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The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula‐to‐whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher‐order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior‐dorsal axis across the insula; those to the lower‐order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior–posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self‐reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting‐state functional magnetic resonance imaging (rs‐fMRI). Intrinsic brain activity was measured by amplitude of low‐frequency fluctuation (ALFF). We trained and tested a two‐level k‐nearest neighbors (k‐NN) model based on resting‐state variability of ALFF with fivefold cross‐validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity. We proposed a 2‐levels (voxel‐wise and cluster level) k‐nearest neighbors framework on neurobiomarkers in bipolar depression for early identifying suicide attempters and predicting their suicide risk. The sample size of our study and the validation of an independent sample demonstrate the generalizable classification performance. The most discriminative regions contributing to models were correlated with suicide risk by clinical measurement, further suggesting the neurobiological and clinical underpinnings of our framework.

Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, studied using high‐resolution resting‐state (RS) 7 T MRI. Forty VSS patients and 60 healthy controls underwent RS MRI. Functional connectivity matrices were calculated, and global efficiency (network integration), modularity (network segregation), local efficiency (LE, connectedness neighbors) and eigenvector centrality (significance node in network) were derived using a dynamic approach (temporal fluctuations during acquisition). Network measures were compared between groups, with regions of significant difference correlated with known aberrant ocular motor VSS metrics (shortened latencies and higher number of inhibitory errors) in VSS patients. Lastly, nodal co‐modularity, a binary measure of node pairs belonging to the same module, was studied. VSS patients had lower modularity, supramarginal centrality and LE dynamics of multiple (sub)cortical regions, centered around occipital and parietal lobules. In VSS patients, lateral occipital cortex LE dynamics correlated positively with shortened prosaccade latencies (p = .041, r = .353). In VSS patients, occipital, parietal, and motor nodes belonged more often to the same module and demonstrated lower nodal co‐modularity with temporal and frontal regions. This study revealed reduced dynamic variation in modularity and local efficiency strength in the VSS brain, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Further investigation of these changes could inform our understanding of the pathogenesis of the disorder and potentially lead to treatment strategies. Visual snow syndrome (VSS) is a neurological disorder characterized by a range of continuous visual disturbances. Little is known about the functional pathological mechanisms underlying VSS and their effect on brain network topology, which was studied using high‐resolution resting‐state 7 T MRI. VSS patients demonstrated reduced dynamic variation in modularity and local efficiency strength, suggesting that brain network dynamics are less variable in terms of segregation and local clustering. Network dynamic alterations were centered around occipital cortices and related to ocular motor processing changes.

BOLD delay is an emerging, noninvasive method for assessing cerebral perfusion that does not require the use of intravenous contrast agents and is thus particularly suited for longitudinal monitoring. In this study, we assess the reproducibility of BOLD delay using data from 136 subjects with normal cerebral perfusion scanned on two separate occasions with scanners, sequence parameters, and intervals between scans varying between subjects. The effects of various factors on the reproducibility of BOLD delay, defined here as the differences in BOLD delay values between the scanning sessions, were investigated using a linear mixed model. Reproducibility was additionally assessed using the intraclass correlation coefficient of BOLD delay between sessions. Reproducibility was highest in the posterior cerebral artery territory. The mean BOLD delay test–retest difference after accounting for the aforementioned factors was 1.2 s (95% CI = 1.0 to 1.4 s). Overall, BOLD delay shows good reproducibility, but care should be taken when interpreting longitudinal BOLD delay changes that are either very small or are located in certain brain regions. This study investigates in detail the test–retest repeatability of a noninvasive perfusion imaging method (BOLD delay) in a large cohort of individuals. We find good overall test–retest repeatability, but longitudinal changes in BOLD delay in some areas of the brain should be interpreted with caution. The results of our study will be interesting to researchers currently using, or planning on using this method by providing them with a benchmark against which to interpret their results.

Functional changes of default mode network (DMN) have been proven to be closely associated with white matter hyperintensity (WMH) related cognitive impairment (CI). However, subsystem mechanisms of DMN underlying WMH‐related CI remain unclear. The present study recruited WMH patients (n = 206) with mild CI and normal cognition, as well as healthy controls (HC, n = 102). Static/dynamic functional connectivity (FC) of the DMN's three subsystems were calculated using resting‐state functional MRI. K‐means clustering analyses were performed to extract distinct dynamic connectivity states. Compared with the WMH‐NC group, the WMH‐MCI group displayed lower static FC within medial temporal lobe (MTL) and core subsystem, between core‐MTL subsystem, as well as between core and dorsal medial prefrontal cortex subsystem. All these static alterations were positively associated with information processing speed (IPS). Regarding dynamic FC, the WMH‐MCI group exhibited higher dynamic FC within MTL subsystem than the HC and WMH‐NC groups. Altered dynamic FC within MTL subsystem mediated the relationship between WMH and memory span (indirect effect: −0.2251, 95% confidence interval [−0.6295, −0.0267]). Additionally, dynamic FCs of DMN subsystems could be clustered into two recurring states. For dynamic FCs within MTL subsystem, WMH‐MCI subjects exhibited longer mean dwell time (MDT) and higher reoccurrence fraction (RF) in a sparsely connected state (State 2). Altered MDT and RF in State 2 were negatively associated with IPS. Taken together, these findings indicated static/dynamic FC of DMN subsystems can provide relevant information on cognitive decline from different aspects, which provides a comprehensive view of subsystem mechanisms of DMN underlying WMH‐related CI. Static/dynamic FCs from DMN subsystems in WMH‐MCI subjects were significantly changed. These alterations helped facilitate the progression of WMH‐related CI. Altered dynamic FC within MTL subsystem was shown to be a mediation framework between WMH and memory span. Static and dynamic FC in DMN subsystems can provide relevant information on WMH‐related CI from different aspects.

The explorations of brain functional connectivity network (FCN) using resting‐state functional magnetic resonance imaging can provide crucial insights into discriminative analysis of neuropsychiatric disorders, such as schizophrenia (SZ). Pearson's correlation (PC) is widely used to construct a densely connected FCN which may overlook some complex interactions of paired regions of interest (ROIs) under confounding effect of other ROIs. Although the method of sparse representation takes into account this issue, it penalizes each edge equally, which often makes the FCN look like a random network. In this paper, we establish a new framework, called convolutional neural network with sparsity‐guided multiple functional connectivity, for SZ classification. The framework consists of two components. (1) The first component constructs a sparse FCN by integrating PC and weighted sparse representation (WSR). The FCN retains the intrinsic correlation between paired ROIs, and eliminates false connection simultaneously, resulting in sparse interactions among multiple ROIs with the confounding effect regressed out. (2) In the second component, we develop a functional connectivity convolution to learn discriminative features for SZ classification from multiple FCNs by mining the joint spatial mapping of FCNs. Finally, an occlusion strategy is employed to explore the contributive regions and connections, to derive the potential biomarkers in identifying associated aberrant connectivity of SZ. The experiments on SZ identification verify the rationality and advantages of our proposed method. This framework also can be used as a diagnostic tool for other neuropsychiatric disorders. We first propose sparsity‐guided multiple functional connectivity patterns, by integrating Pearson's correlation and connectivity strength‐weighted sparse representation. Then an improved convolutional neural network module is introduced to learn the discriminative features of brain networks with different sparsity and the occlusion method is used to find potential biomarkers related to schizophrenia. The experimental results from the Center of Biomedical Research Excellence database demonstrate the promising performance of our method.

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting‐state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) and the B‐SNIP biomarker‐based (Biotype) approach. Statistical group differences and cross‐validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM‐IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM‐IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM‐IV and biology‐based categories while also emphasizing the importance of future work in this direction, including employing further data types. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches.

Aims The effects of subthalamic nucleus (STN)‐deep brain stimulation (DBS) on brain topological metrics, functional connectivity (FC), and white matter integrity were studied in levodopa‐treated Parkinson’s disease (PD) patients before and after DBS. Methods Clinical assessment, resting‐state functional MRI (rs‐fMRI), and diffusion tensor imaging (DTI) were performed pre‐ and post‐DBS in 15 PD patients, using a within‐subject design. The rs‐fMRI identified brain network topological metric and FC changes using graph‐theory‐ and seed‐based methods. White matter integrity was determined by DTI and tract‐based spatial statistics. Results Unified Parkinson's Disease Rating Scale III (UPDRS‐ III) scores were significantly improved by 35.3% (p < 0.01) after DBS in PD patients, compared with pre‐DBS patients without medication. Post‐DBS PD patients showed a significant decrease in the graph‐theory‐based degree and cost in the middle temporal gyrus and temporo‐occipital part‐Right. Changes in FC were seen in four brain regions, and a decrease in white matter integrity was seen in the left anterior corona radiata. The topological metrics changes were correlated with Beck Depression Inventory II (BDI‐II) and the FC changes with UPDRS‐III scores. Conclusion STN‐DBS modulated graph‐theoretical metrics, FC, and white matter integrity. Brain connectivity changes observed with multi‐modal imaging were also associated with postoperative clinical improvement. These findings suggest that the effects of STN‐DBS are caused by brain network alterations. This study uses multi‐modal images to explore the effectiveness of post‐deep brain stimulation (DBS) in Parkinson's disease (PD) patients. The result finds functional connectivity (FC) change in some brain areas. Moreover, Post‐DBS PD patients revealed a significant decrease in the graph‐theory‐based degree and cost in the middle temporal gyrus, temporo‐occipital part‐Right (toMTG‐R). Changes in topological metrics correlated with improved Beck Depression Inventory‐II and the FC changes with Unified Parkinson’s Disease Rating Scale‐III.

•A functional ultrasound imaging in an fMRI-like environment with acoustic noise.•Positive rCBV response in auditory cortex and negative response in motor cortex.•Greater acoustic noise reduces functional connectivity in auditory and motor networks.•Functional connectivity by rsfUS under acoustic noise is similar to that by rsfMRI. Loud acoustic noise from the scanner during functional magnetic resonance imaging (fMRI) can affect functional connectivity (FC) observed in the resting state, but the exact effect of the MRI acoustic noise on resting state FC is not well understood. Functional ultrasound (fUS) is a neuroimaging method that visualizes brain activity based on relative cerebral blood volume (rCBV), a similar neurovascular coupling response to that measured by fMRI, but without the audible acoustic noise. In this study, we investigated the effects of different acoustic noise levels (silent, 80 dB, and 110 dB) on FC by measuring resting state fUS (rsfUS) in awake mice in an environment similar to fMRI measurement. Then, we compared the results to those of resting state fMRI (rsfMRI) conducted using an 11.7 Tesla scanner. RsfUS experiments revealed a significant reduction in FC between the retrosplenial dysgranular and auditory cortexes (0.56 ± 0.07 at silence vs 0.05 ± 0.05 at 110 dB, p=.01) and a significant increase in FC anticorrelation between the infralimbic and motor cortexes (−0.21 ± 0.08 at silence vs −0.47 ± 0.04 at 110 dB, p=.017) as acoustic noise increased from silence to 80 dB and 110 dB, with increased consistency of FC patterns between rsfUS and rsfMRI being found with the louder noise conditions. Event-related auditory stimulation experiments using fUS showed strong positive rCBV changes (16.5% ± 2.9% at 110 dB) in the auditory cortex, and negative rCBV changes (−6.7% ± 0.8% at 110 dB) in the motor cortex, both being constituents of the brain network that was altered by the presence of acoustic noise in the resting state experiments. Anticorrelation between constituent brain regions of the default mode network (such as the infralimbic cortex) and those of task-positive sensorimotor networks (such as the motor cortex) is known to be an important feature of brain network antagonism, and has been studied as a biological marker of brain disfunction and disease. This study suggests that attention should be paid to the acoustic noise level when using rsfMRI to evaluate the anticorrelation between the default mode network and task-positive sensorimotor network.

Objective This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early‐stage Parkinson's disease (PD). Methods Ninety‐three early‐stage PD patients (ePD, H‐Y stages 1–2.5) were recruited, divided into 47 mild (ePD‐mild, H‐Y stages 1–1.5) and 46 moderate (ePD‐moderate, H‐Y stages 2–2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H‐Y) staging system for disease severity assessment and utilized voxel‐mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel‐based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. Results The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD‐moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD‐mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H‐Y stages, UPDRS, and UPDRS‐III scores. No significant asymmetry in gray matter was detected. Conclusions Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early‐stage PD. This graphical depicts evolving brain functional asymmetry in early‐stage Parkinson's disease (ePD). Comparing ePD mild and moderate groups with controls reveals increasing asymmetry akin to Lewy body spread from lower to higher brain regions. This insight offers a novel perspective on PD neuropathology and a potential progression biomarker.