Resting-state Functional Connections Research Articles

Exploring the Neurocognitive Correlates of Suicidal Ideation in Major Depressive Disorder: The Role of Frontoparietal and Default Mode Networks

Suicidal ideation (SI) is a common symptom of major depressive disorder (MDD), often accompanied by cognitive alterations and emotional dysregulation. However, it is unclear whether cognitive dysfunction in patients with MDD is related to the presence or absence of SI and impaired connectivity within or between large-scale neurocognitive networks. Previous studies have shown that the frontoparietal network (FPN) and default mode network (DMN) are critical for cognitive control and emotional regulation. Participants were 51 MDD patients with suicidal ideation (MDDSI), 52 MDD patients without suicidal ideation (MDDNSI), and 55 healthy controls (HC). Using areas located within FPN and DMN networks as regions of interest (ROIs), we compared the cognitive performance of the three groups and the strength of the resting state functional connections (RSFC) within and between the FPN and DMN networks. Additionally, we examined the correlation between the strength of FC within the FPN and cognitive function in the SI group. Furthermore, network-based statistics (NBS) were used to correct for the strength of FPN and DMN functional connections. The study identified significant cognitive deficits in MDD patients. Reduced strength of FC was observed within the FPN and DMN networks in the SI group compared to the NSI group. In the SI group, the strength of FC within the FPN network was positively correlated with attention/vigilance. These insights underscore the critical roles of the FPN and DMN in the suicidal ideation, shedding light on the cognitively relevant neurobiological characteristics of MDDSI, providing new insights into the neural mechanisms of MDDSI.URL: https://www.chictr.org.cn/bin/project/edit?pid=131537Registration number: ChiCTR2100049646

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI). In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD. The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age. Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study.

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Influence of exercise on pain is associated with resting-state functional connections: A cross-sectional functional brain imaging study

Exercise is associated with lower prevalence and severity of pain, and is widely recommended for pain management. However, the mechanisms the exercise effect on pain remain unclear. In this study, we examined the association of exercise with pain and aimed to identify its neurobiological mediators. We utilized a baseline data of a clinical trial for people with low back pain. Participants reported pain intensity and exercise habit, as well as pain-related psychological and emotional assessments. We also obtained brain imaging data using a resting-state functional MRI and performed mediation analyses to identify brain regions mediating the exercise effect on pain. Forty-five people with low back pain (mean pain intensity = 59.6 and mean duration = 9.9 weeks) were included in this study. Participants with an exercise habit (n = 29) showed significant less pain compared to those without an exercise habit (n = 16). Mediation analysis using resting-state functional connectivity identified the left thalamus, right amygdala, and medial prefrontal cortex as statistical mediators of the exercise effect on pain (indirect effect = −0.460, 95% confidence interval = −0.767 to −0.153). In conclusion, our findings suggest that brain function of the specific regions is probably a neuro-mechanism of exercise alleviating pain.

Altered functional connectivity of the thalamus in primary angle-closure glaucoma patients: A resting-state fMRI study.

Background and objectivesGlaucoma is one of the leading irreversible causes of blindness worldwide, and previous studies have shown that there is abnormal functional connectivity (FC) in the visual cortex of glaucoma patients. The thalamus is a relay nucleus for visual signals; however, it is not yet clear how the FC of the thalamus is altered in glaucoma. This study investigated the alterations in thalamic FC in patients with primary angle-closure glaucoma (PACG) by using resting-state functional MRI (rs-fMRI). We hypothesized that PACG patients have abnormal FC between the thalamus and visual as well as extravisual brain regions.MethodsClinically confirmed PACG patients and age- and gender-matched healthy controls (HCs) were evaluated by T1 anatomical and functional MRI on a 3 T scanner. Thirty-four PACG patients and 33 HCs were included in the rs-fMRI analysis. All PACG patients underwent complete ophthalmological examinations; included retinal nerve fiber layer thickness (RNFLT), intraocular pressure (IOP), average cup-to-disc ratio (A-C/D), and vertical cup-to-disc ratio (V-C/D). After the MRI data were preprocessed, the bilateral thalamus was chosen as the seed point; and the differences in resting-state FC between groups were evaluated. The brain regions that significantly differed between PACG patients and HCs were identified, and the correlations were then evaluated between the FC coefficients of these regions and clinical variables.ResultsCompared with the HCs, the PACG patients showed decreased FC between the bilateral thalamus and right transverse temporal gyrus, between the bilateral thalamus and left anterior cingulate cortex, and between the left thalamus and left insula. Concurrently, increased FC was found between the bilateral thalamus and left superior frontal gyrus in PACG patients. The FC between the bilateral thalamus and left superior frontal gyrus was positively correlated with RNFLT and negatively correlated with the A-C/D and V-C/D. The FC between the left thalamus and left insula was negatively correlated with IOP.ConclusionExtensive abnormal resting-state functional connections between the thalamus and visual and extravisual brain areas were found in PACG patients, and there were certain correlations with clinical variables, suggesting that abnormal thalamic FC plays an important role in the progression of PACG.

Abnormal topological parameters in the default mode network in patients with impaired cognition undergoing maintenance hemodialysis.

ObjectiveThe role of the default mode network (DMN) in the cognitive impairment experienced by patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis (MHD) remains unknown. This study tested the hypothesis that the topological architecture of the DMN plays a key role in ESRD-related cognitive impairment.MethodsFor this study, 43 ERSD patients receiving MHD and 41 healthy control (HC) volunteers matched for gender, age and education underwent resting-state functional magnetic resonance imaging examinations. DMN architecture was depicted by 20 selected DMN subregions. Graph theory approaches were applied to investigate multiple topological parameters within the DMN in resting state at the global, local and edge levels.ResultsGlobally, the MHD group exhibited topological irregularities as indicated by reduced values for the clustering coeffcient (Cp), normalized Cp (γ), world-index (σ), and local effciency (Eloc) compared with the HC group. Locally, the MHD group showed greater nodal betweenness in the left retrosplenial cortex (RC) compared with the HC group. At the edge level, the MHD group exhibited disconnected resting-state functional connections (RSFCs) in the medial temporal lobe (MTL) subsystem including the ventral medial prefrontal cortex (VMPC)–left posterior inferior parietal lobule, VMPC–right parahippocampal cortex (PC), and right RC–left PC RSFCs. Additionally, the VMPC–right PC RSFC was positively correlated with the Digit Span Test score and Eloc, and the right RC–left PC RSFC was positively correlated with the Montreal Cognitive Assessment score and Eloc in the MHD group.ConclusionsESRD patients undergoing MHD showed local inefficiency, abnormal nodal centralities, and hypoconnectivity within the DMN, implying that the functional differentiation and local information transmission efficiency of the DMN are disturbed in ESRD. The disconnected RSFCs in the MTL subsystem likely facilitated topological reconfiguration in the DMN of ESRD patients, leading to impairments of multidomain neurocognition including memory and emotion regulation.

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

Precise functional connections between the dorsal anterior cingulate cortex and areas recruited for physical inference.

Recent work has identified brain areas that are engaged when people predict how the physical behaviour of the world will unfold—an ability termed intuitive physics. Among the many unanswered questions about the neural mechanisms of intuitive physics is where the key inputs come from: Which brain regions connect up with intuitive physics processes to regulate when and how they are engaged in service of our goals? In the present work, we targeted the dorsal anterior cingulate cortex (dACC) for study based on characteristics that make it well‐positioned to regulate intuitive physics processes. The dACC is richly interconnected with frontoparietal regions and is implicated in mapping contexts to actions, a process that would benefit from physical predictions to indicate which action(s) would produce the desired physical outcomes. We collected resting state functional magnetic resonance imaging (MRI) data in 17 participants and used independent task‐related runs to find the pattern of activity during a physical inference task in each individual participant. We found that the strongest resting state functional connections of the dACC not only aligned well with physical inference‐related activity at the group level, it also mirrored individual differences in the positioning of physics‐related activity across participants. Our results suggest that the dACC might be a key structure for regulating the engagement of intuitive physics processes in the brain.

Abnormality of Functional Connections in the Resting State Brains of Schizophrenics.

To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer’s disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders.

Youth Screen Media Activity Patterns and Associations with Behavioral Developmental Measures and Resting-State Brain Functional Connections

Youth Screen Media Activity Patterns and Associations with Behavioral Developmental Measures and Resting-State Brain Functional Connections

Abnormal characterization of dynamic functional connectivity in Alzheimer's disease.

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer's disease (AD) or amnestic mild cognitive impairment (aMCI). However, most studies examined traditional resting state functional connections, ignoring the instantaneous connection mode of the whole brain. In this case-control study, we used a new method called dynamic functional connectivity (DFC) to look for abnormalities in patients with AD and aMCI. We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant, and then used a support vector machine to classify AD patients and normal controls. Finally, we highlighted brain regions and brain networks that made the largest contributions to the classification. We found differences in dynamic function connectivity strength in the left precuneus, default mode network, and dorsal attention network among normal controls, aMCI patients, and AD patients. These abnormalities are potential imaging markers for the early diagnosis of AD.

P-TS002. Non-specific facilitatory after-effects of transcranial random noise stimulation on bilateral anterior temporal cortex

Introduction. Previous studies demonstrated that transcranial random noise stimulation (tRNS), as a noninvasive electrical stimulation technique, significantly increases task performances or cortical excitability as it is directly applied to modulate those task-related brain areas. Here, we aimed to investigate whether the facilitatory effect induced by the tRNS on specific task performances could stem from the stimulations of non-task-related brain areas. Methods. Three healthy subject groups were involved in this study, one received a 15-min tRNS at an intensity of 1 mA over bilateral anterior temporal cortex, one at 2 mA, and the third one was the sham-control group. We used EEG under both the resting and active (watching a video) states, as well as resting-state functional MRI to measure changes on neuronal activity and functional connectivity after tRNS stimulation. Behavioral effects were tested by using cortical silent period (CSP) for examining cortical excitability and Corsi block tapping (CBT) tests for assessing visuo-spatial working memory. Results. We found that, as compared to the groups of 1 mA and the sham-control, 2-mA tRNS significantly lengthened the CSP and increased CBT performance. It also tended to increase global brain rate (mean frequencies of EEG oscillations), indicating that the vigilance of subjects might be lifted or sustained by tRNS. We also found a significant transient increase in the strength of resting-state functional connections globally between many different brain areas, especially for connections between temporal and parietal cortices, after tRNS. Conclusion. Based on our findings, it is concluded that tRNS over non-task-related brain areas may enhance certain task performances or influence cortical excitability through the increase in vigilance caused by the global enhancement on relevant functional connectivity.

Functional Disconnection of the Angular Gyrus Related to Cognitive Impairment in Patients With Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is related to a variety of cognitive impairments that may even progress to dementia. Studies have found the angular gyrus (AG) is a cross-modal integration hub that is involved in a variety of cognitive processes. However, few studies have focused on the patterns of resting-state functional connections (rsFCs) of the AG in patients with T2DM. This study explored the functional connection (FC) between the AG and the whole brain and the relationship between the FC and clinical/cognitive variables in patients with T2DM. 44 patients with T2DM and 43 sex-, age-, and education-matched healthy controls underwent resting-state fMRI and received neuropsychological assessments. Compared with the control group, the T2DM group showed abnormal rsFCs between the AG and multiple brain regions. The FC between the left AG and the left medial temporal lobe in the T2DM group was positively correlated with scores on the Montreal Cognitive Assessment, after a Bonferroni correction (r = 0.40, P = 0.009). Collectively, patients with T2DM have abnormal FCs between the AG and extensive brain regions that may be related to various cognitive processes.

Predicting Response to Group Cognitive Behavioral Therapy in Asthma by a Small Number of Abnormal Resting-State Functional Connections.

Group cognitive behavioral therapy (GCBT) is a successful psychotherapy for asthma. However, response varies considerably among individuals, and identifying biomarkers of GCBT has been challenging. Thus, the aim of this study was to predict an individual’s potential response by using machine learning algorithms and functional connectivity (FC) and to improve the personalized treatment of GCBT. We use the lasso method to make the feature selection in the functional connections between brain regions, and we utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and pre-GCBT patients (size = 20), pre-GCBT patients (size = 10) and post-GCBT patients (size = 10), and post-GCBT patients (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls and pre- and post-GCBT patients. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in post-GCBT patients. At last, linear support vector regression was applied to predict the therapeutic effect of GCBT. After feature selection and significant analysis, five discriminated FC regarding neuroimaging biomarkers of GCBT were discovered, which are also correlated with clinical symptoms. Using these discriminated functional connections, we could accurately classify the patients before and after GCBT (classification accuracy, 80%) and predict the therapeutic effect of GCBT in asthma (predicted accuracy, 67.8%). The findings in this study would provide a novel sight toward GCBT response prediction and further confirm neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT.Clinical Trial RegistrationThe brain mechanisms of group cognitive behavioral therapy to improve the symptoms of asthma (Registration number: Chi-CTR-15007442, http://www.chictr.org.cn/index.aspx).

Scotopic Vision Is Selectively Processed in Thick-Type Columns in Human Extrastriate Cortex.

In humans, visual stimuli can be perceived across an enormous range of light levels. Evidence suggests that different neural mechanisms process different subdivisions of this range. For instance, in the retina, stimuli presented at very low (scotopic) light levels activate rod photoreceptors, whereas cone photoreceptors are activated relatively more at higher (photopic) light levels. Similarly, different retinal ganglion cells are activated by scotopic versus photopic stimuli. However, in the brain, it remains unknown whether scotopic versus photopic information is: 1) processed in distinct channels, or 2) neurally merged. Using high-resolution functional magnetic resonance imaging at 7 T, we confirmed the first hypothesis. We first localized thick versus thin-type columns within areas V2, V3, and V4, based on photopic selectivity to motion versus color, respectively. Next, we found that scotopic stimuli selectively activated thick- (compared to thin-) type columns in V2 and V3 (in measurements of both overlap and amplitude) and V4 (based on overlap). Finally, we found stronger resting-state functional connections between scotopically dominated area MT with thick- (compared to thin-) type columns in areas V2, V3, and V4. We conclude that scotopic stimuli are processed in partially segregated parallel streams, emphasizing magnocellular influence, from retina through middle stages of visual cortex.

Functional Connectivity Abnormalities of Brain Regions With Structural Deficits in Primary Insomnia Patients.

ObjectivesThe present study examined the abnormal resting state functional connections (RSFCs) in structural deficit brain regions of primary insomnia (PI) patients.MethodsThirty-three PI patients and 38 well-matched healthy controls participated in our study. We used voxel-based morphometry and RSFC to study functional connectivity abnormalities of brain regions with structural deficits in PI patients.ResultsPI patients showed decreased gray matter (GM) volume in the left dorsolateral prefrontal cortex, left orbitofrontal cortex (OFC), bilateral middle frontal gyrus (MFC), right inferior frontal gyrus (IFG), and left inferior temporal gyrus. Gray matter volume in the right MFC negatively correlated with Self-Rating Scale of Sleep (SRSS) scores, and GM volume in the right IFG negatively correlated with SRSS and Insomnia Severity Index (ISI) scores. Therefore, the right MFC and right IFG were selected as regions of interest for RSFC analysis. PI patients had weakened RSFC between the right inferior parietal gyrus (IPC) and the right MFC compared to the healthy controls and between the left OFC and right IFG. The RSFC between the right MFC and right IPC negatively correlated with SRSS scores. The RSFC between the right IFG and left OFC negatively correlated with SRSS, ISI, SAS, and SDS scores.ConclusionsThe present study found structural changes in the right MFC and right IFG accompanied by RSFC changes. This finding may provide novel insights into the neural mechanisms of PI via combining structural and functional modality information.

The Effects of Traumatic Experience on the Behavior, c‐Fos Expression and Functional Connections in the Mouse Brain Resting State Networks

It is known that the brains of animals and humans is active at resting state. In this paper we investigate how past experience affects characteristics of such resting state networks in animals. To do this we subjected mice to single traumatic experience that induced posttraumatic stress disorder (PTSD) and then analyzed activity of their brain (including cortex; hippocampus; amygdala; basal ganglia; thalamus; hypothalamus and midbrain) by c‐Fos cellular mapping during traumatic memory retrieval and at rest in comparison with non‐stressed animals.PTSD development led to global changes in brain activity: number of c‐Fos‐active neurons was significantly increased in different areas during traumatic memory retrieval. Similarly, at rest PTSD animals showed increased activity in 11 brain regions participating in fear memory.We identified resting state functional connections in PTSD and controls and compared them with model networks: random, scale‐free and small‐world. In both groups of mice clusterization exceeded random level. At the same time, these clusters did not interact well with each other: global efficiency of experimental networks was at the level of random network. Resting state networks of PTSD and control mice differed (Fig. 1): PTSD network was less clustered and longer paths linked the clusters. Induction of PTSD led to global changes in the structure of resting state networks. In naive animals, cortical regions had the most connections, whereas in PTSD thalamus, striatum and amygdala had. PTSD destroyed virtually all functional connections present in naive mice; only fully connected cluster of auditory and visual cortices remained. In addition, if in naive animals the main hubs were cingulate and retrosplenial cortices in PTSD animals paraventricular thalamic nucleus became the hub. In contrast, amygdala functional connectivity was virtually zero in naive animals, whereas in PTSD significant number of connections between amygdala, associative cortices, and striatum were observed.In addition, we have shown that PTSD induction changes spontaneous behavior, causing elevated anxiety and decreased research activity in safe conditions of home cages. Behavior in conditioned fear, EPM and sensitization tests also changed, and these changes could be disrupted by protein synthesis inhibition during traumatic experience, which also returned brain activity and structure of resting networks to normal in PTSD animals.Our findings show that stressful experiences can alter spontaneous behavior, induced and spontaneous brain activity and patterns of functional connections in resting state neuronal networks long after traumatic episode. We assume that these changes reflect replay of neuronal ensembles of the animal’s past subjective experience. This assumption was tested by disrupting the development of PTSD.Support or Funding InformationSupported by RSF 16‐15‐00300, 20‐15‐00187 and RFBR 19‐015‐00534, 20‐015‐00427.Resting state networks of naive mice (A, D) and PTSD animals (B, E), and their interception (C, F). A, B and C are for complete resting state networks, and D, E and À represents only connections of 11 brain areas that significantly change their activity in PTSD compared to naïve mice. Lines are Pearson’s correlations with R>0.5, p<0.05; green lines – naive resting state network; red lines – PTSD resting state network; blue lines – correlations that are present in naive and PTSD mice.Figure 1

Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach.

PurposeIn recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning.Patients and MethodsWe measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests.ResultsThe results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain–behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05).ConclusionThis study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

Emotions and brain function are altered up to one month after a single high dose of psilocybin

Psilocybin is a classic psychedelic compound that may have efficacy for the treatment of mood and substance use disorders. Acute psilocybin effects include reduced negative mood, increased positive mood, and reduced amygdala response to negative affective stimuli. However, no study has investigated the long-term, enduring impact of psilocybin on negative affect and associated brain function. Twelve healthy volunteers (7F/5M) completed an open-label pilot study including assessments 1-day before, 1-week after, and 1-month after receiving a 25 mg/70 kg dose of psilocybin to test the hypothesis that psilocybin administration leads to enduring changes in affect and neural correlates of affect. One-week post-psilocybin, negative affect and amygdala response to facial affect stimuli were reduced, whereas positive affect and dorsal lateral prefrontal and medial orbitofrontal cortex responses to emotionally-conflicting stimuli were increased. One-month post-psilocybin, negative affective and amygdala response to facial affect stimuli returned to baseline levels while positive affect remained elevated, and trait anxiety was reduced. Finally, the number of significant resting-state functional connections across the brain increased from baseline to 1-week and 1-month post-psilocybin. These preliminary findings suggest that psilocybin may increase emotional and brain plasticity, and the reported findings support the hypothesis that negative affect may be a therapeutic target for psilocybin.

Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations.

Estimation of individuals’ cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals’ CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of 10 CBD variables (e.g., age, gender, grip strength, and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables, as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations exhibited strong correlations with the CBD variable estimated, that is, PLSR algorithm automatically selected the RSFCs closely related to one CBD variable to establish predictive models for the variable. Besides, the estimation accuracies based on RSFCs among 100, 200, and 300 regions of interest (ROIs) were higher than those based on RSFCs among 15, 25, and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.