippocampal gyrus and fusiform gyrus, and medication did not result in these being closer to controls. These findings are consistent with the hypothesis that the orbitofrontal cortex is involved in depression, and can influence mood and behavior via the right inferior frontal gyrus, which projects to premotor cortical areas.
The majority of tissue-specific environmental quality standards (EQSs) considering metal tolerance are prior to the chemical-specific EQSs in aquatic organisms. However, metal baseline levels in ...marine organisms were very scarce. We explored the correlation between Hg, Cd, Pb, Cu, and Zn concentrations in water or sediments and those metal concentrations in marine organisms (crustacean, mollusc, and fish) by generalized additive models (GAMs) and executed a meta-analysis of Hg, Cd, Pb, Cu, and Zn contents in those three organisms by implementing cumulative frequency distribution analysis of individual metal distribution in a heavy metal-contaminated semi-enclosed Bay, China. Results showed that the average contents of Hg, Cd, Pb, Cu, and Zn were 0.042±0.01, 0.38±0.22, 1.72±0.65, 3.61±1.01, and 16.08±6.33 μg/L in water; 0.064±0.02, 0.42±0.04, 20.54±7.76, 28.97±3.90, and 96.74±35.11 μg/g dw in sediment; and 0.0049±0.0028, 0.52±0.28, 0.24±0.15, 11.05±6.95, and 21.12±4.47 μg/g dw in crustacean, 0.015±0.0087, 0.24±0.17, 0.08±0.02, 0.37±0.35, and 10.62±6.79 μg/g dw in mollusc; and 0.0038±0.0028, 0.065±0.05, 0.32±0.19, 2.01±0.59, and 16.04±4.97 μg/g dw in fish. The mercury content in mollusc presented a negative correlation with mercury content in sediment, while the content of other metals (Cd, Pb, Cu, and Zn) in organisms showed positive correlations with the content of those metals in water or sediment. We further obtained tissue-baseline-C5% in crustacean, mollusc, and fish which were 1.191, 3.341, and 0.014 μg/g dw for Cu; 0.013, 0.072, and 0.033 μg/g dw for Cd, 0.015, 0.027, and 0.052 μg/g dw for Pb; 9.515, 14.422, and 0.056 μg/g dw for Zn; and 0.0009, 0.004, and 0.0035 μg/g dw for Hg, respectively. However, there were no obvious relationships of the 4d-NOEC in laboratory toxicity tests with C5%, as well as C50% and 4d-LC50 or tolerance index
a
for Cu, Cd, Pb, Zn, and Hg in organisms. Our results pointed out the controversy of laboratory sensitive species toxicity results for deriving chemical-specific EQSs with field studies. We advocated to set up the metal concentration baselines in aquatic organisms and further served the tissue-specific EQSs, which are essential basis for geochemical recordings, bio-monitoring, and semi-enclosed bay management in the world.
Graphical abstract
The first brain-wide voxel-level resting state functional connectivity neuroimaging analysis of depression is reported, with 421 patients with major depressive disorder and 488 control subjects. ...Resting state functional connectivity between different voxels reflects correlations of activity between those voxels and is a fundamental tool in helping to understand the brain regions with altered connectivity and function in depression. One major circuit with altered functional connectivity involved the medial orbitofrontal cortex Brodmann area 13, which is implicated in reward, and which had reduced functional connectivity in depression with memory systems in the parahippocampal gyrus and medial temporal lobe, especially involving the perirhinal cortex Brodmann area 36 and entorhinal cortex Brodmann area 28. The Hamilton Depression Rating Scale scores were correlated with weakened functional connectivity of the medial orbitofrontal cortex Brodmann area 13. Thus in depression there is decreased reward-related and memory system functional connectivity, and this is related to the depressed symptoms. The lateral orbitofrontal cortex Brodmann area 47/12, involved in non-reward and punishing events, did not have this reduced functional connectivity with memory systems. Second, the lateral orbitofrontal cortex Brodmann area 47/12 had increased functional connectivity with the precuneus, the angular gyrus, and the temporal visual cortex Brodmann area 21. This enhanced functional connectivity of the non-reward/punishment system (Brodmann area 47/12) with the precuneus (involved in the sense of self and agency), and the angular gyrus (involved in language) is thus related to the explicit affectively negative sense of the self, and of self-esteem, in depression. A comparison of the functional connectivity in 185 depressed patients not receiving medication and 182 patients receiving medication showed that the functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 with these three brain areas was lower in the medicated than the unmedicated patients. This is consistent with the hypothesis that the increased functional connectivity of the lateral orbitofrontal cortex Brodmann area 47/12 is related to depression. Relating the changes in cortical connectivity to our understanding of the functions of different parts of the orbitofrontal cortex in emotion helps to provide new insight into the brain changes related to depression.
PDF
