Functional Connectivity Methods Research Articles

Functional connectivity in procrastination and emotion regulation

Procrastination, an irrational delay of intended action, leads to numerous adverse effects in many life domains, such as low academic performance, poor mental health, and financial distress. Previous studies have revealed a substantial negative correlation between emotional regulation and procrastination. However, the neural basis for the association between emotion regulation and procrastination remains unclear. Therefore, we employed the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the neural substrates underlying how emotion regulation is responsible for procrastination (N = 243). In line with our hypothesis, the results showed a significant negative correlation between emotion regulation ability and procrastination. Additionally, the VBM analysis showed that emotion regulation ability was positively correlated with gray matter (GM) volumes in the right dorsal-lateral prefrontal cortex (dlPFC). The mediation analysis revealed that emotion regulation ability mediated the relationship between the GM volumes of the right dlPFC and procrastination. Furthermore, the RSFC results indicated that right dlPFC-left insula functional connectivity was positively associated with emotion regulation ability. Emotion regulation ability further mediated the relationship between the right dlPFC-left insula functional connectivity and procrastination. The current findings suggest that the neural pathway related to cognitive control over aversive emotion may be responsible for the close relationship between emotion regulation and procrastination, which provides a novel perspective for explaining the tight association between emotion regulation and procrastination.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network.

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits.

Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.

Higher brain functions require flexible integration of information across widely distributed brain regions depending on the task context. Resting-state functional magnetic resonance imaging (fMRI) has provided substantial insight into large-scale intrinsic brain network organisation, yet the principles of rapid context-dependent reconfiguration of that intrinsic network organisation are much less understood. A major challenge for task connectome mapping is the absence of a gold standard for deriving whole-brain task-modulated functional connectivity matrices. Here, we perform biophysically realistic simulations to control the ground-truth task-modulated functional connectivity over a wide range of experimental settings. We reveal the best-performing methods for different types of task designs and their fundamental limitations. Importantly, we demonstrate that rapid (100 ms) modulations of oscillatory neuronal synchronisation can be recovered from sluggish haemodynamic fluctuations even at typically low fMRI temporal resolution (2 s). Finally, we provide practical recommendations on task design and statistical analysis to foster task connectome mapping.

Functional connectivity of the pediatric brain.

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders. The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents. Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders. Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Resting-state functional magnetic resonance imaging: features of statistical processing of ROI-analysis data

BACKGROUND: In many works, to study intra- and inter-network connections, a method for constructing networks is used — ROI-analysis (region of interest analysis). The conflicting results obtained when assessing brain connectivity using ROI-analysis can be explained by methodological differences associated with the statistical processing of fMRI data. In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data.
 AIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches.
 MATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. Statistical data processing was performed using methods: functional network connectivity, randomization/permutation spatial pairwise clustering statistics, and threshold-free cluster enhancement.
 RESULTS: The number of connections between the structures of brain networks recorded using the method of functional network connectivity is 280, spatial pairwise clustering — 186, threshold-free cluster enhancement — 182. An interesting fact is that negative connections were identified only when using parametric statistics.
 CONCLUSION: A comparative assessment of methods for statistical processing of fMRI data during ROI-analysis was carried out. The functional network connectivity method based on multivariate parametric statistics turned out to be more informative than randomization/permutation spatial pairwise clustering statistics and the method based on threshold-free cluster enhancement. Despite the growing popularity in recent years of resting-state fMRI in the study of functional activity and connectivity of the brain, there are no standardized algorithms for constructing networks of the brain.

Altered functional connectivity strength of primary visual cortex in subjects with thyroid-associated ophthalmopathy.

Our objective was to explore the disparities in the intrinsic functional connectivity (FC) patterns of primary visual cortex (V1) between patients with thyroid-associated ophthalmopathy (TAO) and healthy controls (HCs) utilizing resting-state functional MRI. Twenty-one patients with TAO (14 males and 7 females; mean age: 54.17 ± 4.83 years) and 21 well-matched HCs (14 males and 7 females; mean age: 55.17 ± 5.37 years) underwent functional MRI scans in the resting-state. We assessed modifications in the intrinsic FC patterns of the V1 in TAO patients using the FC method. Subsequently, the identified alterations in FC regions in the analysis were selected as classification features to distinguish TAO patients from HCs through the support vector machine (SVM) method. The results indicated that, in comparison to HCs, patients with TAO exhibited notably reduced FC values between the left V1 and the bilateral calcarine (CAL), lingual gyrus (LING) and superior occipital gyrus, as well as between the right V1 and the bilateral CAL/LING and the right cerebellum. Furthermore, the SVM classification model based on FC maps demonstrated effective performance in distinguishing TAO patients from HCs, achieving an accuracy of 61.9% using the FC of the left V1 and 64.29% using the FC of the right V1. Our study revealed that patients with TAO manifested disruptions in FC between the V1 and higher visual regions during rest. This might indicate that TAO patients could present with impaired top-down modulations, visual imagery and vision-motor function. These insights could be valuable in understanding the underlying neurobiological mechanisms of vision impairment in individuals with TAO.

MRI brain structural and functional networks changes in Parkinson disease with REM sleep behavior disorders.

Rapid eye movement sleep behavior disorder (RBD) is common in individuals with Parkinson's disease (PD). In spite of that, the precise mechanism underlying the pathophysiology of RBD among PD remains unclear. The aim of the present study was to analyze gray matter volumes (GMVs) as well as the changes of functional connectivity (FC) among PD patients with RBD (PD-RBD) by employing a combination of voxel-based morphometry (VBM) and FC methods. A total of 65 PD patients and 21 healthy control (HC) subjects were included in this study. VBM analyses were performed on all subjects. Subsequently, regions with significant different GMVs between PD patients with and without RBD (PD-nRBD) were selected for further analysis of FC. Correlations between altered GMVs and FC values with RBD scores were also investigated. Additionally, receiver operating characteristic (ROC) curves were employed for the evaluation of the predictive value of GMVs and FC in identifying RBD in PD. PD-RBD patients exhibited lower GMVs in the left middle temporal gyrus (MTG) and bilateral cuneus. Furthermore, we observed higher FC between the left MTG and the right postcentral gyrus (PoCG), as well as lower FC between the bilateral cuneus (CUN) and the right middle frontal gyrus (MFG) among PD-RBD patients in contrast with PD-nRBD patients. Moreover, the GMVs of MTG (extending to the right PoCG) was positively correlated with RBD severity [as measured by REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score]. Conversely, the FC value between the bilateral CUN and the right MTG in PD-RBD patients was negatively correlated with RBDSQ score. This study revealed the presence replace with GMV and FC changes among PD-RBD patients, which were closely linked to the severity of RBD symptoms. Furthermore, the combination of basic clinical characteristics, GMVs and FC values effectively predicted RBD for individuals with PD.

The effective connectivity analysis of fMRI based on asymmetric detection of transfer brain entropy.

It is important to explore causal relationships in functional magnetic resonance imaging study. However, the traditional effective connectivity analysis method is easy to produce false causality, and the detection accuracy needs to be improved. In this paper, we introduce a novel functional magnetic resonance imaging effective connectivity method based on the asymmetry detection of transfer entropy, which quantifies the disparity in predictive information between forward and backward time, subsequently normalizing this disparity to establish a more precise criterion for detecting causal relationships while concurrently reducing computational complexity. Then, we evaluate the effectiveness of this method on the simulated data with different level of nonlinearity, and the results demonstrated that the proposed method outperforms others methods on the detection of both linear and nonlinear causal relationships, including Granger Causality, Partial Granger Causality, Kernel Granger Causality, Copula Granger Causality, and traditional transfer entropy. Furthermore, we applied it to study the effective connectivity of brain functional activities in seafarers. The results showed that there are significantly different causal relationships between different brain regions in seafarers compared with non-seafarers, such as Temporal lobe related to sound and auditory information processing, Hippocampus related to spatial navigation, Precuneus related to emotion processing as well as Supp_Motor_Area associated with motor control and coordination, which reflects the occupational specificity of brain function of seafarers.

The neural substrates responsible for punishment sensitivity association with procrastination: Left putamen connectivity with left middle temporal gyrus

Procrastination has adverse consequences across cultural contexts. Behavioral research found a positive correlation between punishment sensitivity and procrastination. However, little is known about the neural substrates underlying the association between them. We employed voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to address this issue with two independent samples. In Sample 1, behavioral results found that punishment sensitivity was positively related to procrastination. The VBM analysis showed that punishment sensitivity was negatively correlated with gray matter volume in left putamen. Subsequently, the RSFC results revealed that left putamen - left middle temporal gyrus (MTG) connectivity was positively associated with punishment sensitivity. More crucially, mediation analysis indicated that left putamen - left MTG connectivity mediated the relationship between punishment sensitivity and procrastination. The aforementioned results were validated in Sample 2. Altogether, left putamen - left MTG connectivity might be the neural signature of the association between punishment sensitivity and procrastination.

Early prediction of dementia using fMRI data with a graph convolutional network approach

Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA.

Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD and provide subjective features as decision references to assist specialists and physicians. Many studies have been devoted to investigating the neural dynamics of the brain through resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance imaging (fMRI). The present study used coherence, which is one of the functional connectivity (FC) methods, to analyze the neural patterns of children and adolescents (8-16 years old) under CPT and continuous auditory test of attention (CATA) task. In the meantime, electroencephalography (EEG) oscillations were recorded by a wireless brain-computer interface (BCI). 72 children were enrolled, of which 53 participants were diagnosed with ADHD and 19 presented to be typical developing (TD). The experimental results exhibited a higher difference in alpha and theta bands between the TD group and the ADHD group. While the differences between the TD group and the ADHD group in all four frequency domains were greater than under CPT conditions. Statistically significant differences ( [Formula: see text]) were observed between the ADHD and TD groups in the alpha rhythm during the CATA task in the short-range of coherence. For the temporal lobe FC during the CATA task, the TD group exhibited statistically significantly FC ( [Formula: see text]) in the alpha rhythm compared to the ADHD group. These findings offering new possibilities for more techniques and diagnostic methods in finding more ADHD features. The differences in alpha and beta frequencies were more pronounced in the ADHD group during the CPT task compared to the CATA task. Additionally, the disparities in brain activity were more evident across delta, theta, alpha and beta frequency domains when the task given was a CATA as opposed to a CPT. The findings presented the underlying mechanisms of the FC differences between children and adolescents with ADHD. Moreover, these findings should extend to use machine learning approaches to assist the ADHD classification and diagnosis.

Static and dynamic fMRI-derived functional connectomes represent largely similar information.

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Toward stability of dynamic FC estimates in neuroimaging and electrophysiology: Solutions and limits.

Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.

Higher-order sensorimotor circuit of the whole-brain functional network involved in pruritus regulation in atopic dermatitis.

Little is known about the neural mechanisms underlying pruritus regulation in Atopic dermatitis (AD). To investigate the functional changes of the resting-state whole brain network of AD participants and the mechanisms by which they were involved in pruritus regulation. Based on the functional magnetic resonance imaging data from 19 AD participants and 37 healthy controls (HC), a graph-theoretical measure of degree centrality (DC) conjoined with a voxel-level seed-based functional connectivity (FC) method was used to identify abnormal higher-order nodes and the functionally relevant circuit in AD participants compared to healthy controls (HC). Of 64 participants screened, 19 AD participants (12M/7F, median [IQR] age, 27 [14] years) and 36 HCs (13M/23F, median [IQR] age, 20 [1] years) were enrolled. DC values of the left superior frontal gyrus (LSFG) increased in AD participants and exhibited a negative correlation with the SCORAD score (r = -0.561, p = 0.012) compared with HC. In the FC analysis with LSFG as the seed, FC values of several sensory and motor regions increased in AD participants, highly overlapping with the anatomical distribution of the inferior fronto-occipital fascicle (IFOF). AD participants with severe pruritus exhibited lower levels of DC (T = -2.316, p = 0.033) and FC between the LSFG and left insula (T = -2.203, p = 0.042) than those with mild-to- moderate pruritus. LSFG was involved in pruritus regulation in AD by forming a high-order sensorimotor circuit through the IFOF, a white matter fascicle that proved to provide multimodal integration in motor control and sensory information processing. These results offer more mechanism-guided treatment targets for severe pruritus in AD.

Characteristic patterns of functional connectivity-mediated cerebral small vessel disease-related cognitive impairment and depression.

Cerebral small vessel disease is common in most individuals aged 60years or older, and it is associated with cognitive dysfunction, depression, anxiety disorder, and mobility problems. Currently, many cerebral small vessel disease patients have both cognitive impairment and depressive symptoms, but the relationship between the 2 is unclear. The present research combined static and dynamic functional network connectivity methods to explore the patterns of functional networks in cerebral small vessel disease individuals with cognitive impairment and depression (cerebral small vessel disease-mild cognitive impairment with depression) and their relationship. We found specific functional network patterns in the cerebral small vessel disease-mild cognitive impairment with depression individuals (P < 0.05). The cerebral small vessel disease individuals with depression exhibited unstable dynamic functional network connectivity states (transitions likelihood: P = 0.040). In addition, we found that the connections within the lateral visual network between the sensorimotor network and ventral attention network could mediate white matter hyperintensity-related cognitive impairment (indirect effect: 0.064; 95% CI: 0.003, 0.170) and depression (indirect effect: -0.415; 95% CI: -1.080, -0.011). Cognitive function can negatively regulate white matter hyperintensity-related depression. These findings elucidate the association between cognitive impairment and depression and provide new insights into the underlying mechanism of cerebral small vessel disease-related cognitive dysfunction and depression.

Alterations of brain activity in patients with alcohol use disorder: a resting-state fMRI study

BackgroundAlcohol use disorder (AUD) has a negative impact on one’s health and wastes a lot of societal resources since it damages one’s brain tissue. Yet the knowledge of the neural mechanisms underlying alcohol addiction still remains limited. This study aims to investigate the neural mechanisms underlying alcohol addiction by using voxel-wise binarized degree centrality (DC), weighted DC and functional connectivity (FC) methods to analyze brain network activity in individuals with AUD.MethodsThirty-three AUD patients and 29 healthy controls (HC) participated in this study. Binarized and weighted DC approach coupled with a second seed-based FC algorithm was used to assess the abnormal intrinsic hub features in AUD. We also examined the correlation between changes in functional network nodes and the severity of alcohol dependence.ResultsThirty AUD patients and 26 HC were retained after head motion correction. The spatial distribution maps of the binarized DC and weighted DC for the AUD and HC groups were roughly similar. In comparison to HC, the AUD group had decreased binarized DC and decreased weighted DC in the left precentral gyrus (PreCG) and the left inferior parietal lobule (IPL). Significantly different brain regions in the DC analysis were defined as seed points in the FC analysis. Compared with HC, changes in FC within the right inferior temporal gyrus (ITG), right middle temporal gyrus (MTG), left dorsolateral superior frontal gyrus (SFGdor), bilateral IPL, left precuneus (PCUN), left lingual gyrus (LING), right cerebellum_crus1/ITG/inferior occipital gyrus (IOG) and right superior parietal gyrus (SPG) were observed. The correlation analysis revealed that FC of right MTG-right PreCG was negatively correlated with MAST scores, and FC of right IPL-left IPL was positively correlated with ADS scores.ConclusionsAlcohol use disorder is associated with aberrant regional activities in multiple brain areas. Binarized DC, weighted DC and FC analyses may be useful biological indicators for the detection of regional brain activities in patients with AUD. Intergroup differences in FC have also been observed in AUD patients, and these variations were connected to the severity of the symptoms. The AUD patients with lower FC value of the right IPL - left IPL has a lighter dependence on alcohol. This difference in symptom severity may be a compensation for cognitive impairment, indicating a difference in pathological pathways. Future AUD research will now have a fresh path thanks to these discoveries.

Edge time series components of functional connectivity and cognitive function in Alzheimer’s disease

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer’s disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer’s Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer’s disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Distracted driving recognition based on functional connectivity analysis between physiological signals and perinasal perspiration index

Automatic detection of distracted driving is essential to ensure safety of drivers. In this paper, a novel set of features were extracted from thermal and physiological signals in order to detect and recognize distraction of drivers. Thermal video data which measured the temperature of different areas of the face, heart rate, breathing rate and behavioural signals were used while various types of distractions including cognitive, emotional and sensory-motor were applied to the subjects. The proposed discriminator features were extracted by different functional connectivity methods between the perinasal perspiration extracted from thermal images of the face and physiological variables of heart rate and breathing rate. After feature extraction, binary classification methods were applied to detect the distractions. The results showed that using functional connectivity features significantly increased the accuracy of distraction detection system (up to 99.16% with a highly acceptable F1 score of 0.99). Hence, the proposed model significantly improved (p-value<0.001) the detection of driver’s distraction compared to previous studies. Functional connectivity between perinasal perspiration and breathing signals was the most informative feature for distraction detection purpose. This feature set explained almost all modulations in physiological changes by distractions. For the first time, we designed a system capable of recognizing types of distractions including sensory-motor, cognitive, emotional distractions and also normal state. We used the same feature set to recognize different types of distractions by using three-class and four-class classifiers. The suggested methods distinguished three types of distractions with the best accuracy of 81.94% related to cognitive, sensory-motor distraction and no-distraction states. We also tried to discriminate two types of cognitive distractions, analytic and mathematical distractions. The recognition system classified two types of cognitive distractions with an accuracy of 91.78%. The results suggest that there is important and complementary information in the connectivity between facial temperature signals and physiological variables for distraction detection and recognition.